{"ID":2840657,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13487","arxiv_id":"2511.13487","title":"Systematic Evaluation of Time-Frequency Features for Binaural Sound Source Localization","abstract":"This study presents a systematic evaluation of time-frequency feature design for binaural sound source localization (SSL), focusing on how feature selection influences model performance across diverse conditions. We investigate the performance of a convolutional neural network (CNN) model using various combinations of amplitude-based features (magnitude spectrogram, interaural level difference - ILD) and phase-based features (phase spectrogram, interaural phase difference - IPD). Evaluations on in-domain and out-of-domain data with mismatched head-related transfer functions (HRTFs) reveal that carefully chosen feature combinations often outperform increases in model complexity. While two-feature sets such as ILD + IPD are sufficient for in-domain SSL, generalization to diverse content requires richer inputs combining channel spectrograms with both ILD and IPD. Using the optimal feature sets, our low-complexity CNN model achieves competitive performance. Our findings underscore the importance of feature design in binaural SSL and provide practical guidance for both domain-specific and general-purpose localization.","short_abstract":"This study presents a systematic evaluation of time-frequency feature design for binaural sound source localization (SSL), focusing on how feature selection influences model performance across diverse conditions. We investigate the performance of a convolutional neural network (CNN) model using various combinations of...","url_abs":"https://arxiv.org/abs/2511.13487","url_pdf":"https://arxiv.org/pdf/2511.13487v2","authors":"[\"Davoud Shariat Panah\",\"Alessandro Ragano\",\"Dan Barry\",\"Jan Skoglund\",\"Andrew Hines\"]","published":"2025-11-17T15:25:49Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"cs.LG\",\"cs.SD\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}