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• W4321608474 abstract "In this study, we propose a dense frequency-time attentive network (DeFT-AN) for multichannel speech enhancement. DeFT-AN is a mask estimation network that predicts a complex spectral masking pattern for suppressing the noise and reverberation embedded in the short-time Fourier transform (STFT) of an input signal. The proposed mask estimation network incorporates three different types of blocks for aggregating information in the spatial, spectral, and temporal dimensions. It utilizes a spectral transformer with a modified feed-forward network and a temporal conformer with sequential dilated convolutions. The use of dense blocks and transformers dedicated to the three different characteristics of audio signals enables more comprehensive enhancement in noisy and reverberant environments. The remarkable performance of DeFT-AN over state-of-the-art multichannel models is demonstrated based on two popular noisy and reverberant datasets in terms of various metrics for speech quality and intelligibility." @default.
• W4321608474 created "2023-02-24" @default.
• W4321608474 creator A5002212089 @default.
• W4321608474 creator A5003089552 @default.
• W4321608474 date "2023-01-01" @default.
• W4321608474 modified "2023-09-29" @default.
• W4321608474 title "DeFT-AN: Dense Frequency-Time Attentive Network for Multichannel Speech Enhancement" @default.
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• W4321608474 doi "https://doi.org/10.1109/lsp.2023.3244428" @default.
• W4321608474 hasPublicationYear "2023" @default.
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