MADUV: The 1st INTERSPEECH Mice Autism Detection via Ultrasound Vocalization Challenge
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912404790575104 |
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| author | Yang, Zijiang Song, Meishu Jing, Xin Zhang, Haojie Qian, Kun Hu, Bin Tamada, Kota Takumi, Toru Schuller, Björn W. Yamamoto, Yoshiharu |
| author_facet | Yang, Zijiang Song, Meishu Jing, Xin Zhang, Haojie Qian, Kun Hu, Bin Tamada, Kota Takumi, Toru Schuller, Björn W. Yamamoto, Yoshiharu |
| contents | The Mice Autism Detection via Ultrasound Vocalization (MADUV) Challenge introduces the first INTERSPEECH challenge focused on detecting autism spectrum disorder (ASD) in mice through their vocalizations. Participants are tasked with developing models to automatically classify mice as either wild-type or ASD models based on recordings with a high sampling rate. Our baseline system employs a simple CNN-based classification using three different spectrogram features. Results demonstrate the feasibility of automated ASD detection, with the considered audible-range features achieving the best performance (UAR of 0.600 for segment-level and 0.625 for subject-level classification). This challenge bridges speech technology and biomedical research, offering opportunities to advance our understanding of ASD models through machine learning approaches. The findings suggest promising directions for vocalization analysis and highlight the potential value of audible and ultrasound vocalizations in ASD detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04292 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MADUV: The 1st INTERSPEECH Mice Autism Detection via Ultrasound Vocalization Challenge Yang, Zijiang Song, Meishu Jing, Xin Zhang, Haojie Qian, Kun Hu, Bin Tamada, Kota Takumi, Toru Schuller, Björn W. Yamamoto, Yoshiharu Sound Artificial Intelligence Machine Learning Audio and Speech Processing The Mice Autism Detection via Ultrasound Vocalization (MADUV) Challenge introduces the first INTERSPEECH challenge focused on detecting autism spectrum disorder (ASD) in mice through their vocalizations. Participants are tasked with developing models to automatically classify mice as either wild-type or ASD models based on recordings with a high sampling rate. Our baseline system employs a simple CNN-based classification using three different spectrogram features. Results demonstrate the feasibility of automated ASD detection, with the considered audible-range features achieving the best performance (UAR of 0.600 for segment-level and 0.625 for subject-level classification). This challenge bridges speech technology and biomedical research, offering opportunities to advance our understanding of ASD models through machine learning approaches. The findings suggest promising directions for vocalization analysis and highlight the potential value of audible and ultrasound vocalizations in ASD detection. |
| title | MADUV: The 1st INTERSPEECH Mice Autism Detection via Ultrasound Vocalization Challenge |
| topic | Sound Artificial Intelligence Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2501.04292 |