Forknet: Simultaneous Time And Time-frequency Domain Modeling For Speech Enhancement

Abstract

Previous research in speech enhancement has mostly focused on modeling time or time-frequency domain information alone, with little consideration given to the potential benefits of simultaneously modeling both domains. Since these domains contain complementary information, combining them may improve the performance of the model. In this letter, we propose a new approach to simultaneously model time and time-frequency domain information in a single model. We begin with the DPT-FSNet (causal version) model as a baseline and modify the encoder structure by replacing the original encoder with three separate encoders, each dedicated to modeling time-domain, real-imaginary, and magnitude information, respectively. Additionally, we introduce a feature fusion module both before and after the dual-path processing blocks to better leverage information from the different domains. The outcomes of our experiments reveal that the proposed approach achieves superior performance compared to existing s

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keydang2023forknet

Related papers

• Dpt-fsnet: Dual-path Transformer Based Full-band And Sub-band Fusion Network For Speech Enhancement (2021)0.00
• Efficient Encoder-decoder And Dual-path Conformer For Comprehensive Feature Learning In Speech Enhancement (2023)7.16
• Speech Enhancement With Perceptually-motivated Optimization And Dual Transformations (2022)0.00
• Time-graph Frequency Representation With Singular Value Decomposition For Neural Speech Enhancement (2024)2.26
• Dmf-net: A Decoupling-style Multi-band Fusion Model For Full-band Speech Enhancement (2022)7.16
• FB-MSTCN: A Full-band Single-channel Speech Enhancement Method Based On Multi-scale Temporal Convolutional Network (2022)6.77
• Time-domain Speech Enhancement Assisted By Multi-resolution Frequency Encoder And Decoder (2023)9.76
• Speech Enhancement With Overlapped-frame Information Fusion And Causal Self-attention (2025)0.00