Speech Denoising

18 papers with code • 2 benchmarks • 2 datasets

Obtain the clean speech of the target speaker by suppressing the background noise.

Most implemented papers

Speech Denoising with Deep Feature Losses

anicolson/DeepXi 27 Jun 2018

We present an end-to-end deep learning approach to denoising speech signals by processing the raw waveform directly.

WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing

microsoft/unilm 26 Oct 2021

Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks.

Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation

bill9800/speech_separation 13 Feb 2015

In this paper, we explore joint optimization of masking functions and deep recurrent neural networks for monaural source separation tasks, including monaural speech separation, monaural singing voice separation, and speech denoising.

Speech Denoising Convolutional Neural Network trained with Deep Feature Losses.

francoisgermain/SpeechDenoisingWithDeepFeatureLosses Interspeech 2018

We present an end-to-end deep learning approach to denoising speech signals by processing the raw waveform directly.

Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization

mohammadiha/bnmf 15 Sep 2017

We propose a novel speech enhancement method that is based on a Bayesian formulation of NMF (BNMF).

Investigating the effect of residual and highway connections in speech enhancement models

jfsantos/irasl2018 NIPS Workshop IRASL 2018

We visualize the outputs of such connections, projected back to the spectral domain, in models trained for speech denoising, and show that while skip connections do not necessarily improve performance with regards to the number of parameters, they make speech enhancement models more interpretable.

Speech Denoising by Accumulating Per-Frequency Modeling Fluctuations

mosheman5/DNP 16 Apr 2019

The method is completely unsupervised and only trains on the specific audio clip that is being denoised.

Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation

sunwookimiub/BLSH 14 Feb 2020

Speech enhancement tasks have seen significant improvements with the advance of deep learning technology, but with the cost of increased computational complexity.

WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement

aleXiehta/WaveCRN 6 Apr 2020

In WaveCRN, the speech locality feature is captured by a convolutional neural network (CNN), while the temporal sequential property of the locality feature is modeled by stacked simple recurrent units (SRU).

Sparse Mixture of Local Experts for Efficient Speech Enhancement

IU-SAIGE/sparse_mle 16 May 2020

In this paper, we investigate a deep learning approach for speech denoising through an efficient ensemble of specialist neural networks.