# Speech Enhancement In Multiple-Noise Conditions using Deep Neural Networks

arXiv: https://arxiv.org/abs/1605.02427 · HTML: https://mlsanity.com/paper/1605.02427/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2016.

## Built on by (most cited first, 7 of 7)

- [VisualVoice: Audio-Visual Speech Separation with Cross-Modal Consistency](https://mlsanity.com/paper/2101.03149/) (2021)
- [Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis](https://mlsanity.com/paper/2203.17263/) (2022)
- [Deep neural network techniques for monaural speech enhancement: state of the art analysis](https://mlsanity.com/paper/2212.00369/) (2022)
- [Deep learning of nanopore sensing signals using a bi-path network](https://mlsanity.com/paper/2105.03660/) (2021)
- [NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling](https://mlsanity.com/paper/2206.09058/) (2022)
- [Integrating Uncertainty into Neural Network-based Speech Enhancement](https://mlsanity.com/paper/2305.08744/) (2023)
- [Speech Separation based on Contrastive Learning and Deep Modularization](https://mlsanity.com/paper/2305.10652/) (2023)

## Abstract (arXiv)

In this paper we consider the problem of speech enhancement in real-world like conditions where multiple noises can simultaneously corrupt speech. Most of the current literature on speech enhancement focus primarily on presence of single noise in corrupted speech which is far from real-world environments. Specifically, we deal with improving speech quality in office environment where multiple stationary as well as non-stationary noises can be simultaneously present in speech. We propose several strategies based on Deep Neural Networks (DNN) for speech enhancement in these scenarios. We also investigate a DNN training strategy based on psychoacoustic models from speech coding for enhancement of noisy speech