Spectral Masking With Explicit Time-context Windowing For Neural Network-based Monaural Speech Enhancement

Abstract

We propose and analyze the use of an explicit time-context window for neural network-based spectral masking speech enhancement to leverage signal context dependencies between neighboring frames. In particular, we concentrate on soft masking and loss computed on the time-frequency representation of the reconstructed speech. We show that the application of a time-context windowing function at both input and output of the neural network model improves the soft mask estimation process by combining multiple estimates taken from different contexts. The proposed approach is only applied as post-optimization in inference mode, not requiring additional layers or special training for the neural network model. Our results show that the method consistently increases both intelligibility and signal quality of the denoised speech, as demonstrated for two classes of convolutional-based speech enhancement models. Importantly, the proposed method requires only a negligible (\(\leq1%\)) increase in the

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keyfiorio2024spectral

Related papers

• Deep Interaction Between Masking And Mapping Targets For Single-channel Speech Enhancement (2021)0.00
• Deft-an: Dense Frequency-time Attentive Network For Multichannel Speech Enhancement (2022)12.10
• Enhancement Of Coded Speech Using A Mask-based Post-filter (2020)8.82
• Htmd-net: A Hybrid Masking-denoising Approach To Time-domain Monaural Singing Voice Separation (2021)2.26
• Time-graph Frequency Representation With Singular Value Decomposition For Neural Speech Enhancement (2024)2.26
• FB-MSTCN: A Full-band Single-channel Speech Enhancement Method Based On Multi-scale Temporal Convolutional Network (2022)6.77
• Parallel Gated Neural Network With Attention Mechanism For Speech Enhancement (2022)0.00
• Decoupled Spatial And Temporal Processing For Resource Efficient Multichannel Speech Enhancement (2024)0.00