# Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation

arXiv: https://arxiv.org/abs/2303.00529 · HTML: https://mlsanity.com/paper/2303.00529/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering](https://mlsanity.com/paper/2110.05588/) (2021)
- [Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement](https://mlsanity.com/paper/2106.11789/) (2021)
- [DCCRN+: Channel-wise Subband DCCRN with SNR Estimation for Speech Enhancement](https://mlsanity.com/paper/2106.08672/) (2021)
- [Deep Filtering: Signal Extraction and Reconstruction Using Complex Time-Frequency Filters](https://mlsanity.com/paper/1904.08369/) (2019)
- [Speech Dereverberation with A Reverberation Time Shortening Target](https://mlsanity.com/paper/2204.08765/) (2022)
- [CLCNet: Deep learning-based Noise Reduction for Hearing Aids using Complex Linear Coding](https://mlsanity.com/paper/2001.10218/) (2020)
- [ICASSP 2021 Deep Noise Suppression Challenge: Decoupling Magnitude and Phase Optimization with a Two-Stage Deep Network](https://mlsanity.com/paper/2102.04198/) (2021)
- [Deep Multi-Frame MVDR Filtering for Single-Microphone Speech Enhancement](https://mlsanity.com/paper/2011.10345/) (2020)
- [Customizable End-to-end Optimization of Online Neural Network-supported Dereverberation for Hearing Devices](https://mlsanity.com/paper/2204.02694/) (2022)
- [Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments](https://mlsanity.com/paper/2204.02741/) (2022)

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

- [Unsupervised Blind Joint Dereverberation and Room Acoustics Estimation with Diffusion Models](https://mlsanity.com/paper/2408.07472/) (2024)
- [EDNet: A Versatile Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training](https://mlsanity.com/paper/2506.16231/) (2025)

## Abstract (arXiv)

In this paper, we present a scheme for extending deep neural network-based multiplicative maskers to deep subband filters for speech restoration in the time-frequency domain. The resulting method can be generically applied to any deep neural network providing masks in the time-frequency domain, while requiring only few more trainable parameters and a computational overhead that is negligible for state-of-the-art neural networks. We demonstrate that the resulting deep subband filtering scheme outperforms multiplicative masking for dereverberation, while leaving the denoising performance virtually the same. We argue that this is because deep subband filtering in the time-frequency domain fits the subband approximation often assumed in the dereverberation literature, whereas multiplicative masking corresponds to the narrowband approximation generally employed for denoising.