# Binaural Sound Event Localization and Detection based on HRTF Cues for Humanoid Robots

arXiv: https://arxiv.org/abs/2507.20530 · HTML: https://mlsanity.com/paper/2507.20530/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2025.

## Built on by (most cited first, 3 of 3)

- [Auditory Intelligence: Understanding the World Through Sound](https://mlsanity.com/paper/2508.07829/) (2025)
- [Spectro-Temporal Interference Confounds Phase Encoding in Spatial Audio Foundation Models](https://mlsanity.com/paper/2606.14820/) (2026)
- [BinauralVAE: Spatial Audio Reconstruction For World Models](https://mlsanity.com/paper/2609.06837/) (2026)

## Abstract (arXiv)

This paper introduces Binaural Sound Event Localization and Detection (BiSELD), a task that aims to jointly detect and localize multiple sound events using binaural audio, inspired by the spatial hearing mechanism of humans. To support this task, we present a synthetic benchmark dataset, called the Binaural Set, which simulates realistic auditory scenes using measured head-related transfer functions (HRTFs) and diverse sound events. To effectively address the BiSELD task, we propose a new input feature representation called the Binaural Time-Frequency Feature (BTFF), which encodes interaural time difference (ITD), interaural level difference (ILD), and high-frequency spectral cues (SC) from binaural signals. BTFF is composed of eight channels, including left and right mel-spectrograms, velocity-maps, SC-maps, and ITD-/ILD-maps, designed to cover different spatial cues across frequency bands and spatial axes. A CRNN-based model, BiSELDnet, is then developed to learn both spectro-temporal patterns and HRTF-based localization cues from BTFF. Experiments on the Binaural Set show that each BTFF sub-feature enhances task performance: V-map improves detection, ITD-/ILD-maps enable accurate horizontal localization, and SC-map captures vertical spatial cues. The final system achieves a SELD error of 0.110 with 87.1% F-score and 4.4deg localization error, demonstrating the effectiveness of the proposed framework in mimicking human-like auditory perception.