January 01, 2019

Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems

  • Isopoussu A.
  • Kawsar F.
  • Mathur A.
  • Nadia Berthouze
  • Nic Lane

Mobile and embedded devices are increasingly using microphones and audio- based computational models to infer user context and drive user interfaces. A major challenge in building systems that combine audio models with commodity microphones is to guarantee their accuracy and robustness in the real-world. Besides many environmental dynamics, a primary factor that impacts the robustness of audio models is microphone variability. In this work, we propose Mic2Mic - a machine-learned system component - which resides in the inference pipeline of audio models and at real-time reduces the variability in audio data caused by microphone-specific factors. Two key considerations for the design of Mic2Mic were: a) to decouple the problem of microphone variability from the audio task, and b) put minimal burden on end-users to provide training data. With these in mind, we apply the principles of cycle-consistent generative adversarial networks (CycleGANs) to learn Mic2Mic using just unlabeled and unpaired data collected from different microphones. Our experimental evaluation shows that Mic2Mic can recover between 67% to 89% of the accuracy lost due to microphone variability for two common audio tasks on embedded devices.

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