{"ID":2890230,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.20027","arxiv_id":"2507.20027","title":"Binaural Localization Model for Speech in Noise","abstract":"Binaural acoustic source localization is important to human listeners for spatial awareness, communication and safety. In this paper, an end-to-end binaural localization model for speech in noise is presented. A lightweight convolutional recurrent network that localizes sound in the frontal azimuthal plane for noisy reverberant binaural signals is introduced. The model incorporates additive internal ear noise to represent the frequency-dependent hearing threshold of a typical listener. The localization performance of the model is compared with the steered response power algorithm, and the use of the model as a measure of interaural cue preservation for binaural speech enhancement methods is studied. A listening test was performed to compare the performance of the model with human localization of speech in noisy conditions.","short_abstract":"Binaural acoustic source localization is important to human listeners for spatial awareness, communication and safety. In this paper, an end-to-end binaural localization model for speech in noise is presented. A lightweight convolutional recurrent network that localizes sound in the frontal azimuthal plane for noisy re...","url_abs":"https://arxiv.org/abs/2507.20027","url_pdf":"https://arxiv.org/pdf/2507.20027v1","authors":"[\"Vikas Tokala\",\"Eric Grinstein\",\"Rory Brooks\",\"Mike Brookes\",\"Simon Doclo\",\"Jesper Jensen\",\"Patrick A. Naylor\"]","published":"2025-07-26T18:01:45Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"eess.SP\"]","methods":"[]","has_code":false}