Binaural Sound Event Localization and Detection based on HRTF Cues for Humanoid Robots
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916867127377920 |
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| author | Lee, Gyeong-Tae Nam, Hyeonuk Park, Yong-Hwa |
| author_facet | Lee, Gyeong-Tae Nam, Hyeonuk Park, Yong-Hwa |
| contents | 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 4.4° localization error, demonstrating the effectiveness of the proposed framework in mimicking human-like auditory perception. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20530 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Binaural Sound Event Localization and Detection based on HRTF Cues for Humanoid Robots Lee, Gyeong-Tae Nam, Hyeonuk Park, Yong-Hwa Audio and Speech Processing Sound 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 4.4° localization error, demonstrating the effectiveness of the proposed framework in mimicking human-like auditory perception. |
| title | Binaural Sound Event Localization and Detection based on HRTF Cues for Humanoid Robots |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2507.20530 |