# Receptive Field Analysis of Temporal Convolutional Networks for Monaural Speech Dereverberation

arXiv: https://arxiv.org/abs/2204.06439 · HTML: https://mlsanity.com/paper/2204.06439/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (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)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://mlsanity.com/paper/1608.08242/) (2016)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [TasNet: time-domain audio separation network for real-time, single-channel speech separation](https://mlsanity.com/paper/1711.00541/) (2017)
- [WHAMR!: Noisy and Reverberant Single-Channel Speech Separation](https://mlsanity.com/paper/1910.10279/) (2019)
- [Far-Field Automatic Speech Recognition](https://mlsanity.com/paper/2009.09395/) (2020)
- [On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments](https://mlsanity.com/paper/2011.05958/) (2020)
- [Deep Learning Based Dereverberation of Temporal Envelopesfor Robust Speech Recognition](https://mlsanity.com/paper/2008.03339/) (2020)

## Built on by (most cited first, 6 of 6)

- [Utterance Weighted Multi-Dilation Temporal Convolutional Networks for Monaural Speech Dereverberation](https://mlsanity.com/paper/2205.08455/) (2022)
- [Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation](https://mlsanity.com/paper/2210.15305/) (2022)
- [On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments](https://mlsanity.com/paper/2310.06125/) (2023)
- [On Data Sampling Strategies for Training Neural Network Speech Separation Models](https://mlsanity.com/paper/2304.07142/) (2023)
- [Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker Automatic Speech Recognition](https://mlsanity.com/paper/2406.08914/) (2024)
- [U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model](https://mlsanity.com/paper/2507.14237/) (2025)

## Abstract (arXiv)

Speech dereverberation is often an important requirement in robust speech processing tasks. Supervised deep learning (DL) models give state-of-the-art performance for single-channel speech dereverberation. Temporal convolutional networks (TCNs) are commonly used for sequence modelling in speech enhancement tasks. A feature of TCNs is that they have a receptive field (RF) dependent on the specific model configuration which determines the number of input frames that can be observed to produce an individual output frame. It has been shown that TCNs are capable of performing dereverberation of simulated speech data, however a thorough analysis, especially with focus on the RF is yet lacking in the literature. This paper analyses dereverberation performance depending on the model size and the RF of TCNs. Experiments using the WHAMR corpus which is extended to include room impulse responses (RIRs) with larger T60 values demonstrate that a larger RF can have significant improvement in performance when training smaller TCN models. It is also demonstrated that TCNs benefit from a wider RF when dereverberating RIRs with larger RT60 values.