# Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation

arXiv: https://arxiv.org/abs/1502.04149 · HTML: https://mlsanity.com/paper/1502.04149/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2015.

## Built on by (most cited first, 20 of 20)

- [Music Source Separation with Band-split RNN](https://mlsanity.com/paper/2209.15174/) (2022)
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- [Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation](https://mlsanity.com/paper/2208.10325/) (2022)
- [Perceptual Contrast Stretching on Target Feature for Speech Enhancement](https://mlsanity.com/paper/2203.17152/) (2022)
- [RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge](https://mlsanity.com/paper/2409.08839/) (2024)
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- [Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification](https://mlsanity.com/paper/2104.02018/) (2021)
- [Music Source Restoration](https://mlsanity.com/paper/2505.21827/) (2025)
- [Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection](https://mlsanity.com/paper/2105.03542/) (2021)
- [Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals](https://mlsanity.com/paper/2209.04871/) (2022)
- [Deep neural network techniques for monaural speech enhancement: state of the art analysis](https://mlsanity.com/paper/2212.00369/) (2022)
- [Multiple Choice Learning for Efficient Speech Separation with Many Speakers](https://mlsanity.com/paper/2411.18497/) (2024)
- [Improved singing voice separation with chromagram-based pitch-aware remixing](https://mlsanity.com/paper/2203.15092/) (2022)
- [The Performance Evaluation of Attention-Based Neural ASR under Mixed Speech Input](https://mlsanity.com/paper/2108.01245/) (2021)
- [Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems](https://mlsanity.com/paper/2203.04420/) (2022)
- [An Empirical Analysis on the Vulnerabilities of End-to-End Speech Segregation Models](https://mlsanity.com/paper/2206.09556/) (2022)
- [Ground-roll Separation From Land Seismic Records Based on Convolutional Neural Network](https://mlsanity.com/paper/2409.03878/) (2024)

## Abstract (arXiv)

Monaural source separation is important for many real world applications. It is challenging because, with only a single channel of information available, without any constraints, an infinite number of solutions are possible. In this paper, we explore joint optimization of masking functions and deep recurrent neural networks for monaural source separation tasks, including monaural speech separation, monaural singing voice separation, and speech denoising. The joint optimization of the deep recurrent neural networks with an extra masking layer enforces a reconstruction constraint. Moreover, we explore a discriminative criterion for training neural networks to further enhance the separation performance. We evaluate the proposed system on the TSP, MIR-1K, and TIMIT datasets for speech separation, singing voice separation, and speech denoising tasks, respectively. Our approaches achieve 2.30--4.98 dB SDR gain compared to NMF models in the speech separation task, 2.30--2.48 dB GNSDR gain and 4.32--5.42 dB GSIR gain compared to existing models in the singing voice separation task, and outperform NMF and DNN baselines in the speech denoising task.