# Speaker-independent Speech Separation with Deep Attractor Network

arXiv: https://arxiv.org/abs/1707.03634 · HTML: https://mlsanity.com/paper/1707.03634/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2017.

## Built on by (most cited first, 17 of 17)

- [A Unified Model for Zero-shot Music Source Separation, Transcription and Synthesis](https://mlsanity.com/paper/2108.03456/) (2021)
- [SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing](https://mlsanity.com/paper/2211.02718/) (2022)
- [TriBERT: Full-body Human-centric Audio-visual Representation Learning for Visual Sound Separation](https://mlsanity.com/paper/2110.13412/) (2021)
- [A Neural State-Space Model Approach to Efficient Speech Separation](https://mlsanity.com/paper/2305.16932/) (2023)
- [Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction](https://mlsanity.com/paper/2310.07284/) (2023)
- [Directed Speech Separation for Automatic Speech Recognition of Long Form Conversational Speech](https://mlsanity.com/paper/2112.05863/) (2021)
- [Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-order Latent Domain](https://mlsanity.com/paper/2110.04791/) (2021)
- [Learning to Separate Voices by Spatial Regions](https://mlsanity.com/paper/2207.04203/) (2022)
- [Multi-Microphone Speaker Separation by Spatial Regions](https://mlsanity.com/paper/2303.07143/) (2023)
- [UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures](https://mlsanity.com/paper/2305.20054/) (2023)
- [A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References](https://mlsanity.com/paper/2508.14623/) (2025)
- [Deep neural network techniques for monaural speech enhancement: state of the art analysis](https://mlsanity.com/paper/2212.00369/) (2022)
- [Single-channel speech separation using Soft-minimum Permutation Invariant Training](https://mlsanity.com/paper/2111.08635/) (2021)
- [Unsupervised Speaker Diarization in Distributed IoT Networks Using Federated Learning](https://mlsanity.com/paper/2404.10842/) (2024)
- [Guided Training: A Simple Method for Single-channel Speaker Separation](https://mlsanity.com/paper/2103.14330/) (2021)
- [Manifold-Aware Deep Clustering: Maximizing Angles between Embedding Vectors Based on Regular Simplex](https://mlsanity.com/paper/2106.02331/) (2021)
- [Improving Neural Diarization through Speaker Attribute Attractors and Local Dependency Modeling](https://mlsanity.com/paper/2506.05593/) (2025)

## Abstract (arXiv)

Despite the recent success of deep learning for many speech processing tasks, single-microphone, speaker-independent speech separation remains challenging for two main reasons. The first reason is the arbitrary order of the target and masker speakers in the mixture permutation problem, and the second is the unknown number of speakers in the mixture output dimension problem. We propose a novel deep learning framework for speech separation that addresses both of these issues. We use a neural network to project the time-frequency representation of the mixture signal into a high-dimensional embedding space. A reference point attractor is created in the embedding space to represent each speaker which is defined as the centroid of the speaker in the embedding space. The time-frequency embeddings of each speaker are then forced to cluster around the corresponding attractor point which is used to determine the time-frequency assignment of the speaker. We propose three methods for finding the attractors for each source in the embedding space and compare their advantages and limitations. The objective function for the network is standard signal reconstruction error which enables end-to-end operation during both training and test phases. We evaluated our system using the Wall Street Journal dataset WSJ0 on two and three speaker mixtures and report comparable or better performance than other state-of-the-art deep learning methods for speech separation.