# Lightweight Online Separation of the Sound Source of Interest through BLSTM-Based Binary Masking

arXiv: https://arxiv.org/abs/2002.11241 · HTML: https://mlsanity.com/paper/2002.11241/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [Compute and memory efficient universal sound source separation](https://mlsanity.com/paper/2103.02644/) (2021)
- [Scaling strategies for on-device low-complexity source separation with Conv-Tasnet](https://mlsanity.com/paper/2303.03005/) (2023)

## Abstract (arXiv)

Online audio source separation has been an important part of auditory scene analysis and robot audition. The main type of technique to carry this out, because of its online capabilities, has been spatial filtering (or beamforming), where it is assumed that the location (mainly, the direction of arrival; DOA) of the source of interest (SOI) is known. However, these techniques suffer from considerable interference leakage in the final result. In this paper, we propose a two step technique: 1) a phase-based beamformer that provides, in addition to the estimation of the SOI, an estimation of the cumulative environmental interference; and 2) a BLSTM-based TF binary masking stage that calculates a binary mask that aims to separate the SOI from the cumulative environmental interference. In our tests, this technique provides a signal-to-interference ratio (SIR) above 20 dB with simulated data. Because of the nature of the beamformer outputs, the label permutation problem is handled from the beginning. This makes the proposed solution a lightweight alternative that requires considerably less computational resources (almost an order of magnitude) compared to current deep-learning based techniques, while providing a comparable SIR performance.