Systematic Evaluation of Time-Frequency Features for Binaural Sound Source Localization
It addresses feature design for binaural sound source localization, providing practical guidance for domain-specific and general-purpose applications, but is incremental as it focuses on systematic evaluation rather than introducing new methods.
This study systematically evaluated time-frequency feature combinations for binaural sound source localization, finding that optimal feature sets like ILD + IPD or richer inputs improve performance and generalization, with a low-complexity CNN achieving competitive results.
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.