US based leading semiconductor manufacturer offering microcontrollers and processors for sensors, analog ICs and connectivity.
- The client was looking for a technology partner to help them in proving the application of newly developed chipsets for machine learning at edge to be launched in the market
- Designed the voice-based gender detection application using supervised machine learning algorithm – Depth-wise separable convolution neural network
- DS-CNN is based on MobileNet architecture that is best suited for memory constrained devices
- Extracted audio features using Mel Frequency Cepstral Coefficients technique
- Developed model using TensorFlow framework and trained model with extracted features from 9000 audio files
- Developed application on NXP i.MXRT600, performed data pre-processing and audio feature extraction (MFCC) of the real-time audio samples on Hi-Fi 4 DSP and the inference of the Deep Learning model was deployed on ARM Cortex-M33
- Performance evaluation using real human voice input through microphone
- Accelerated client’s product launch timeline by 20% with years of expertise in machine learning domain
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