AMFFCN: Attentional Multi-layer Feature Fusion Convolution Network For Audio-visual Speech Enhancement

Abstract

Audio-visual speech enhancement system is regarded to be one of promising solutions for isolating and enhancing speech of desired speaker. Conventional methods focus on predicting clean speech spectrum via a naive convolution neural network based encoder-decoder architecture, and these methods a) not adequate to use data fully and effectively, b) cannot process features selectively. The proposed model addresses 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 soft threshold attention into the model to select the informative modality softly. This paper proposes attentional audio-visual multi-layer feature fusion model, in which soft threshold attention unit are applied on feature mapping at every layer of decoder. The proposed model demonstrates the superior performance of the network against the state-of-the-a

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyxu2021amffcn

Related papers

• Multi-layer Feature Fusion Convolution Network For Audio-visual Speech Enhancement (2021)0.00
• Attentive Fusion Enhanced Audio-visual Encoding For Transformer Based Robust Speech Recognition (2020)0.00
• Audio-visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks (2017)17.39
• Dual-path Cross-modal Attention For Better Audio-visual Speech Extraction (2022)0.00
• Attention-based Audio-visual Fusion For Robust Automatic Speech Recognition (2018)16.67
• Audio-visual Speech Separation Based On Joint Feature Representation With Cross-modal Attention (2022)0.00
• Robust Wake Word Spotting With Frame-level Cross-modal Attention Based Audio-visual Conformer (2024)5.24
• MLCA-AVSR: Multi-layer Cross Attention Fusion Based Audio-visual Speech Recognition (2024)10.07