Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916328685699072 |
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| author | Schmid, Florian Primus, Paul Heittola, Toni Mesaros, Annamaria Martín-Morató, Irene Koutini, Khaled Widmer, Gerhard |
| author_facet | Schmid, Florian Primus, Paul Heittola, Toni Mesaros, Annamaria Martín-Morató, Irene Koutini, Khaled Widmer, Gerhard |
| contents | This article describes the Data-Efficient Low-Complexity Acoustic Scene Classification Task in the DCASE 2024 Challenge and the corresponding baseline system. The task setup is a continuation of previous editions (2022 and 2023), which focused on recording device mismatches and low-complexity constraints. This year's edition introduces an additional real-world problem: participants must develop data-efficient systems for five scenarios, which progressively limit the available training data. The provided baseline system is based on an efficient, factorized CNN architecture constructed from inverted residual blocks and uses Freq-MixStyle to tackle the device mismatch problem. The task received 37 submissions from 17 teams, with the large majority of systems outperforming the baseline. The top-ranked system's accuracy ranges from 54.3% on the smallest to 61.8% on the largest subset, corresponding to relative improvements of approximately 23% and 9% over the baseline system on the evaluation set. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_10018 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge Schmid, Florian Primus, Paul Heittola, Toni Mesaros, Annamaria Martín-Morató, Irene Koutini, Khaled Widmer, Gerhard Audio and Speech Processing Sound This article describes the Data-Efficient Low-Complexity Acoustic Scene Classification Task in the DCASE 2024 Challenge and the corresponding baseline system. The task setup is a continuation of previous editions (2022 and 2023), which focused on recording device mismatches and low-complexity constraints. This year's edition introduces an additional real-world problem: participants must develop data-efficient systems for five scenarios, which progressively limit the available training data. The provided baseline system is based on an efficient, factorized CNN architecture constructed from inverted residual blocks and uses Freq-MixStyle to tackle the device mismatch problem. The task received 37 submissions from 17 teams, with the large majority of systems outperforming the baseline. The top-ranked system's accuracy ranges from 54.3% on the smallest to 61.8% on the largest subset, corresponding to relative improvements of approximately 23% and 9% over the baseline system on the evaluation set. |
| title | Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2405.10018 |