0.10.0

Bug Fixes/Improvements

• Set tflite-support<0.4.2 in setup.py. This works around dependency issue where tflite-support>=0.4.2 requires flatbuffer>=2.0 while Tensorflow requires < 2.0
• Fixed bug in AudioFeatureGenerator Python wrapper causing dynamic quantization during training to not work correctly
• Re-trained keyword_spotting_on_off_v2 using fixed AudioFeatureGenerator Python wrapper
• After training completes, do not automatically run evaluation if the model does not inherit EvaluateMixin

0.9.0

New Features/Improvements

• Added support for training on a remote SSH server: mltk ssh train
• Added tutorial for model training in the "cloud": Cloud Training Tutorial
• Updated ParallelAudioDataGenerator and ParallelImageDataGenerator to use joblib

0.8.0

New Features/Improvements

• Added Pac-Man demo: https://mltk-pacman.web.app
• Added Pac-Man tutorial
• Added BLE Audio Classifier C++ example application
• Add keyword_spotting_pacman reference model
• Added CLI option to disable GPU
• Optimized MVP-accelerated Conv2D kernels. Improved latency by 2x at expense of additional RAM
• Added Supported Hardware documentation page

Bug Fixes

• Fixed issue with reporting required tensor memory during profiling
• Fixed issue with reporting unsupported layers when profiling on device
• Fixed issue with recording audio when using activity detection block
• Fixed issue with building MVP Python wrapper on Winodws

0.7.0

General Updates

• Updated to Gecko SDK 4.1.0
• Updated to Tensorflow-Lite Micro June 8th, 2022
• Updates to support Tensorflow-2.9

New Tutorials

• Fingerprint Reader
• Image Classification

See all tutorials here

New C++ Examples

• fingerprint_authenticator
• image_classifier
• Updated audio_classifier to support new Audio Feature Generator settings

See all C++ examples here

New Reference Models

• keyword_spotting_on_off_v2
• rock_paper_scissors
• fingerprint_signature_generator

See all reference models here

Other Changes

• Added new settings Audio Feature Generator:
  • fe.activity_detection_enable - Enable the activity detection block. This indicates when activity, such as a speech command, is detected in the audio stream
  • fe.activity_detection_alpha_a - The activity detection “fast filter” coefficient. The filter is a 1-real pole IIR filter: computes out = (1-k)in + kout
  • fe.activity_detection_alpha_b - The activity detection “slow filter” coefficient. The filter is a 1-real pole IIR filter: computes out = (1-k)in + kout
  • fe.activity_detection_arm_threshold - The threshold for when there should be considered possible activity in the audio stream
  • fe.activity_detection_trip_threshold - The threshold for when activity is considered detected in the audio stream
  • fe.dc_notch_filter_enable - Enable the DC notch filter. This will help negate any DC components in the audio signal
  • fe.dc_notch_filter_coefficient - The coefficient used by the DC notch filter, DC notch filter coefficient k in Q(16,15) format, H(z) = (1 - z^-1)/(1 - k*z^-1)
  • fe.quantize_dynamic_scale_enable - Enable dynamic quantization of the generated audio spectrogram. With this, the max spectrogram value is mapped to +127, and the max spectrogram minus fe.quantize_dynamic_scale_range_db is mapped to -128. Anything below max spectrogram minus fe.quantize_dynamic_scale_range_db is mapped to -128.
  • fe.quantize_dynamic_scale_range_db - The dynamic range in dB used by the dynamic quantization
  • samplewise_norm.rescale - Value to scale each element of the sample: norm_sample = sample * . The model input dtype should be a float32
  • samplewise_norm.mean_and_std - Normalize the sample by the mean and standard deviation: norm_sample = (sample - mean(sample)) / std(sample). The model input dtype should be a float32