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Found 13 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.
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.
GLD-Net: Improving Monaural Speech Enhancement by Learning Global and Local Dependency Features with GLD Block
For monaural speech enhancement, contextual information is important for accurate speech estimation.
Improving Visual Speech Enhancement Network by Learning Audio-visual Affinity with Multi-head Attention
The proposed model alleviates these drawbacks by a) applying a model that fuses audio and visual features layer by layer in encoding phase, and that feeds fused audio-visual features to each corresponding decoder layer, and more importantly, b) introducing a 2-stage multi-head cross attention (MHCA) mechanism to infer audio-visual speech enhancement for balancing the fused audio-visual features and eliminating irrelevant features.
U-Former: Improving Monaural Speech Enhancement with Multi-head Self and Cross Attention
For supervised speech enhancement, contextual information is important for accurate spectral mapping.
Improving Dual-Microphone Speech Enhancement by Learning Cross-Channel Features with Multi-Head Attention
Hand-crafted spatial features, such as inter-channel intensity difference (IID) and inter-channel phase difference (IPD), play a fundamental role in recent deep learning based dual-microphone speech enhancement (DMSE) systems.
VSEGAN: Visual Speech Enhancement Generative Adversarial Network
This paper proposes a novel frameworkthat involves visual information for speech enhancement, by in-corporating a Generative Adversarial Network (GAN).
AMFFCN: Attentional Multi-layer Feature Fusion Convolution Network for Audio-visual Speech Enhancement
Audio-visual speech enhancement system is regarded to be one of promising solutions for isolating and enhancing speech of desired speaker.
Multi-layer Feature Fusion Convolution Network for Audio-visual Speech Enhancement
Most of recent AV speech enhancement approaches separately process the acoustic and visual features and fuse them via a simple concatenation operation.
