# Multi-Decoder DPRNN: High Accuracy Source Counting and Separation

arXiv: https://arxiv.org/abs/2011.12022 · HTML: https://mlsanity.com/paper/2011.12022/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Disentangling the Impacts of Language and Channel Variability on Speech Separation Networks](https://mlsanity.com/paper/2203.16040/) (2022)
- [TACNET: Temporal Audio Source Counting Network](https://mlsanity.com/paper/2311.02369/) (2023)
- [MDS-ViTNet: Improving saliency prediction for Eye-Tracking with Vision Transformer](https://mlsanity.com/paper/2405.19501/) (2024)

## Abstract (arXiv)

We propose an end-to-end trainable approach to single-channel speech separation with unknown number of speakers. Our approach extends the MulCat source separation backbone with additional output heads: a count-head to infer the number of speakers, and decoder-heads for reconstructing the original signals. Beyond the model, we also propose a metric on how to evaluate source separation with variable number of speakers. Specifically, we cleared up the issue on how to evaluate the quality when the ground-truth hasmore or less speakers than the ones predicted by the model. We evaluate our approach on the WSJ0-mix datasets, with mixtures up to five speakers. We demonstrate that our approach outperforms state-of-the-art in counting the number of speakers and remains competitive in quality of reconstructed signals.