diff --git a/libraries/TFLiteMicro/examples/micro_speech/README.md b/libraries/TFLiteMicro/examples/micro_speech/README.md
deleted file mode 100644
index ed293bf2e06..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/README.md
+++ /dev/null
@@ -1,22 +0,0 @@
-# Micro Speech Example
-
-This example shows how to run a 20 kB model that can recognize 2 keywords,
-"yes" and "no", from speech data.
-
-The application listens to its surroundings with a microphone and indicates
-when it has detected a word by displaying data on a screen.
-
-## Deploy to ESP32
-
-The sample has been tested on ESP-IDF version `release/v4.2` and `release/v4.4` with the following devices:
-- [ESP32-DevKitC](http://esp-idf.readthedocs.io/en/latest/get-started/get-started-devkitc.html)
-- [ESP32-S3-DevKitC](https://docs.espressif.com/projects/esp-idf/en/latest/esp32s3/hw-reference/esp32s3/user-guide-devkitc-1.html)
-- [ESP-EYE](https://github.com/espressif/esp-who/blob/master/docs/en/get-started/ESP-EYE_Getting_Started_Guide.md)
-
-### Sample output
-
-  * When a keyword is detected you will see following output sample output on the log screen:
-
-```
-Heard yes (<score>) at <time>
-```
diff --git a/libraries/TFLiteMicro/examples/micro_speech/audio_provider.cpp b/libraries/TFLiteMicro/examples/micro_speech/audio_provider.cpp
deleted file mode 100644
index 8de6a306332..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/audio_provider.cpp
+++ /dev/null
@@ -1,184 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "audio_provider.h"
-
-#include <cstdlib>
-#include <cstring>
-
-// FreeRTOS.h must be included before some of the following dependencies.
-// Solves b/150260343.
-// clang-format off
-#include "freertos/FreeRTOS.h"
-// clang-format on
-
-#include "driver/i2s.h"
-#include "esp_log.h"
-#include "esp_spi_flash.h"
-#include "esp_system.h"
-#include "esp_timer.h"
-#include "freertos/task.h"
-#include "ringbuf.h"
-#include "micro_model_settings.h"
-
-using namespace std;
-
-#define NO_I2S_SUPPORT CONFIG_IDF_TARGET_ESP32C2 || (CONFIG_IDF_TARGET_ESP32C3 && (ESP_IDF_VERSION < ESP_IDF_VERSION_VAL(4, 4, 0)))
-
-static const char *TAG = "TF_LITE_AUDIO_PROVIDER";
-/* ringbuffer to hold the incoming audio data */
-ringbuf_t *g_audio_capture_buffer;
-volatile int32_t g_latest_audio_timestamp = 0;
-/* model requires 20ms new data from g_audio_capture_buffer and 10ms old data
- * each time , storing old data in the histrory buffer , {
- * history_samples_to_keep = 10 * 16 } */
-constexpr int32_t history_samples_to_keep = ((kFeatureSliceDurationMs - kFeatureSliceStrideMs) * (kAudioSampleFrequency / 1000));
-/* new samples to get each time from ringbuffer, { new_samples_to_get =  20 * 16
- * } */
-constexpr int32_t new_samples_to_get = (kFeatureSliceStrideMs * (kAudioSampleFrequency / 1000));
-
-namespace {
-int16_t g_audio_output_buffer[kMaxAudioSampleSize];
-bool g_is_audio_initialized = false;
-int16_t g_history_buffer[history_samples_to_keep];
-}  // namespace
-
-const int32_t kAudioCaptureBufferSize = 80000;
-const int32_t i2s_bytes_to_read = 3200;
-
-#if NO_I2S_SUPPORT
-// nothing to be done here
-#else
-static void i2s_init(void) {
-  // Start listening for audio: MONO @ 16KHz
-  i2s_config_t i2s_config = {
-    .mode = (i2s_mode_t)(I2S_MODE_MASTER | I2S_MODE_RX | I2S_MODE_TX),
-    .sample_rate = 16000,
-    .bits_per_sample = (i2s_bits_per_sample_t)16,
-    .channel_format = I2S_CHANNEL_FMT_ONLY_LEFT,
-    .communication_format = I2S_COMM_FORMAT_I2S,
-    .intr_alloc_flags = 0,
-    .dma_buf_count = 3,
-    .dma_buf_len = 300,
-    .use_apll = false,
-    .tx_desc_auto_clear = false,
-    .fixed_mclk = -1,
-  };
-  i2s_pin_config_t pin_config = {
-    .bck_io_num = 26,    // IIS_SCLK
-    .ws_io_num = 32,     // IIS_LCLK
-    .data_out_num = -1,  // IIS_DSIN
-    .data_in_num = 33,   // IIS_DOUT
-  };
-  esp_err_t ret = 0;
-  ret = i2s_driver_install((i2s_port_t)1, &i2s_config, 0, NULL);
-  if (ret != ESP_OK) {
-    ESP_LOGE(TAG, "Error in i2s_driver_install");
-  }
-  ret = i2s_set_pin((i2s_port_t)1, &pin_config);
-  if (ret != ESP_OK) {
-    ESP_LOGE(TAG, "Error in i2s_set_pin");
-  }
-
-  ret = i2s_zero_dma_buffer((i2s_port_t)1);
-  if (ret != ESP_OK) {
-    ESP_LOGE(TAG, "Error in initializing dma buffer with 0");
-  }
-}
-#endif
-
-static void CaptureSamples(void *arg) {
-#if NO_I2S_SUPPORT
-  ESP_LOGE(TAG, "i2s support not available on C3 chip for IDF < 4.4.0");
-  return;
-#else
-  size_t bytes_read = i2s_bytes_to_read;
-  uint8_t i2s_read_buffer[i2s_bytes_to_read] = {};
-  i2s_init();
-  while (1) {
-    /* read 100ms data at once from i2s */
-    i2s_read((i2s_port_t)1, (void *)i2s_read_buffer, i2s_bytes_to_read, &bytes_read, pdMS_TO_TICKS(100));
-    if (bytes_read <= 0) {
-      ESP_LOGE(TAG, "Error in I2S read : %d", bytes_read);
-    } else {
-      if (bytes_read < i2s_bytes_to_read) {
-        ESP_LOGW(TAG, "Partial I2S read");
-      }
-      /* write bytes read by i2s into ring buffer */
-      int bytes_written = rb_write(g_audio_capture_buffer, (uint8_t *)i2s_read_buffer, bytes_read, pdMS_TO_TICKS(100));
-      /* update the timestamp (in ms) to let the model know that new data has
-       * arrived */
-      g_latest_audio_timestamp = g_latest_audio_timestamp + ((1000 * (bytes_written / 2)) / kAudioSampleFrequency);
-      if (bytes_written <= 0) {
-        ESP_LOGE(TAG, "Could Not Write in Ring Buffer: %d ", bytes_written);
-      } else if (bytes_written < bytes_read) {
-        ESP_LOGW(TAG, "Partial Write");
-      }
-    }
-  }
-#endif
-  vTaskDelete(NULL);
-}
-
-TfLiteStatus InitAudioRecording() {
-  g_audio_capture_buffer = rb_init("tf_ringbuffer", kAudioCaptureBufferSize);
-  if (!g_audio_capture_buffer) {
-    ESP_LOGE(TAG, "Error creating ring buffer");
-    return kTfLiteError;
-  }
-  /* create CaptureSamples Task which will get the i2s_data from mic and fill it
-   * in the ring buffer */
-  xTaskCreate(CaptureSamples, "CaptureSamples", 1024 * 32, NULL, 10, NULL);
-  while (!g_latest_audio_timestamp) {
-    vTaskDelay(1);  // one tick delay to avoid watchdog
-  }
-  ESP_LOGI(TAG, "Audio Recording started");
-  return kTfLiteOk;
-}
-
-TfLiteStatus GetAudioSamples(int start_ms, int duration_ms, int *audio_samples_size, int16_t **audio_samples) {
-  if (!g_is_audio_initialized) {
-    TfLiteStatus init_status = InitAudioRecording();
-    if (init_status != kTfLiteOk) {
-      return init_status;
-    }
-    g_is_audio_initialized = true;
-  }
-  /* copy 160 samples (320 bytes) into output_buff from history */
-  memcpy((void *)(g_audio_output_buffer), (void *)(g_history_buffer), history_samples_to_keep * sizeof(int16_t));
-
-  /* copy 320 samples (640 bytes) from rb at ( int16_t*(g_audio_output_buffer) +
-   * 160 ), first 160 samples (320 bytes) will be from history */
-  int bytes_read =
-    rb_read(g_audio_capture_buffer, ((uint8_t *)(g_audio_output_buffer + history_samples_to_keep)), new_samples_to_get * sizeof(int16_t), pdMS_TO_TICKS(100));
-  if (bytes_read < 0) {
-    ESP_LOGE(TAG, " Model Could not read data from Ring Buffer");
-  } else if (bytes_read < new_samples_to_get * sizeof(int16_t)) {
-    ESP_LOGD(TAG, "RB FILLED RIGHT NOW IS %d", rb_filled(g_audio_capture_buffer));
-    ESP_LOGD(TAG, " Partial Read of Data by Model ");
-    ESP_LOGV(TAG, " Could only read %d bytes when required %d bytes ", bytes_read, (int)(new_samples_to_get * sizeof(int16_t)));
-  }
-
-  /* copy 320 bytes from output_buff into history */
-  memcpy((void *)(g_history_buffer), (void *)(g_audio_output_buffer + new_samples_to_get), history_samples_to_keep * sizeof(int16_t));
-
-  *audio_samples_size = kMaxAudioSampleSize;
-  *audio_samples = g_audio_output_buffer;
-  return kTfLiteOk;
-}
-
-int32_t LatestAudioTimestamp() {
-  return g_latest_audio_timestamp;
-}
diff --git a/libraries/TFLiteMicro/examples/micro_speech/audio_provider.h b/libraries/TFLiteMicro/examples/micro_speech/audio_provider.h
deleted file mode 100644
index 3b65b4d6087..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/audio_provider.h
+++ /dev/null
@@ -1,43 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
-
-#include "tensorflow/lite/c/common.h"
-
-// This is an abstraction around an audio source like a microphone, and is
-// expected to return 16-bit PCM sample data for a given point in time. The
-// sample data itself should be used as quickly as possible by the caller, since
-// to allow memory optimizations there are no guarantees that the samples won't
-// be overwritten by new data in the future. In practice, implementations should
-// ensure that there's a reasonable time allowed for clients to access the data
-// before any reuse.
-// The reference implementation can have no platform-specific dependencies, so
-// it just returns an array filled with zeros. For real applications, you should
-// ensure there's a specialized implementation that accesses hardware APIs.
-TfLiteStatus GetAudioSamples(int start_ms, int duration_ms, int *audio_samples_size, int16_t **audio_samples);
-
-// Returns the time that audio data was last captured in milliseconds. There's
-// no contract about what time zero represents, the accuracy, or the granularity
-// of the result. Subsequent calls will generally not return a lower value, but
-// even that's not guaranteed if there's an overflow wraparound.
-// The reference implementation of this function just returns a constantly
-// incrementing value for each call, since it would need a non-portable platform
-// call to access time information. For real applications, you'll need to write
-// your own platform-specific implementation.
-int32_t LatestAudioTimestamp();
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
diff --git a/libraries/TFLiteMicro/examples/micro_speech/command_responder.cpp b/libraries/TFLiteMicro/examples/micro_speech/command_responder.cpp
deleted file mode 100644
index d397f568e1e..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/command_responder.cpp
+++ /dev/null
@@ -1,26 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "command_responder.h"
-#include "tensorflow/lite/micro/micro_log.h"
-
-// The default implementation writes out the name of the recognized command
-// to the error console. Real applications will want to take some custom
-// action instead, and should implement their own versions of this function.
-void RespondToCommand(int32_t current_time, const char *found_command, uint8_t score, bool is_new_command) {
-  if (is_new_command) {
-    MicroPrintf("Heard %s (%d) @%dms", found_command, score, current_time);
-  }
-}
diff --git a/libraries/TFLiteMicro/examples/micro_speech/command_responder.h b/libraries/TFLiteMicro/examples/micro_speech/command_responder.h
deleted file mode 100644
index 4368f235615..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/command_responder.h
+++ /dev/null
@@ -1,29 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Provides an interface to take an action based on an audio command.
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
-
-#include "tensorflow/lite/c/common.h"
-
-// Called every time the results of an audio recognition run are available. The
-// human-readable name of any recognized command is in the `found_command`
-// argument, `score` has the numerical confidence, and `is_new_command` is set
-// if the previous command was different to this one.
-void RespondToCommand(int32_t current_time, const char *found_command, uint8_t score, bool is_new_command);
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
diff --git a/libraries/TFLiteMicro/examples/micro_speech/feature_provider.cpp b/libraries/TFLiteMicro/examples/micro_speech/feature_provider.cpp
deleted file mode 100644
index 6b445039b54..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/feature_provider.cpp
+++ /dev/null
@@ -1,105 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "feature_provider.h"
-
-#include "audio_provider.h"
-#include "micro_features_generator.h"
-#include "micro_model_settings.h"
-#include "tensorflow/lite/micro/micro_log.h"
-
-FeatureProvider::FeatureProvider(int feature_size, int8_t *feature_data) : feature_size_(feature_size), feature_data_(feature_data), is_first_run_(true) {
-  // Initialize the feature data to default values.
-  for (int n = 0; n < feature_size_; ++n) {
-    feature_data_[n] = 0;
-  }
-}
-
-FeatureProvider::~FeatureProvider() {}
-
-TfLiteStatus FeatureProvider::PopulateFeatureData(int32_t last_time_in_ms, int32_t time_in_ms, int *how_many_new_slices) {
-  if (feature_size_ != kFeatureElementCount) {
-    MicroPrintf("Requested feature_data_ size %d doesn't match %d", feature_size_, kFeatureElementCount);
-    return kTfLiteError;
-  }
-
-  // Quantize the time into steps as long as each window stride, so we can
-  // figure out which audio data we need to fetch.
-  const int last_step = (last_time_in_ms / kFeatureSliceStrideMs);
-  const int current_step = (time_in_ms / kFeatureSliceStrideMs);
-
-  int slices_needed = current_step - last_step;
-  // If this is the first call, make sure we don't use any cached information.
-  if (is_first_run_) {
-    TfLiteStatus init_status = InitializeMicroFeatures();
-    if (init_status != kTfLiteOk) {
-      return init_status;
-    }
-    is_first_run_ = false;
-    slices_needed = kFeatureSliceCount;
-  }
-  if (slices_needed > kFeatureSliceCount) {
-    slices_needed = kFeatureSliceCount;
-  }
-  *how_many_new_slices = slices_needed;
-
-  const int slices_to_keep = kFeatureSliceCount - slices_needed;
-  const int slices_to_drop = kFeatureSliceCount - slices_to_keep;
-  // If we can avoid recalculating some slices, just move the existing data
-  // up in the spectrogram, to perform something like this:
-  // last time = 80ms          current time = 120ms
-  // +-----------+             +-----------+
-  // | data@20ms |         --> | data@60ms |
-  // +-----------+       --    +-----------+
-  // | data@40ms |     --  --> | data@80ms |
-  // +-----------+   --  --    +-----------+
-  // | data@60ms | --  --      |  <empty>  |
-  // +-----------+   --        +-----------+
-  // | data@80ms | --          |  <empty>  |
-  // +-----------+             +-----------+
-  if (slices_to_keep > 0) {
-    for (int dest_slice = 0; dest_slice < slices_to_keep; ++dest_slice) {
-      int8_t *dest_slice_data = feature_data_ + (dest_slice * kFeatureSliceSize);
-      const int src_slice = dest_slice + slices_to_drop;
-      const int8_t *src_slice_data = feature_data_ + (src_slice * kFeatureSliceSize);
-      for (int i = 0; i < kFeatureSliceSize; ++i) {
-        dest_slice_data[i] = src_slice_data[i];
-      }
-    }
-  }
-  // Any slices that need to be filled in with feature data have their
-  // appropriate audio data pulled, and features calculated for that slice.
-  if (slices_needed > 0) {
-    for (int new_slice = slices_to_keep; new_slice < kFeatureSliceCount; ++new_slice) {
-      const int new_step = (current_step - kFeatureSliceCount + 1) + new_slice;
-      const int32_t slice_start_ms = (new_step * kFeatureSliceStrideMs);
-      int16_t *audio_samples = nullptr;
-      int audio_samples_size = 0;
-      // TODO(petewarden): Fix bug that leads to non-zero slice_start_ms
-      GetAudioSamples((slice_start_ms > 0 ? slice_start_ms : 0), kFeatureSliceDurationMs, &audio_samples_size, &audio_samples);
-      if (audio_samples_size < kMaxAudioSampleSize) {
-        MicroPrintf("Audio data size %d too small, want %d", audio_samples_size, kMaxAudioSampleSize);
-        return kTfLiteError;
-      }
-      int8_t *new_slice_data = feature_data_ + (new_slice * kFeatureSliceSize);
-      size_t num_samples_read;
-      TfLiteStatus generate_status = GenerateMicroFeatures(audio_samples, audio_samples_size, kFeatureSliceSize, new_slice_data, &num_samples_read);
-      if (generate_status != kTfLiteOk) {
-        return generate_status;
-      }
-    }
-  }
-  return kTfLiteOk;
-}
diff --git a/libraries/TFLiteMicro/examples/micro_speech/feature_provider.h b/libraries/TFLiteMicro/examples/micro_speech/feature_provider.h
deleted file mode 100644
index be43e381bbe..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/feature_provider.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
-
-#include "tensorflow/lite/c/common.h"
-
-// Binds itself to an area of memory intended to hold the input features for an
-// audio-recognition neural network model, and fills that data area with the
-// features representing the current audio input, for example from a microphone.
-// The audio features themselves are a two-dimensional array, made up of
-// horizontal slices representing the frequencies at one point in time, stacked
-// on top of each other to form a spectrogram showing how those frequencies
-// changed over time.
-class FeatureProvider {
-public:
-  // Create the provider, and bind it to an area of memory. This memory should
-  // remain accessible for the lifetime of the provider object, since subsequent
-  // calls will fill it with feature data. The provider does no memory
-  // management of this data.
-  FeatureProvider(int feature_size, int8_t *feature_data);
-  ~FeatureProvider();
-
-  // Fills the feature data with information from audio inputs, and returns how
-  // many feature slices were updated.
-  TfLiteStatus PopulateFeatureData(int32_t last_time_in_ms, int32_t time_in_ms, int *how_many_new_slices);
-
-private:
-  int feature_size_;
-  int8_t *feature_data_;
-  // Make sure we don't try to use cached information if this is the first call
-  // into the provider.
-  bool is_first_run_;
-};
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
diff --git a/libraries/TFLiteMicro/examples/micro_speech/micro_features_generator.cpp b/libraries/TFLiteMicro/examples/micro_speech/micro_features_generator.cpp
deleted file mode 100644
index 4f67476aae3..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/micro_features_generator.cpp
+++ /dev/null
@@ -1,110 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "micro_features_generator.h"
-
-#include <cmath>
-#include <cstring>
-
-#include "tensorflow/lite/experimental/microfrontend/lib/frontend.h"
-#include "tensorflow/lite/experimental/microfrontend/lib/frontend_util.h"
-#include "tensorflow/lite/micro/micro_log.h"
-#include "micro_model_settings.h"
-
-// Configure FFT to output 16 bit fixed point.
-#define FIXED_POINT 16
-
-namespace {
-
-FrontendState g_micro_features_state;
-bool g_is_first_time = true;
-
-}  // namespace
-
-TfLiteStatus InitializeMicroFeatures() {
-  FrontendConfig config;
-  config.window.size_ms = kFeatureSliceDurationMs;
-  config.window.step_size_ms = kFeatureSliceStrideMs;
-  config.noise_reduction.smoothing_bits = 10;
-  config.filterbank.num_channels = kFeatureSliceSize;
-  config.filterbank.lower_band_limit = 125.0;
-  config.filterbank.upper_band_limit = 7500.0;
-  config.noise_reduction.smoothing_bits = 10;
-  config.noise_reduction.even_smoothing = 0.025;
-  config.noise_reduction.odd_smoothing = 0.06;
-  config.noise_reduction.min_signal_remaining = 0.05;
-  config.pcan_gain_control.enable_pcan = 1;
-  config.pcan_gain_control.strength = 0.95;
-  config.pcan_gain_control.offset = 80.0;
-  config.pcan_gain_control.gain_bits = 21;
-  config.log_scale.enable_log = 1;
-  config.log_scale.scale_shift = 6;
-  if (!FrontendPopulateState(&config, &g_micro_features_state, kAudioSampleFrequency)) {
-    MicroPrintf("FrontendPopulateState() failed");
-    return kTfLiteError;
-  }
-  g_is_first_time = true;
-  return kTfLiteOk;
-}
-
-// This is not exposed in any header, and is only used for testing, to ensure
-// that the state is correctly set up before generating results.
-void SetMicroFeaturesNoiseEstimates(const uint32_t *estimate_presets) {
-  for (int i = 0; i < g_micro_features_state.filterbank.num_channels; ++i) {
-    g_micro_features_state.noise_reduction.estimate[i] = estimate_presets[i];
-  }
-}
-
-TfLiteStatus GenerateMicroFeatures(const int16_t *input, int input_size, int output_size, int8_t *output, size_t *num_samples_read) {
-  const int16_t *frontend_input;
-  if (g_is_first_time) {
-    frontend_input = input;
-    g_is_first_time = false;
-  } else {
-    frontend_input = input + 160;
-  }
-  FrontendOutput frontend_output = FrontendProcessSamples(&g_micro_features_state, frontend_input, input_size, num_samples_read);
-
-  for (size_t i = 0; i < frontend_output.size; ++i) {
-    // These scaling values are derived from those used in input_data.py in the
-    // training pipeline.
-    // The feature pipeline outputs 16-bit signed integers in roughly a 0 to 670
-    // range. In training, these are then arbitrarily divided by 25.6 to get
-    // float values in the rough range of 0.0 to 26.0. This scaling is performed
-    // for historical reasons, to match up with the output of other feature
-    // generators.
-    // The process is then further complicated when we quantize the model. This
-    // means we have to scale the 0.0 to 26.0 real values to the -128 to 127
-    // signed integer numbers.
-    // All this means that to get matching values from our integer feature
-    // output into the tensor input, we have to perform:
-    // input = (((feature / 25.6) / 26.0) * 256) - 128
-    // To simplify this and perform it in 32-bit integer math, we rearrange to:
-    // input = (feature * 256) / (25.6 * 26.0) - 128
-    constexpr int32_t value_scale = 256;
-    constexpr int32_t value_div = static_cast<int32_t>((25.6f * 26.0f) + 0.5f);
-    int32_t value = ((frontend_output.values[i] * value_scale) + (value_div / 2)) / value_div;
-    value -= 128;
-    if (value < -128) {
-      value = -128;
-    }
-    if (value > 127) {
-      value = 127;
-    }
-    output[i] = value;
-  }
-
-  return kTfLiteOk;
-}
diff --git a/libraries/TFLiteMicro/examples/micro_speech/micro_features_generator.h b/libraries/TFLiteMicro/examples/micro_speech/micro_features_generator.h
deleted file mode 100644
index 4187d0ac9e3..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/micro_features_generator.h
+++ /dev/null
@@ -1,28 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
-
-#include "tensorflow/lite/c/common.h"
-
-// Sets up any resources needed for the feature generation pipeline.
-TfLiteStatus InitializeMicroFeatures();
-
-// Converts audio sample data into a more compact form that's appropriate for
-// feeding into a neural network.
-TfLiteStatus GenerateMicroFeatures(const int16_t *input, int input_size, int output_size, int8_t *output, size_t *num_samples_read);
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
diff --git a/libraries/TFLiteMicro/examples/micro_speech/micro_model_settings.cpp b/libraries/TFLiteMicro/examples/micro_speech/micro_model_settings.cpp
deleted file mode 100644
index c060ac62fa1..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/micro_model_settings.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "micro_model_settings.h"
-
-const char *kCategoryLabels[kCategoryCount] = {
-  "silence",
-  "unknown",
-  "yes",
-  "no",
-};
diff --git a/libraries/TFLiteMicro/examples/micro_speech/micro_model_settings.h b/libraries/TFLiteMicro/examples/micro_speech/micro_model_settings.h
deleted file mode 100644
index 05dc92ebb53..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/micro_model_settings.h
+++ /dev/null
@@ -1,43 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
-
-// Keeping these as constant expressions allow us to allocate fixed-sized arrays
-// on the stack for our working memory.
-
-// The size of the input time series data we pass to the FFT to produce the
-// frequency information. This has to be a power of two, and since we're dealing
-// with 30ms of 16KHz inputs, which means 480 samples, this is the next value.
-constexpr int kMaxAudioSampleSize = 512;
-constexpr int kAudioSampleFrequency = 16000;
-
-// The following values are derived from values used during model training.
-// If you change the way you preprocess the input, update all these constants.
-constexpr int kFeatureSliceSize = 40;
-constexpr int kFeatureSliceCount = 49;
-constexpr int kFeatureElementCount = (kFeatureSliceSize * kFeatureSliceCount);
-constexpr int kFeatureSliceStrideMs = 20;
-constexpr int kFeatureSliceDurationMs = 30;
-
-// Variables for the model's output categories.
-constexpr int kSilenceIndex = 0;
-constexpr int kUnknownIndex = 1;
-// If you modify the output categories, you need to update the following values.
-constexpr int kCategoryCount = 4;
-extern const char *kCategoryLabels[kCategoryCount];
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
diff --git a/libraries/TFLiteMicro/examples/micro_speech/micro_speech.ino b/libraries/TFLiteMicro/examples/micro_speech/micro_speech.ino
deleted file mode 100644
index d16e9e342fd..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/micro_speech.ino
+++ /dev/null
@@ -1,158 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "audio_provider.h"
-#include "command_responder.h"
-#include "feature_provider.h"
-#include "micro_model_settings.h"
-#include "model.h"
-#include "recognize_commands.h"
-#include "tensorflow/lite/micro/micro_interpreter.h"
-#include "tensorflow/lite/micro/micro_log.h"
-#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
-#include "tensorflow/lite/micro/system_setup.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-
-// Globals, used for compatibility with Arduino-style sketches.
-namespace {
-const tflite::Model *model = nullptr;
-tflite::MicroInterpreter *interpreter = nullptr;
-TfLiteTensor *model_input = nullptr;
-FeatureProvider *feature_provider = nullptr;
-RecognizeCommands *recognizer = nullptr;
-int32_t previous_time = 0;
-
-// Create an area of memory to use for input, output, and intermediate arrays.
-// The size of this will depend on the model you're using, and may need to be
-// determined by experimentation.
-constexpr int kTensorArenaSize = 30 * 1024;
-uint8_t tensor_arena[kTensorArenaSize];
-int8_t feature_buffer[kFeatureElementCount];
-int8_t *model_input_buffer = nullptr;
-}  // namespace
-
-// The name of this function is important for Arduino compatibility.
-void setup() {
-  // Map the model into a usable data structure. This doesn't involve any
-  // copying or parsing, it's a very lightweight operation.
-  model = tflite::GetModel(g_model);
-  if (model->version() != TFLITE_SCHEMA_VERSION) {
-    MicroPrintf(
-      "Model provided is schema version %d not equal to supported "
-      "version %d.",
-      model->version(), TFLITE_SCHEMA_VERSION
-    );
-    return;
-  }
-
-  // Pull in only the operation implementations we need.
-  // This relies on a complete list of all the ops needed by this graph.
-  // An easier approach is to just use the AllOpsResolver, but this will
-  // incur some penalty in code space for op implementations that are not
-  // needed by this graph.
-  //
-  // tflite::AllOpsResolver resolver;
-  // NOLINTNEXTLINE(runtime-global-variables)
-  static tflite::MicroMutableOpResolver<4> micro_op_resolver;
-  if (micro_op_resolver.AddDepthwiseConv2D() != kTfLiteOk) {
-    return;
-  }
-  if (micro_op_resolver.AddFullyConnected() != kTfLiteOk) {
-    return;
-  }
-  if (micro_op_resolver.AddSoftmax() != kTfLiteOk) {
-    return;
-  }
-  if (micro_op_resolver.AddReshape() != kTfLiteOk) {
-    return;
-  }
-
-  // Build an interpreter to run the model with.
-  static tflite::MicroInterpreter static_interpreter(model, micro_op_resolver, tensor_arena, kTensorArenaSize);
-  interpreter = &static_interpreter;
-
-  // Allocate memory from the tensor_arena for the model's tensors.
-  TfLiteStatus allocate_status = interpreter->AllocateTensors();
-  if (allocate_status != kTfLiteOk) {
-    MicroPrintf("AllocateTensors() failed");
-    return;
-  }
-
-  // Get information about the memory area to use for the model's input.
-  model_input = interpreter->input(0);
-  if ((model_input->dims->size != 2) || (model_input->dims->data[0] != 1) || (model_input->dims->data[1] != (kFeatureSliceCount * kFeatureSliceSize))
-      || (model_input->type != kTfLiteInt8)) {
-    MicroPrintf("Bad input tensor parameters in model");
-    return;
-  }
-  model_input_buffer = model_input->data.int8;
-
-  // Prepare to access the audio spectrograms from a microphone or other source
-  // that will provide the inputs to the neural network.
-  // NOLINTNEXTLINE(runtime-global-variables)
-  static FeatureProvider static_feature_provider(kFeatureElementCount, feature_buffer);
-  feature_provider = &static_feature_provider;
-
-  static RecognizeCommands static_recognizer;
-  recognizer = &static_recognizer;
-
-  previous_time = 0;
-}
-
-// The name of this function is important for Arduino compatibility.
-void loop() {
-  // Fetch the spectrogram for the current time.
-  const int32_t current_time = LatestAudioTimestamp();
-  int how_many_new_slices = 0;
-  TfLiteStatus feature_status = feature_provider->PopulateFeatureData(previous_time, current_time, &how_many_new_slices);
-  if (feature_status != kTfLiteOk) {
-    MicroPrintf("Feature generation failed");
-    return;
-  }
-  previous_time = current_time;
-  // If no new audio samples have been received since last time, don't bother
-  // running the network model.
-  if (how_many_new_slices == 0) {
-    return;
-  }
-
-  // Copy feature buffer to input tensor
-  for (int i = 0; i < kFeatureElementCount; i++) {
-    model_input_buffer[i] = feature_buffer[i];
-  }
-
-  // Run the model on the spectrogram input and make sure it succeeds.
-  TfLiteStatus invoke_status = interpreter->Invoke();
-  if (invoke_status != kTfLiteOk) {
-    MicroPrintf("Invoke failed");
-    return;
-  }
-
-  // Obtain a pointer to the output tensor
-  TfLiteTensor *output = interpreter->output(0);
-  // Determine whether a command was recognized based on the output of inference
-  const char *found_command = nullptr;
-  uint8_t score = 0;
-  bool is_new_command = false;
-  TfLiteStatus process_status = recognizer->ProcessLatestResults(output, current_time, &found_command, &score, &is_new_command);
-  if (process_status != kTfLiteOk) {
-    MicroPrintf("RecognizeCommands::ProcessLatestResults() failed");
-    return;
-  }
-  // Do something based on the recognized command. The default implementation
-  // just prints to the error console, but you should replace this with your
-  // own function for a real application.
-  RespondToCommand(current_time, found_command, score, is_new_command);
-}
diff --git a/libraries/TFLiteMicro/examples/micro_speech/model.cpp b/libraries/TFLiteMicro/examples/micro_speech/model.cpp
deleted file mode 100644
index f570dc64e52..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/model.cpp
+++ /dev/null
@@ -1,757 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This is a standard TensorFlow Lite FlatBuffer model file that has been
-// converted into a C data array, so it can be easily compiled into a binary
-// for devices that don't have a file system. It was created using the command:
-// xxd -i model.tflite > model.cc
-
-#include "model.h"
-
-// We need to keep the data array aligned on some architectures.
-#ifdef __has_attribute
-#define HAVE_ATTRIBUTE(x) __has_attribute(x)
-#else
-#define HAVE_ATTRIBUTE(x) 0
-#endif
-#if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__))
-#define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4)))
-#else
-#define DATA_ALIGN_ATTRIBUTE
-#endif
-
-const unsigned char g_model[] DATA_ALIGN_ATTRIBUTE = {
-  0x20, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0x00, 0x1c, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00,
-  0x14, 0x00, 0x00, 0x00, 0x18, 0x00, 0x12, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x94, 0x48, 0x00, 0x00, 0x34, 0x42, 0x00, 0x00, 0x1c, 0x42, 0x00, 0x00,
-  0x3c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00,
-  0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0x6d, 0x69, 0x6e, 0x5f, 0x72, 0x75, 0x6e, 0x74, 0x69, 0x6d, 0x65, 0x5f,
-  0x76, 0x65, 0x72, 0x73, 0x69, 0x6f, 0x6e, 0x00, 0x0c, 0x00, 0x00, 0x00, 0xd4, 0x41, 0x00, 0x00, 0xb4, 0x41, 0x00, 0x00, 0x24, 0x03, 0x00, 0x00, 0xf4, 0x02,
-  0x00, 0x00, 0xec, 0x02, 0x00, 0x00, 0xe4, 0x02, 0x00, 0x00, 0xc4, 0x02, 0x00, 0x00, 0xbc, 0x02, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00,
-  0x1c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x16, 0xbd, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x31, 0x2e, 0x35, 0x2e, 0x30, 0x00,
-  0x00, 0x00, 0x94, 0xba, 0xff, 0xff, 0x98, 0xba, 0xff, 0xff, 0x32, 0xbd, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x80, 0x02, 0x00, 0x00, 0xfa, 0xee, 0x28, 0xc4,
-  0xee, 0xfe, 0xcf, 0x0f, 0x1e, 0xf7, 0x1f, 0x06, 0x0d, 0xed, 0xe9, 0x83, 0x5c, 0xc9, 0x18, 0xe3, 0xf9, 0x14, 0x28, 0x2a, 0x09, 0xf2, 0x18, 0x34, 0x62, 0xea,
-  0xef, 0xd6, 0x36, 0xb7, 0x1e, 0xf7, 0x3b, 0x22, 0x28, 0x39, 0xc2, 0x9d, 0xf1, 0x07, 0x5e, 0x0b, 0x1e, 0x2c, 0x07, 0xdd, 0xfd, 0xc3, 0xd8, 0x4a, 0xf3, 0x28,
-  0xa7, 0x16, 0xd5, 0xf1, 0xc3, 0x05, 0xfd, 0x27, 0xcc, 0xba, 0x1e, 0xcb, 0xd7, 0x3d, 0xd4, 0x29, 0x00, 0xfd, 0x28, 0x44, 0xfb, 0xf2, 0xf3, 0xb6, 0x4f, 0xcf,
-  0x09, 0xf0, 0xfa, 0x45, 0x41, 0x49, 0x05, 0xc5, 0x17, 0x5d, 0x64, 0x00, 0xf8, 0xee, 0x48, 0x17, 0xf4, 0xe9, 0x2e, 0x4b, 0x2e, 0x3f, 0xdf, 0xee, 0xe4, 0x08,
-  0x38, 0xf1, 0x16, 0x13, 0x2f, 0x2a, 0xed, 0xc2, 0xbf, 0x36, 0xf4, 0x02, 0xcf, 0xaa, 0xd2, 0xfa, 0xac, 0x13, 0xf6, 0xe8, 0xb5, 0x68, 0x12, 0xb6, 0xce, 0x0e,
-  0xdf, 0x58, 0xe4, 0x49, 0x14, 0x15, 0x03, 0xed, 0xfa, 0xd4, 0x40, 0xa7, 0xf6, 0xca, 0xfb, 0x00, 0x4d, 0x5e, 0xe4, 0x55, 0x1d, 0x30, 0x45, 0xe2, 0xfc, 0x01,
-  0x48, 0x81, 0xe9, 0xf1, 0x1e, 0xfc, 0x21, 0x32, 0xed, 0x4b, 0xed, 0xfa, 0x2f, 0xd2, 0xfa, 0xfb, 0x4d, 0xa7, 0xed, 0xc7, 0x92, 0xdf, 0xe6, 0xdb, 0xf8, 0x1f,
-  0xd9, 0xfa, 0x91, 0xf5, 0xe5, 0xc5, 0x8c, 0x17, 0x0f, 0xb9, 0xd2, 0xc7, 0xfe, 0x68, 0xd3, 0x51, 0x2e, 0x49, 0x1f, 0xbd, 0x01, 0xeb, 0x31, 0x17, 0xf0, 0xef,
-  0xff, 0xb8, 0x5d, 0x62, 0x02, 0x0f, 0x1f, 0x78, 0x6a, 0xb0, 0xf9, 0xfe, 0x4f, 0xcc, 0xd3, 0xff, 0x0a, 0x96, 0x1e, 0x2c, 0xed, 0xbc, 0xf4, 0x0b, 0x42, 0xc8,
-  0xf1, 0xea, 0x6e, 0x58, 0xec, 0xc4, 0x99, 0xae, 0xdc, 0xd7, 0x12, 0x87, 0xd8, 0x06, 0xa2, 0xc2, 0xe6, 0xa2, 0x81, 0x24, 0xe9, 0xac, 0xce, 0xb6, 0x15, 0x6b,
-  0xba, 0x00, 0x19, 0x58, 0x29, 0xb6, 0xfe, 0x01, 0x25, 0x96, 0xd2, 0xec, 0x0e, 0x9c, 0x60, 0x5f, 0xe9, 0xf4, 0xf5, 0x69, 0x6b, 0xb5, 0xe1, 0xf6, 0x5e, 0xb7,
-  0xb1, 0xe5, 0x11, 0x9b, 0x18, 0x10, 0xe3, 0xe1, 0xe0, 0x0d, 0x4f, 0xa5, 0xde, 0xe5, 0x6f, 0xe2, 0xfb, 0x99, 0x82, 0xa5, 0xc9, 0xb6, 0x1f, 0x46, 0xf3, 0x04,
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-  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
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-  0x9a, 0xbb, 0x84, 0x38, 0x83, 0x84, 0x73, 0x37, 0x5b, 0xa3, 0xa0, 0x38, 0x16, 0x41, 0x3a, 0x38, 0xc7, 0x9a, 0x70, 0x38, 0xed, 0x70, 0x4e, 0x38, 0x54, 0x4f,
-  0xac, 0x38, 0xfd, 0x07, 0x8d, 0x38, 0x0b, 0x00, 0x00, 0x00, 0x43, 0x6f, 0x6e, 0x76, 0x32, 0x44, 0x5f, 0x62, 0x69, 0x61, 0x73, 0x00, 0x01, 0x00, 0x00, 0x00,
-  0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x4c, 0x00, 0x00, 0x00, 0x30, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xe6, 0xff,
-  0xff, 0xff, 0x00, 0x00, 0x00, 0x19, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x06, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x16, 0x0a, 0x00,
-  0x0e, 0x00, 0x07, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0a, 0x00, 0x0c, 0x00,
-  0x07, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x03, 0x00, 0x00, 0x00
-};
-const int g_model_len = 18712;
diff --git a/libraries/TFLiteMicro/examples/micro_speech/model.h b/libraries/TFLiteMicro/examples/micro_speech/model.h
deleted file mode 100644
index deec2d64627..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/model.h
+++ /dev/null
@@ -1,27 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This is a standard TensorFlow Lite FlatBuffer model file that has been
-// converted into a C data array, so it can be easily compiled into a binary
-// for devices that don't have a file system. It was created using the command:
-// xxd -i model.tflite > model.cc
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MODEL_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MODEL_H_
-
-extern const unsigned char g_model[];
-extern const int g_model_len;
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MODEL_H_
diff --git a/libraries/TFLiteMicro/examples/micro_speech/recognize_commands.cpp b/libraries/TFLiteMicro/examples/micro_speech/recognize_commands.cpp
deleted file mode 100644
index 5d94d62fc12..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/recognize_commands.cpp
+++ /dev/null
@@ -1,121 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "recognize_commands.h"
-
-#include <limits>
-
-RecognizeCommands::RecognizeCommands(int32_t average_window_duration_ms, uint8_t detection_threshold, int32_t suppression_ms, int32_t minimum_count)
-  : average_window_duration_ms_(average_window_duration_ms), detection_threshold_(detection_threshold), suppression_ms_(suppression_ms),
-    minimum_count_(minimum_count), previous_results_() {
-  previous_top_label_ = "silence";
-  previous_top_label_time_ = std::numeric_limits<int32_t>::min();
-}
-
-TfLiteStatus RecognizeCommands::ProcessLatestResults(
-  const TfLiteTensor *latest_results, const int32_t current_time_ms, const char **found_command, uint8_t *score, bool *is_new_command
-) {
-  if ((latest_results->dims->size != 2) || (latest_results->dims->data[0] != 1) || (latest_results->dims->data[1] != kCategoryCount)) {
-    MicroPrintf(
-      "The results for recognition should contain %d elements, but there are "
-      "%d in an %d-dimensional shape",
-      kCategoryCount, latest_results->dims->data[1], latest_results->dims->size
-    );
-    return kTfLiteError;
-  }
-
-  if (latest_results->type != kTfLiteInt8) {
-    MicroPrintf("The results for recognition should be int8_t elements, but are %d", latest_results->type);
-    return kTfLiteError;
-  }
-
-  if ((!previous_results_.empty()) && (current_time_ms < previous_results_.front().time_)) {
-    MicroPrintf(
-      "Results must be fed in increasing time order, but received a "
-      "timestamp of %d that was earlier than the previous one of %d",
-      current_time_ms, previous_results_.front().time_
-    );
-    return kTfLiteError;
-  }
-
-  // Add the latest results to the head of the queue.
-  previous_results_.push_back({current_time_ms, latest_results->data.int8});
-
-  // Prune any earlier results that are too old for the averaging window.
-  const int64_t time_limit = current_time_ms - average_window_duration_ms_;
-  while ((!previous_results_.empty()) && previous_results_.front().time_ < time_limit) {
-    previous_results_.pop_front();
-  }
-
-  // If there are too few results, assume the result will be unreliable and
-  // bail.
-  const int64_t how_many_results = previous_results_.size();
-  const int64_t earliest_time = previous_results_.front().time_;
-  const int64_t samples_duration = current_time_ms - earliest_time;
-  if ((how_many_results < minimum_count_) || (samples_duration < (average_window_duration_ms_ / 4))) {
-    *found_command = previous_top_label_;
-    *score = 0;
-    *is_new_command = false;
-    return kTfLiteOk;
-  }
-
-  // Calculate the average score across all the results in the window.
-  int32_t average_scores[kCategoryCount];
-  for (int offset = 0; offset < previous_results_.size(); ++offset) {
-    PreviousResultsQueue::Result previous_result = previous_results_.from_front(offset);
-    const int8_t *scores = previous_result.scores;
-    for (int i = 0; i < kCategoryCount; ++i) {
-      if (offset == 0) {
-        average_scores[i] = scores[i] + 128;
-      } else {
-        average_scores[i] += scores[i] + 128;
-      }
-    }
-  }
-  for (int i = 0; i < kCategoryCount; ++i) {
-    average_scores[i] /= how_many_results;
-  }
-
-  // Find the current highest scoring category.
-  int current_top_index = 0;
-  int32_t current_top_score = 0;
-  for (int i = 0; i < kCategoryCount; ++i) {
-    if (average_scores[i] > current_top_score) {
-      current_top_score = average_scores[i];
-      current_top_index = i;
-    }
-  }
-  const char *current_top_label = kCategoryLabels[current_top_index];
-
-  // If we've recently had another label trigger, assume one that occurs too
-  // soon afterwards is a bad result.
-  int64_t time_since_last_top;
-  if ((previous_top_label_ == kCategoryLabels[0]) || (previous_top_label_time_ == std::numeric_limits<int32_t>::min())) {
-    time_since_last_top = std::numeric_limits<int32_t>::max();
-  } else {
-    time_since_last_top = current_time_ms - previous_top_label_time_;
-  }
-  if ((current_top_score > detection_threshold_) && ((current_top_label != previous_top_label_) || (time_since_last_top > suppression_ms_))) {
-    previous_top_label_ = current_top_label;
-    previous_top_label_time_ = current_time_ms;
-    *is_new_command = true;
-  } else {
-    *is_new_command = false;
-  }
-  *found_command = current_top_label;
-  *score = current_top_score;
-
-  return kTfLiteOk;
-}
diff --git a/libraries/TFLiteMicro/examples/micro_speech/recognize_commands.h b/libraries/TFLiteMicro/examples/micro_speech/recognize_commands.h
deleted file mode 100644
index e991e48f270..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/recognize_commands.h
+++ /dev/null
@@ -1,154 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
-
-#include <cstdint>
-
-#include "tensorflow/lite/c/common.h"
-#include "micro_model_settings.h"
-#include "tensorflow/lite/micro/micro_log.h"
-
-// Partial implementation of std::dequeue, just providing the functionality
-// that's needed to keep a record of previous neural network results over a
-// short time period, so they can be averaged together to produce a more
-// accurate overall prediction. This doesn't use any dynamic memory allocation
-// so it's a better fit for microcontroller applications, but this does mean
-// there are hard limits on the number of results it can store.
-class PreviousResultsQueue {
-public:
-  PreviousResultsQueue() : front_index_(0), size_(0) {}
-
-  // Data structure that holds an inference result, and the time when it
-  // was recorded.
-  struct Result {
-    Result() : time_(0), scores() {}
-    Result(int32_t time, int8_t *input_scores) : time_(time) {
-      for (int i = 0; i < kCategoryCount; ++i) {
-        scores[i] = input_scores[i];
-      }
-    }
-    int32_t time_;
-    int8_t scores[kCategoryCount];
-  };
-
-  int size() {
-    return size_;
-  }
-  bool empty() {
-    return size_ == 0;
-  }
-  Result &front() {
-    return results_[front_index_];
-  }
-  Result &back() {
-    int back_index = front_index_ + (size_ - 1);
-    if (back_index >= kMaxResults) {
-      back_index -= kMaxResults;
-    }
-    return results_[back_index];
-  }
-
-  void push_back(const Result &entry) {
-    if (size() >= kMaxResults) {
-      MicroPrintf("Couldn't push_back latest result, too many already!");
-      return;
-    }
-    size_ += 1;
-    back() = entry;
-  }
-
-  Result pop_front() {
-    if (size() <= 0) {
-      MicroPrintf("Couldn't pop_front result, none present!");
-      return Result();
-    }
-    Result result = front();
-    front_index_ += 1;
-    if (front_index_ >= kMaxResults) {
-      front_index_ = 0;
-    }
-    size_ -= 1;
-    return result;
-  }
-
-  // Most of the functions are duplicates of dequeue containers, but this
-  // is a helper that makes it easy to iterate through the contents of the
-  // queue.
-  Result &from_front(int offset) {
-    if ((offset < 0) || (offset >= size_)) {
-      MicroPrintf("Attempt to read beyond the end of the queue!");
-      offset = size_ - 1;
-    }
-    int index = front_index_ + offset;
-    if (index >= kMaxResults) {
-      index -= kMaxResults;
-    }
-    return results_[index];
-  }
-
-private:
-  static constexpr int kMaxResults = 50;
-  Result results_[kMaxResults];
-
-  int front_index_;
-  int size_;
-};
-
-// This class is designed to apply a very primitive decoding model on top of the
-// instantaneous results from running an audio recognition model on a single
-// window of samples. It applies smoothing over time so that noisy individual
-// label scores are averaged, increasing the confidence that apparent matches
-// are real.
-// To use it, you should create a class object with the configuration you
-// want, and then feed results from running a TensorFlow model into the
-// processing method. The timestamp for each subsequent call should be
-// increasing from the previous, since the class is designed to process a stream
-// of data over time.
-class RecognizeCommands {
-public:
-  // labels should be a list of the strings associated with each one-hot score.
-  // The window duration controls the smoothing. Longer durations will give a
-  // higher confidence that the results are correct, but may miss some commands.
-  // The detection threshold has a similar effect, with high values increasing
-  // the precision at the cost of recall. The minimum count controls how many
-  // results need to be in the averaging window before it's seen as a reliable
-  // average. This prevents erroneous results when the averaging window is
-  // initially being populated for example. The suppression argument disables
-  // further recognitions for a set time after one has been triggered, which can
-  // help reduce spurious recognitions.
-  explicit RecognizeCommands(
-    int32_t average_window_duration_ms = 1000, uint8_t detection_threshold = 200, int32_t suppression_ms = 1500, int32_t minimum_count = 3
-  );
-
-  // Call this with the results of running a model on sample data.
-  TfLiteStatus
-    ProcessLatestResults(const TfLiteTensor *latest_results, const int32_t current_time_ms, const char **found_command, uint8_t *score, bool *is_new_command);
-
-private:
-  // Configuration
-  int32_t average_window_duration_ms_;
-  uint8_t detection_threshold_;
-  int32_t suppression_ms_;
-  int32_t minimum_count_;
-
-  // Working variables
-  PreviousResultsQueue previous_results_;
-  const char *previous_top_label_;
-  int32_t previous_top_label_time_;
-};
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
diff --git a/libraries/TFLiteMicro/examples/micro_speech/ringbuf.c b/libraries/TFLiteMicro/examples/micro_speech/ringbuf.c
deleted file mode 100644
index 291f640fa82..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/ringbuf.c
+++ /dev/null
@@ -1,333 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "ringbuf.h"
-
-#include <esp_heap_caps.h>
-#include <sdkconfig.h>
-#include <stdbool.h>
-#include <stdint.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-
-#include "esp_err.h"
-#include "esp_log.h"
-#include "freertos/FreeRTOS.h"
-#include "freertos/queue.h"
-#include "freertos/semphr.h"
-#include "freertos/task.h"
-
-#define RB_TAG "RINGBUF"
-
-ringbuf_t *rb_init(const char *name, uint32_t size) {
-  ringbuf_t *r;
-  unsigned char *buf;
-
-  if (size < 2 || !name) {
-    return NULL;
-  }
-
-  r = malloc(sizeof(ringbuf_t));
-  assert(r);
-#if (CONFIG_SPIRAM_SUPPORT && (CONFIG_SPIRAM_USE_CAPS_ALLOC || CONFIG_SPIRAM_USE_MALLOC))
-  buf = heap_caps_calloc(1, size, MALLOC_CAP_SPIRAM | MALLOC_CAP_8BIT);
-#else
-  buf = calloc(1, size);
-#endif
-  assert(buf);
-
-  r->name = (char *)name;
-  r->base = r->readptr = r->writeptr = buf;
-  r->fill_cnt = 0;
-  r->size = size;
-
-  r->can_read = xSemaphoreCreateBinary();
-  assert(r->can_read);
-  r->can_write = xSemaphoreCreateBinary();
-  assert(r->can_write);
-  r->lock = xSemaphoreCreateMutex();
-  assert(r->lock);
-
-  r->abort_read = 0;
-  r->abort_write = 0;
-  r->writer_finished = 0;
-  r->reader_unblock = 0;
-
-  return r;
-}
-
-void rb_cleanup(ringbuf_t *rb) {
-  free(rb->base);
-  rb->base = NULL;
-  vSemaphoreDelete(rb->can_read);
-  rb->can_read = NULL;
-  vSemaphoreDelete(rb->can_write);
-  rb->can_write = NULL;
-  vSemaphoreDelete(rb->lock);
-  rb->lock = NULL;
-  free(rb);
-}
-
-/*
- * @brief: get the number of filled bytes in the buffer
- */
-ssize_t rb_filled(ringbuf_t *rb) {
-  return rb->fill_cnt;
-}
-
-/*
- * @brief: get the number of empty bytes available in the buffer
- */
-ssize_t rb_available(ringbuf_t *rb) {
-  ESP_LOGD(RB_TAG, "rb leftover %d bytes", rb->size - rb->fill_cnt);
-  return (rb->size - rb->fill_cnt);
-}
-
-int rb_read(ringbuf_t *rb, uint8_t *buf, int buf_len, uint32_t ticks_to_wait) {
-  int read_size;
-  int total_read_size = 0;
-
-  /**
-   * In case where we are able to read buf_len in one go,
-   * we are not able to check for abort and keep returning buf_len as bytes
-   * read. Check for argument validity check and abort case before entering
-   * memcpy loop.
-   */
-
-  if (rb == NULL || rb->abort_read == 1) {
-    return ESP_FAIL;
-  }
-
-  xSemaphoreTake(rb->lock, portMAX_DELAY);
-
-  while (buf_len) {
-    if (rb->fill_cnt < buf_len) {
-      read_size = rb->fill_cnt;
-    } else {
-      read_size = buf_len;
-    }
-    if ((rb->readptr + read_size) > (rb->base + rb->size)) {
-      int rlen1 = rb->base + rb->size - rb->readptr;
-      int rlen2 = read_size - rlen1;
-      if (buf) {
-        memcpy(buf, rb->readptr, rlen1);
-        memcpy(buf + rlen1, rb->base, rlen2);
-      }
-      rb->readptr = rb->base + rlen2;
-    } else {
-      if (buf) {
-        memcpy(buf, rb->readptr, read_size);
-      }
-      rb->readptr = rb->readptr + read_size;
-    }
-
-    buf_len -= read_size;
-    rb->fill_cnt -= read_size;
-    total_read_size += read_size;
-    if (buf) {
-      buf += read_size;
-    }
-
-    xSemaphoreGive(rb->can_write);
-
-    if (buf_len == 0) {
-      break;
-    }
-
-    xSemaphoreGive(rb->lock);
-    if (!rb->writer_finished && !rb->abort_read && !rb->reader_unblock) {
-      if (xSemaphoreTake(rb->can_read, ticks_to_wait) != pdTRUE) {
-        goto out;
-      }
-    }
-    if (rb->abort_read == 1) {
-      total_read_size = RB_ABORT;
-      goto out;
-    }
-    if (rb->writer_finished == 1) {
-      goto out;
-    }
-    if (rb->reader_unblock == 1) {
-      if (total_read_size == 0) {
-        total_read_size = RB_READER_UNBLOCK;
-      }
-      goto out;
-    }
-
-    xSemaphoreTake(rb->lock, portMAX_DELAY);
-  }
-
-  xSemaphoreGive(rb->lock);
-out:
-  if (rb->writer_finished == 1 && total_read_size == 0) {
-    total_read_size = RB_WRITER_FINISHED;
-  }
-  rb->reader_unblock = 0; /* We are anyway unblocking reader */
-  return total_read_size;
-}
-
-int rb_write(ringbuf_t *rb, const uint8_t *buf, int buf_len, uint32_t ticks_to_wait) {
-  int write_size;
-  int total_write_size = 0;
-
-  /**
-   * In case where we are able to write buf_len in one go,
-   * we are not able to check for abort and keep returning buf_len as bytes
-   * written. Check for arguments' validity and abort case before entering
-   * memcpy loop.
-   */
-
-  if (rb == NULL || buf == NULL || rb->abort_write == 1) {
-    return RB_FAIL;
-  }
-
-  xSemaphoreTake(rb->lock, portMAX_DELAY);
-
-  while (buf_len) {
-    if ((rb->size - rb->fill_cnt) < buf_len) {
-      write_size = rb->size - rb->fill_cnt;
-    } else {
-      write_size = buf_len;
-    }
-    if ((rb->writeptr + write_size) > (rb->base + rb->size)) {
-      int wlen1 = rb->base + rb->size - rb->writeptr;
-      int wlen2 = write_size - wlen1;
-      memcpy(rb->writeptr, buf, wlen1);
-      memcpy(rb->base, buf + wlen1, wlen2);
-      rb->writeptr = rb->base + wlen2;
-    } else {
-      memcpy(rb->writeptr, buf, write_size);
-      rb->writeptr = rb->writeptr + write_size;
-    }
-
-    buf_len -= write_size;
-    rb->fill_cnt += write_size;
-    total_write_size += write_size;
-    buf += write_size;
-
-    xSemaphoreGive(rb->can_read);
-
-    if (buf_len == 0) {
-      break;
-    }
-
-    xSemaphoreGive(rb->lock);
-    if (rb->writer_finished) {
-      return write_size > 0 ? write_size : RB_WRITER_FINISHED;
-    }
-    if (xSemaphoreTake(rb->can_write, ticks_to_wait) != pdTRUE) {
-      goto out;
-    }
-    if (rb->abort_write == 1) {
-      goto out;
-    }
-    xSemaphoreTake(rb->lock, portMAX_DELAY);
-  }
-
-  xSemaphoreGive(rb->lock);
-out:
-  return total_write_size;
-}
-
-/**
- * abort and set abort_read and abort_write to asked values.
- */
-static void _rb_reset(ringbuf_t *rb, int abort_read, int abort_write) {
-  if (rb == NULL) {
-    return;
-  }
-  xSemaphoreTake(rb->lock, portMAX_DELAY);
-  rb->readptr = rb->writeptr = rb->base;
-  rb->fill_cnt = 0;
-  rb->writer_finished = 0;
-  rb->reader_unblock = 0;
-  rb->abort_read = abort_read;
-  rb->abort_write = abort_write;
-  xSemaphoreGive(rb->lock);
-}
-
-void rb_reset(ringbuf_t *rb) {
-  _rb_reset(rb, 0, 0);
-}
-
-void rb_abort_read(ringbuf_t *rb) {
-  if (rb == NULL) {
-    return;
-  }
-  rb->abort_read = 1;
-  xSemaphoreGive(rb->can_read);
-  xSemaphoreGive(rb->lock);
-}
-
-void rb_abort_write(ringbuf_t *rb) {
-  if (rb == NULL) {
-    return;
-  }
-  rb->abort_write = 1;
-  xSemaphoreGive(rb->can_write);
-  xSemaphoreGive(rb->lock);
-}
-
-void rb_abort(ringbuf_t *rb) {
-  if (rb == NULL) {
-    return;
-  }
-  rb->abort_read = 1;
-  rb->abort_write = 1;
-  xSemaphoreGive(rb->can_read);
-  xSemaphoreGive(rb->can_write);
-  xSemaphoreGive(rb->lock);
-}
-
-/**
- * Reset the ringbuffer and keep rb_write aborted.
- * Note that we are taking lock before even toggling `abort_write` variable.
- * This serves a special purpose to not allow this abort to be mixed with
- * rb_write.
- */
-void rb_reset_and_abort_write(ringbuf_t *rb) {
-  _rb_reset(rb, 0, 1);
-  xSemaphoreGive(rb->can_write);
-}
-
-void rb_signal_writer_finished(ringbuf_t *rb) {
-  if (rb == NULL) {
-    return;
-  }
-  rb->writer_finished = 1;
-  xSemaphoreGive(rb->can_read);
-}
-
-int rb_is_writer_finished(ringbuf_t *rb) {
-  if (rb == NULL) {
-    return RB_FAIL;
-  }
-  return (rb->writer_finished);
-}
-
-void rb_wakeup_reader(ringbuf_t *rb) {
-  if (rb == NULL) {
-    return;
-  }
-  rb->reader_unblock = 1;
-  xSemaphoreGive(rb->can_read);
-}
-
-void rb_stat(ringbuf_t *rb) {
-  xSemaphoreTake(rb->lock, portMAX_DELAY);
-  ESP_LOGI(RB_TAG, "filled: %d, base: %p, read_ptr: %p, write_ptr: %p, size: %d\n", rb->fill_cnt, rb->base, rb->readptr, rb->writeptr, rb->size);
-  xSemaphoreGive(rb->lock);
-}
diff --git a/libraries/TFLiteMicro/examples/micro_speech/ringbuf.h b/libraries/TFLiteMicro/examples/micro_speech/ringbuf.h
deleted file mode 100644
index ff732dfa37d..00000000000
--- a/libraries/TFLiteMicro/examples/micro_speech/ringbuf.h
+++ /dev/null
@@ -1,85 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_ESP_RINGBUF_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_ESP_RINGBUF_H_
-
-#include <freertos/FreeRTOS.h>
-#include <freertos/semphr.h>
-#include <stdint.h>
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-#define RB_FAIL            ESP_FAIL
-#define RB_ABORT           -1
-#define RB_WRITER_FINISHED -2
-#define RB_READER_UNBLOCK  -3
-
-#if __has_include("esp_idf_version.h")
-#include "esp_idf_version.h"
-#else
-#define ESP_IDF_VERSION_VAL(major, minor, patch) 0
-#endif
-
-#if (ESP_IDF_VERSION >= ESP_IDF_VERSION_VAL(5, 0, 0))
-#if !(configENABLE_BACKWARD_COMPATIBILITY == 1)
-#define xSemaphoreHandle SemaphoreHandle_t
-#endif
-#endif
-
-typedef struct ringbuf {
-  char *name;
-  uint8_t *base; /**< Original pointer */
-  /* XXX: these need to be volatile? */
-  uint8_t *volatile readptr;  /**< Read pointer */
-  uint8_t *volatile writeptr; /**< Write pointer */
-  volatile ssize_t fill_cnt;  /**< Number of filled slots */
-  ssize_t size;               /**< Buffer size */
-  xSemaphoreHandle can_read;
-  xSemaphoreHandle can_write;
-  xSemaphoreHandle lock;
-  int abort_read;
-  int abort_write;
-  int writer_finished;  // to prevent infinite blocking for buffer read
-  int reader_unblock;
-} ringbuf_t;
-
-ringbuf_t *rb_init(const char *rb_name, uint32_t size);
-void rb_abort_read(ringbuf_t *rb);
-void rb_abort_write(ringbuf_t *rb);
-void rb_abort(ringbuf_t *rb);
-void rb_reset(ringbuf_t *rb);
-/**
- * @brief Special function to reset the buffer while keeping rb_write aborted.
- *        This rb needs to be reset again before being useful.
- */
-void rb_reset_and_abort_write(ringbuf_t *rb);
-void rb_stat(ringbuf_t *rb);
-ssize_t rb_filled(ringbuf_t *rb);
-ssize_t rb_available(ringbuf_t *rb);
-int rb_read(ringbuf_t *rb, uint8_t *buf, int len, uint32_t ticks_to_wait);
-int rb_write(ringbuf_t *rb, const uint8_t *buf, int len, uint32_t ticks_to_wait);
-void rb_cleanup(ringbuf_t *rb);
-void rb_signal_writer_finished(ringbuf_t *rb);
-void rb_wakeup_reader(ringbuf_t *rb);
-int rb_is_writer_finished(ringbuf_t *rb);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_ESP_RINGBUF_H_