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Found 9 papers, 4 papers with code
Noise Adaptive Speech Enhancement using Domain Adversarial Training
The proposed noise adaptive SE system contains an encoder-decoder-based enhancement model and a domain discriminator model.
Sound Audio and Speech Processing
Incorporating Symbolic Sequential Modeling for Speech Enhancement
In this study, the symbolic sequences for acoustic signals are obtained as discrete representations with a Vector Quantized Variational Autoencoder algorithm.
Learning with Learned Loss Function: Speech Enhancement with Quality-Net to Improve Perceptual Evaluation of Speech Quality
Utilizing a human-perception-related objective function to train a speech enhancement model has become a popular topic recently.
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.
Ranked #21 on
Speech Enhancement
on VoiceBank + DEMAND
Boosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing
The Transformer architecture has demonstrated a superior ability compared to recurrent neural networks in many different natural language processing applications.
Incorporating Broad Phonetic Information for Speech Enhancement
In noisy conditions, knowing speech contents facilitates listeners to more effectively suppress background noise components and to retrieve pure speech signals.
Transformers with Competitive Ensembles of Independent Mechanisms
In this work we explore a way in which the Transformer architecture is deficient: it represents each position with a large monolithic hidden representation and a single set of parameters which are applied over the entire hidden representation.
SpeechBrain: A General-Purpose Speech Toolkit
SpeechBrain is an open-source and all-in-one speech toolkit.
KaraSinger: Score-Free Singing Voice Synthesis with VQ-VAE using Mel-spectrograms
In this paper, we propose a novel neural network model called KaraSinger for a less-studied singing voice synthesis (SVS) task named score-free SVS, in which the prosody and melody are spontaneously decided by machine.
