# Single Channel Audio Source Separation using Convolutional Denoising Autoencoders

arXiv: https://arxiv.org/abs/1703.08019 · HTML: https://mlsanity.com/paper/1703.08019/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2017.

## Built on by (most cited first, 8 of 8)

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- [Improving weakly supervised sound event detection with self-supervised auxiliary tasks](https://mlsanity.com/paper/2106.06858/) (2021)
- [Deep neural network techniques for monaural speech enhancement: state of the art analysis](https://mlsanity.com/paper/2212.00369/) (2022)
- [One to Multiple Mapping Dual Learning: Learning Multiple Sources from One Mixed Signal](https://mlsanity.com/paper/2110.06568/) (2021)
- [Speech Separation based on Contrastive Learning and Deep Modularization](https://mlsanity.com/paper/2305.10652/) (2023)
- [Ground-roll Separation From Land Seismic Records Based on Convolutional Neural Network](https://mlsanity.com/paper/2409.03878/) (2024)

## Abstract (arXiv)

Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose to use deep fully convolutional denoising autoencoders (CDAEs) for monaural audio source separation. We use as many CDAEs as the number of sources to be separated from the mixed signal. Each CDAE is trained to separate one source and treats the other sources as background noise. The main idea is to allow each CDAE to learn suitable spectral-temporal filters and features to its corresponding source. Our experimental results show that CDAEs perform source separation slightly better than the deep feedforward neural networks (FNNs) even with fewer parameters than FNNs.