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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2477-2481
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

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Keywords

  • Gibbs sampling
  • Machine learning
  • Markov Random Field
  • real-time streaming hardware
  • source separation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Ko, G. G., & Rutenbar, R. A. (2017). A case study of machine learning hardware: Real-time source separation using Markov Random Fields via sampling-based inference. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 2477-2481). [7952602] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7952602