Speech Denoising

26 papers with code • 2 benchmarks • 2 datasets

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

Most implemented papers

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.

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.

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.

Speech Denoising Without Clean Training Data: A Noise2Noise Approach

madhavmk/Noise2Noise-audio_denoising_without_clean_training_data 8 Apr 2021

This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio-denoising methods by showing that it is possible to train deep speech denoising networks using only noisy speech samples.

FRA-RIR: Fast Random Approximation of the Image-source Method

yluo42/fra-rir 8 Aug 2022

The training of modern speech processing systems often requires a large amount of simulated room impulse response (RIR) data in order to allow the systems to generalize well in real-world, reverberant environments.

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.