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Voice Based Gender Detection on Edge for Leading Semiconductor Company in US


Client Overview

US based leading semiconductor manufacturer offering microcontrollers and processors for sensors, analog ICs and connectivity.

Business Need

  • 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

VOLANSYS Contribution

  • 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

Solution Diagram

Benefits Delivered

  • Accelerated client’s product launch timeline by 20% with years of expertise in machine learning domain

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Success Stories