arXiv:2511.13487v3 Announce Type: replace-cross 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 mism
The paper provides a timely evaluation as AI research continues to push the boundaries of sensory processing, particularly in sound source localization vital for agentic systems.
Improved sound source localization directly enhances the capabilities of AI in complex environments, which is crucial for the development of more sophisticated AI agents and robotics.
This research systematically clarifies which time-frequency features are most effective for binaural sound source localization using CNNs, providing a clearer path for future AI system design.
- · AI Agents Developers
- · Robotics Industry
- · Speech Recognition Systems
- · Audio Processing Hardware Manufacturers
AI systems will gain more precise spatial awareness through sound.
This improved spatial audio processing could lead to more robust navigation and interaction for autonomous systems.
Advanced capabilities might accelerate public acceptance and integration of AI agents into daily life, assuming ethical considerations are met.
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