A case study of machine learning hardware: Real-time source separation using Markov Random Fields via sampling-based inference

Glenn G. Ko; Rob A. Rutenbar

ICASSP 2017accepted0 citations

A case study of machine learning hardware: Real-time source separation using Markov Random Fields via sampling-based inference

Abstract

We explore sound source separation to isolate human voice from background noise on mobile phones, e.g. talking on your cell phone in an airport. The challenges involved are real-time execution and power constraints. As a solution, we present a novel hardware-based sound source separation implementation capable of real-time streaming performance. The implementation uses a recently introduced Markov Random Field (MRF) inference formulation of foreground/background separation, and targets voice separation on mobile phones with two microphones. We demonstrate a real-time streaming FPGA implementation running at 150 MHz with total of 207 KB RAM. Our implementation achieves a speedup of 20× over a conventional software implementation, achieves an SDR of 6.655 dB with 1.601 ms latency, and exhibits excellent perceived audio quality. A virtual ASIC design shows that this architecture is quite small (less than 10M gates), consumes only 69.977 mW running at 20 MHz (52× less than an ARM Cortex-A9 software reference), and appears amenable to additional optimization for power.

BibTeX

@inproceedings{icassp2017_acasestudyofmach,
  title = {A case study of machine learning hardware: Real-time source separation using Markov Random Fields via sampling-based inference},
  author = {Glenn G. Ko and Rob A. Rutenbar},
  booktitle = {ICASSP 2017},
  year = {2017}
}