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

arXiv: https://arxiv.org/abs/2511.13487 · HTML: https://mlsanity.com/paper/2511.13487/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [MambaVision: A Hybrid Mamba-Transformer Vision Backbone](https://mlsanity.com/paper/2407.08083/) (2024)
- [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018)
- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [A Sequence Matching Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2002.05865/) (2020)
- [End-to-end Binaural Sound Localisation from the Raw Waveform](https://mlsanity.com/paper/1904.01916/) (2019)
- [Improved feature extraction for CRNN-based multiple sound source localization](https://mlsanity.com/paper/2105.01897/) (2021)
- [Robust Binaural Localization of a Target Sound Source by Combining Spectral Source Models and Deep Neural Networks](https://mlsanity.com/paper/1904.03006/) (2019)
- [Binamix -- A Python Library for Generating Binaural Audio Datasets](https://mlsanity.com/paper/2505.01369/) (2025)
- [BINAQUAL: A Full-Reference Objective Localization Similarity Metric for Binaural Audio](https://mlsanity.com/paper/2505.11915/) (2025)
- [Learning Robust Spatial Representations from Binaural Audio through Feature Distillation](https://mlsanity.com/paper/2508.20914/) (2025)

## Abstract (arXiv)

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.