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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.17166 |
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| _version_ | 1866916375580114944 |
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| author | Berg, Axel Engman, Johanna Gulin, Jens Åström, Karl Oskarsson, Magnus |
| author_facet | Berg, Axel Engman, Johanna Gulin, Jens Åström, Karl Oskarsson, Magnus |
| contents | Sound event localization and detection (SELD) systems using audio recordings from a microphone array rely on spatial cues for determining the location of sound events. As a consequence, the localization performance of such systems is to a large extent determined by the quality of the audio features that are used as inputs to the system. We propose a new feature, based on neural generalized cross-correlations with phase-transform (NGCC-PHAT), that learns audio representations suitable for localization. Using permutation invariant training for the time-difference of arrival (TDOA) estimation problem enables NGCC-PHAT to learn TDOA features for multiple overlapping sound events. These features can be used as a drop-in replacement for GCC-PHAT inputs to a SELD-network. We test our method on the STARSS23 dataset and demonstrate improved localization performance compared to using standard GCC-PHAT or SALSA-Lite input features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_17166 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Multi-Target TDOA Features for Sound Event Localization and Detection Berg, Axel Engman, Johanna Gulin, Jens Åström, Karl Oskarsson, Magnus Audio and Speech Processing Machine Learning Sound event localization and detection (SELD) systems using audio recordings from a microphone array rely on spatial cues for determining the location of sound events. As a consequence, the localization performance of such systems is to a large extent determined by the quality of the audio features that are used as inputs to the system. We propose a new feature, based on neural generalized cross-correlations with phase-transform (NGCC-PHAT), that learns audio representations suitable for localization. Using permutation invariant training for the time-difference of arrival (TDOA) estimation problem enables NGCC-PHAT to learn TDOA features for multiple overlapping sound events. These features can be used as a drop-in replacement for GCC-PHAT inputs to a SELD-network. We test our method on the STARSS23 dataset and demonstrate improved localization performance compared to using standard GCC-PHAT or SALSA-Lite input features. |
| title | Learning Multi-Target TDOA Features for Sound Event Localization and Detection |
| topic | Audio and Speech Processing Machine Learning |
| url | https://arxiv.org/abs/2408.17166 |