# On the Use of Deep Mask Estimation Module for Neural Source Separation Systems

arXiv: https://arxiv.org/abs/2206.07347 · HTML: https://mlsanity.com/paper/2206.07347/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2022.

## Built on by (most cited first, 11 of 11)

- [Music Source Separation with Band-split RNN](https://mlsanity.com/paper/2209.15174/) (2022)
- [An efficient encoder-decoder architecture with top-down attention for speech separation](https://mlsanity.com/paper/2209.15200/) (2022)
- [SafeEar: Content Privacy-Preserving Audio Deepfake Detection](https://mlsanity.com/paper/2409.09272/) (2024)
- [TIGER: Time-frequency Interleaved Gain Extraction and Reconstruction for Efficient Speech Separation](https://mlsanity.com/paper/2410.01469/) (2024)
- [Unifying Speech Enhancement and Separation with Gradient Modulation for End-to-End Noise-Robust Speech Separation](https://mlsanity.com/paper/2302.11131/) (2023)
- [Subnetwork-to-go: Elastic Neural Network with Dynamic Training and Customizable Inference](https://mlsanity.com/paper/2312.03464/) (2023)
- [A Neural State-Space Model Approach to Efficient Speech Separation](https://mlsanity.com/paper/2305.16932/) (2023)
- [SonicSim: A customizable simulation platform for speech processing in moving sound source scenarios](https://mlsanity.com/paper/2410.01481/) (2024)
- [SPMamba: State-space model is all you need in speech separation](https://mlsanity.com/paper/2404.02063/) (2024)
- [Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation](https://mlsanity.com/paper/2505.13094/) (2025)
- [Omni-directional attention mechanism based on Mamba for speech separation](https://mlsanity.com/paper/2601.16603/) (2026)

## Abstract (arXiv)

Most of the recent neural source separation systems rely on a masking-based pipeline where a set of multiplicative masks are estimated from and applied to a signal representation of the input mixture. The estimation of such masks, in almost all network architectures, is done by a single layer followed by an optional nonlinear activation function. However, recent literatures have investigated the use of a deep mask estimation module and observed performance improvement compared to a shallow mask estimation module. In this paper, we analyze the role of such deeper mask estimation module by connecting it to a recently proposed unsupervised source separation method, and empirically show that the deep mask estimation module is an efficient approximation of the so-called overseparation-grouping paradigm with the conventional shallow mask estimation layers.