Speech Enhancement
218 papers with code • 12 benchmarks • 19 datasets
Speech Enhancement is a signal processing task that involves improving the quality of speech signals captured under noisy or degraded conditions. The goal of speech enhancement is to make speech signals clearer, more intelligible, and more pleasant to listen to, which can be used for various applications such as voice recognition, teleconferencing, and hearing aids.
( Image credit: A Fully Convolutional Neural Network For Speech Enhancement )
Benchmarks
These leaderboards are used to track progress in Speech Enhancement
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Libraries
Use these libraries to find Speech Enhancement models and implementationsDatasets
AVA
WHAM!
CHiME-5
VoiceBank + DEMAND
TIMIT
AVA-ActiveSpeaker
Subtasks
Most implemented papers
Proximal Policy Optimization Algorithms
labmlai/annotated_deep_learning_paper_implementations
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We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent.
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
We consider image transformation problems, where an input image is transformed into an output image.
SEGAN: Speech Enhancement Generative Adversarial Network
santi-pdp/segan
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In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them.
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
The majority of the previous methods have formulated the separation problem through the time-frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase and magnitude of the signal, the suboptimality of time-frequency representation for speech separation, and the long latency in calculating the spectrograms.
Phase-aware Speech Enhancement with Deep Complex U-Net
AppleHolic/source_separation
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ICLR 2019
Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction.
DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement
huyanxin/DeepComplexCRN
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Interspeech 2020
Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality.
A Fully Convolutional Neural Network for Speech Enhancement
zhr1201/CNN-for-single-channel-speech-enhancement
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In hearing aids, the presence of babble noise degrades hearing intelligibility of human speech greatly.
FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement
In our proposed FullSubNet, we connect a pure full-band model and a pure sub-band model sequentially and use practical joint training to integrate these two types of models' advantages.
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
Adversarial loss in a conditional generative adversarial network (GAN) is not designed to directly optimize evaluation metrics of a target task, and thus, may not always guide the generator in a GAN to generate data with improved metric scores.
SoundStream: An End-to-End Neural Audio Codec
We present SoundStream, a novel neural audio codec that can efficiently compress speech, music and general audio at bitrates normally targeted by speech-tailored codecs.
