# Abstract

Given a multi-microphone recording of an unknown number of speakers talking concurrently, we simultaneously localize the sources and separate the individual speakers. At the core of our method is a deep network, in the waveform domain, which isolates sources within an angular region $$\theta \pm w/2$$, given an angle of interest $$\theta$$ and angular window size $$w$$. By exponentially decreasing $$w$$, we can perform a binary search to localize and separate all sources in logarithmic time. Our algorithm also allows for an arbitrary number of potentially moving speakers at test time, including more speakers than seen during training. Experiments demonstrate state of the art performance for both source separation and source localization, particularly in high levels of background noise.

# Comparisons

We show comparisons with existing methods on a challenging synthetic audio mixture. We only play one channel, but the experiment uses a virtual 6 microphone array. The perfect spectrogram mask shows the best possible performance for any method that operates on a magnitude spectrogram.

#### Input Mixture (2 Voices + BG)

[1] Y. Luo, Z. Chen, N. Mesgarani, and T. Yoshioka, “End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation,” ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020.

[2] Y. Luo and N. Mesgarani, “Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 8, pp. 1256–1266, 2019.

# Interactive Visualization

In this interactive demo, we show output signals by region from the separation network. You can listen to the output of each region by clicking the respective region on the diagram.

### Ground Truth Locations

Mixed signal

Ground Truths (played sequentially)

### Network Output

Instructions:
1. Click on a region to hear network output from that region.
2. Click on Step 2 and later to see the refinement process.
Note: The dots represent the locations of speakers.

# Source code

Training code and inference code available Here.

# Citation

@inproceedings{jenrungrot2020cone,      author = {Jenrungrot, Teerapat and Jayaram, Vivek and Seitz, Steve and Kemelmacher-Shlizerman, Ira},      title = {The Cone of Silence: Speech Separation by Localization},      booktitle = {Advances in Neural Information Processing Systems}      year = {2020} }

# Contact and Info

### UW GRAIL, UW Reality Lab, University of Washington

{tjenrung, vjayaram, seitz, kemelmi}@cs.washington.edu

# Acknowledgements

The authors thank the labmates from UW GRAIL Lab. This work was supported by the UW Reality Lab, Facebook, Google, Futurewei, and Amazon.