# Phase-Aware Deep Speech Enhancement: It's All About The Frame Length

arXiv: https://arxiv.org/abs/2203.16222 · HTML: https://mlsanity.com/paper/2203.16222/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [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)
- [Attention is All You Need in Speech Separation](https://mlsanity.com/paper/2010.13154/) (2020)
- [Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation](https://mlsanity.com/paper/1910.06379/) (2019)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results](https://mlsanity.com/paper/2005.13981/) (2020)
- [The Conversation: Deep Audio-Visual Speech Enhancement](https://mlsanity.com/paper/1804.04121/) (2018)
- [Filterbank design for end-to-end speech separation](https://mlsanity.com/paper/1910.10400/) (2019)
- [Demystifying TasNet: A Dissecting Approach](https://mlsanity.com/paper/1911.08895/) (2019)
- [On The Compensation Between Magnitude and Phase in Speech Separation](https://mlsanity.com/paper/2108.05470/) (2021)
- [A Multi-Phase Gammatone Filterbank for Speech Separation via TasNet](https://mlsanity.com/paper/1910.11615/) (2019)
- [Exploring the Best Loss Function for DNN-Based Low-latency Speech Enhancement with Temporal Convolutional Networks](https://mlsanity.com/paper/2005.11611/) (2020)
- [Phasebook and Friends: Leveraging Discrete Representations for Source Separation](https://mlsanity.com/paper/1810.01395/) (2018)
- [Phase reconstruction based on recurrent phase unwrapping with deep neural networks](https://mlsanity.com/paper/2002.05832/) (2020)
- [Phase reconstruction of spectrograms with linear unwrapping: application to audio signal restoration](https://mlsanity.com/paper/1605.07467/) (2016)

## Built on by (most cited first, 5 of 5)

- [Speech Enhancement and Dereverberation with Diffusion-based Generative Models](https://mlsanity.com/paper/2208.05830/) (2022)
- [A2SB: Audio-to-Audio Schrodinger Bridges](https://mlsanity.com/paper/2501.11311/) (2025)
- [Efficient Transformer-based Speech Enhancement Using Long Frames and STFT Magnitudes](https://mlsanity.com/paper/2206.11703/) (2022)
- [Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction](https://mlsanity.com/paper/2306.15942/) (2023)
- [Hybrid Real- and Complex-valued Neural Network Architecture](https://mlsanity.com/paper/2504.03497/) (2025)

## Abstract (arXiv)

Algorithmic latency in speech processing is dominated by the frame length used for Fourier analysis, which in turn limits the achievable performance of magnitude-centric approaches. As previous studies suggest the importance of phase grows with decreasing frame length, this work presents a systematical study on the contribution of phase and magnitude in modern Deep Neural Network (DNN)-based speech enhancement at different frame lengths. Results indicate that DNNs can successfully estimate phase when using short frames, with similar or better overall performance compared to using longer frames. Thus, interestingly, modern phase-aware DNNs allow for low-latency speech enhancement at high quality.