Deep Ad-hoc Beamforming Based On Speaker Extraction For Target-dependent Speech Separation

Abstract

Recently, the research on ad-hoc microphone arrays with deep learning has drawn much attention, especially in speech enhancement and separation. Because an ad-hoc microphone array may cover such a large area that multiple speakers may locate far apart and talk independently, target-dependent speech separation, which aims to extract a target speaker from a mixed speech, is important for extracting and tracing a specific speaker in the ad-hoc array. However, this technique has not been explored yet. In this paper, we propose deep ad-hoc beamforming based on speaker extraction, which is to our knowledge the first work for target-dependent speech separation based on ad-hoc microphone arrays and deep learning. The algorithm contains three components. First, we propose a supervised channel selection framework based on speaker extraction, where the estimated utterance-level SNRs of the target speech are used as the basis for the channel selection. Second, we apply the selected channels to a d

Stats

• citations9
• S2 citations—
• github stars0
• HF likes0
• heat score7.50
• arxiv keyyang2020deep

Related papers

• Deep Ad-hoc Beamforming (2018)9.59
• Deep Attractor Network For Single-microphone Speaker Separation (2016)17.88
• Speaker-independent Speech Separation With Deep Attractor Network (2017)16.84
• Deep Learning Based Stage-wise Two-dimensional Speaker Localization With Large Ad-hoc Microphone Arrays (2022)3.58
• Improving Speaker Discrimination Of Target Speech Extraction With Time-domain Speakerbeam (2020)14.76
• Short-time Deep-learning Based Source Separation For Speech Enhancement In Reverberant Environments With Beamforming (2020)0.00
• Locate And Beamform: Two-dimensional Locating All-neural Beamformer For Multi-channel Speech Separation (2023)3.58
• Enhanced Neural Beamformer With Spatial Information For Target Speech Extraction (2023)2.26