The problem of single-channel speech enhancement has been traditionally addressed by using statistical signal processing algorithms that are designed to suppress time-frequency regions affected by noise. We study an alternative data-driven approach which uses deep neural networks (DNNs) to learn the transformation from noisy and reverberant speech to clean speech, with a focus on real-time applications which require low-latency causal processing. We examine several structures in which deep learning can be used within an enhancement system. These include end-to-end DNN regression from noisy to clean spectra, as well as less intervening approaches which estimate a suppression gain for each time-frequency bin instead of directly recovering the clean spectral features. We also propose a novel architecture in which the general structure of a conventional noise suppressor is preserved, but the sub-tasks are independently learned and carried out by separate networks. It is shown that DNN-based suppression gain estimation outperforms the regression approach in the causal processing mode and for noise types that are not seen during DNN training.<\/p>\n","protected":false},"excerpt":{"rendered":"
The problem of single-channel speech enhancement has been traditionally addressed by using statistical signal processing algorithms that are designed to suppress time-frequency regions affected by noise. We study an alternative data-driven approach which uses deep neural networks (DNNs) to learn the transformation from noisy and reverberant speech to clean speech, with a focus on real-time […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proc. 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