Systematic Evaluation of Time-Frequency Features for Binaural Sound Source Localization

• URL: http://arxiv.org/abs/2511.13487v2
• Date: Tue, 18 Nov 2025 13:25:04 GMT
• Title: Systematic Evaluation of Time-Frequency Features for Binaural Sound Source Localization
• Authors: Davoud Shariat Panah, Alessandro Ragano, Dan Barry, Jan Skoglund, Andrew Hines,
• Abstract summary: This study focuses on how feature selection influences model performance across diverse conditions.<n>We investigate the performance of a convolutional neural network (CNN) model using various combinations of amplitude-based features.
• Score: 47.16858222861157
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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