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Found 8 papers, 1 papers with code
SE Territory: Monaural Speech Enhancement Meets the Fixed Virtual Perceptual Space Mapping
To overcome this limitation, we introduce a strategy to map monaural speech into a fixed simulation space for better differentiation between target speech and noise.
A comparative study of Grid and Natural sentences effects on Normal-to-Lombard conversion
Then We compare natural and grid sentences in terms of Lombard effect and Normal-to-Lombard conversion using LCT and Enhanced MAndarin Lombard Grid corpus (EMALG).
PCNN: A Lightweight Parallel Conformer Neural Network for Efficient Monaural Speech Enhancement
Convolutional neural networks (CNN) and Transformer have wildly succeeded in multimedia applications.
Exploring the Interactions between Target Positive and Negative Information for Acoustic Echo Cancellation
We observe that besides target positive information, e. g., ground-truth speech and features, the target negative information, such as interference signals and features, helps make pattern of target speech and interference signals more discriminative.
All Information is Necessary: Integrating Speech Positive and Negative Information by Contrastive Learning for Speech Enhancement
In this study, we propose a SE model that integrates both speech positive and negative information for improving SE performance by adopting contrastive learning, in which two innovations have consisted.
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement
Attention mechanisms, such as local and non-local attention, play a fundamental role in recent deep learning based speech enhancement (SE) systems.
Injecting Spatial Information for Monaural Speech Enhancement via Knowledge Distillation
To address this issue, we inject spatial information into the monaural SE model and propose a knowledge distillation strategy to enable the monaural SE model to learn binaural speech features from the binaural SE model, which makes monaural SE model possible to reconstruct higher intelligibility and quality enhanced speeches under low signal-to-noise ratio (SNR) conditions.
FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion
Voice conversion (VC) can be achieved by first extracting source content information and target speaker information, and then reconstructing waveform with these information.
