{"ID":6620573,"CreatedAt":"2026-07-15T01:01:48.440468303Z","UpdatedAt":"2026-07-17T05:26:38.611582588Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.12529","arxiv_id":"2607.12529","title":"Listen first: Output-based multi-microphone speech enhancement","abstract":"Traditionally, hearing-aid speech enhancement (SE) algorithms rely on input-based feature estimation, often derived by a voice activity detector (VAD), to configure beamformers. Yet features extracted from noisy microphone signals can become unreliable in challenging acoustic scenes where users most need help. We introduce a novel paradigm in which the settings of a sound processing system are determined by evaluating characteristics of its output. To demonstrate this idea, we employ an output-based system that selects among a set of minimum power distortionless response (MPDR) beamformers. Although MPDR beamformers are typically avoided due to their sensitivity to steering errors, we show that they become effective within an output-based framework. We compare the proposed system to a conventional input-based minimum variance distortionless response (MVDR) baseline. Experimental results show that the proposed system consistently outperforms the MVDR baseline, particularly at low SNRs, in terms of SNR, ESTOI and PESQ.","short_abstract":"Traditionally, hearing-aid speech enhancement (SE) algorithms rely on input-based feature estimation, often derived by a voice activity detector (VAD), to configure beamformers. Yet features extracted from noisy microphone signals can become unreliable in challenging acoustic scenes where users most need help. We intro...","url_abs":"https://arxiv.org/abs/2607.12529","url_pdf":"https://arxiv.org/pdf/2607.12529v1","authors":"[\"Panos Apostolidis\",\"Svend Feldt\",\"Zheng-Hua Tan\",\"Jan Østergaard\",\"Jesper Jensen\"]","published":"2026-07-14T09:04:29Z","proceeding":"eess.AS","tasks":"[\"eess.AS\"]","methods":"[]","has_code":false}