选对声源定位的时频特征，比堆模型更有效。
Systematic Evaluation of Time-Frequency Features for Binaural Sound Source Localization
- 用幅度和相位特征组合提升定位精度
- 双特征组合在域内表现优异，多特征泛化更强
- 低复杂度模型也能达到领先性能
本研究系统评估了用于双耳声源定位（SSL）的时频特征设计，重点分析特征选择如何影响模型在不同条件下的表现。我们采用卷积神经网络（CNN）模型，测试了多种基于幅度的特征（如幅值谱图、双耳强度差-ILD）和基于相位的特征（如相位谱图、双耳相位差-IPD）的组合。在包含匹配与不匹配头相关传递函数（HRTFs）的域内和域外数据上进行评估发现，精心设计的特征组合通常优于单纯增加模型复杂度。对于域内定位，如ILD+IPD等双特征组合已足够；而实现跨内容泛化则需结合通道谱图与ILD、IPD的丰富输入。使用最优特征组合，我们的低复杂度CNN模型实现了具有竞争力的性能。研究强调了特征设计在双耳声源定位中的关键作用，并为特定场景与通用场景的定位任务提供了实用指导。
原文摘要 · Abstract (English)
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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