On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments

Ravenscroft, William; Goetze, Stefan; Hain, Thomas

doi:10.1109/asru57964.2023.10389669

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2024

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Cited by 2 publications

References 30 publications

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Combining Conformer and Dual-Path-Transformer Networks for Single Channel Noisy Reverberant Speech Separation

Ravenscroft,

Goetze,

Hain

2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Combining Conformer and Dual-Path-Transformer Networks for Single Channel Noisy Reverberant Speech Separation

Ravenscroft,

Goetze,

Hain

2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network

Aung,

Liao,

Hung

2024

Future Internet

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Speech separation, sometimes known as the “cocktail party problem”, is the process of separating individual speech signals from an audio mixture that includes ambient noises and several speakers. The goal is to extract the target speech in this complicated sound scenario and either make it easier to understand or increase its quality so that it may be used in subsequent processing. Speech separation on overlapping audio data is important for many speech-processing tasks, including natural language processing, automatic speech recognition, and intelligent personal assistants. New speech separation algorithms are often built on a deep neural network (DNN) structure, which seeks to learn the complex relationship between the speech mixture and any specific speech source of interest. DNN-based speech separation algorithms outperform conventional statistics-based methods, although they typically need a lot of processing and/or a larger model size. This study presents a new end-to-end speech separation network called ESC-MASD-Net (effective speaker separation through convolutional multi-view attention and SuDoRM-RF network), which has relatively fewer model parameters compared with the state-of-the-art speech separation architectures. The network is partly inspired by the SuDoRM-RF++ network, which uses multiple time-resolution features with downsampling and resampling for effective speech separation. ESC-MASD-Net incorporates the multi-view attention and residual conformer modules into SuDoRM-RF++. Additionally, the U-Convolutional block in ESC-MASD-Net is refined with a conformer layer. Experiments conducted on the WHAM! dataset show that ESC-MASD-Net outperforms SuDoRM-RF++ significantly in the SI-SDRi metric. Furthermore, the use of the conformer layer has also improved the performance of ESC-MASD-Net.

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On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments

Abstract: This is a repository copy of On time domain conformer models for monaural speech separation in noisy reverberant acoustic environments.

Cited by 2 publications

References 30 publications

Combining Conformer and Dual-Path-Transformer Networks for Single Channel Noisy Reverberant Speech Separation

Combining Conformer and Dual-Path-Transformer Networks for Single Channel Noisy Reverberant Speech Separation

Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network

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