Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system
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arXiv
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| Format: | Preprint |
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2022
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| author | Ronchini, Francesca Cornell, Samuele Serizel, Romain Turpault, Nicolas Fonseca, Eduardo Ellis, Daniel P. W. |
| author_facet | Ronchini, Francesca Cornell, Samuele Serizel, Romain Turpault, Nicolas Fonseca, Eduardo Ellis, Daniel P. W. |
| contents | The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task 4 is to evaluate systems for the detection of sound events in domestic environments using an heterogeneous dataset. The systems need to be able to correctly detect the sound events present in a recorded audio clip, as well as localize the events in time. This year's task is a follow-up of DCASE 2021 Task 4, with some important novelties. The goal of this paper is to describe and motivate these new additions, and report an analysis of their impact on the baseline system. We introduced three main novelties: the use of external datasets, including recently released strongly annotated clips from Audioset, the possibility of leveraging pre-trained models, and a new energy consumption metric to raise awareness about the ecological impact of training sound events detectors. The results on the baseline system show that leveraging open-source pretrained on AudioSet improves the results significantly in terms of event classification but not in terms of event segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_07856 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system Ronchini, Francesca Cornell, Samuele Serizel, Romain Turpault, Nicolas Fonseca, Eduardo Ellis, Daniel P. W. Audio and Speech Processing Sound The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task 4 is to evaluate systems for the detection of sound events in domestic environments using an heterogeneous dataset. The systems need to be able to correctly detect the sound events present in a recorded audio clip, as well as localize the events in time. This year's task is a follow-up of DCASE 2021 Task 4, with some important novelties. The goal of this paper is to describe and motivate these new additions, and report an analysis of their impact on the baseline system. We introduced three main novelties: the use of external datasets, including recently released strongly annotated clips from Audioset, the possibility of leveraging pre-trained models, and a new energy consumption metric to raise awareness about the ecological impact of training sound events detectors. The results on the baseline system show that leveraging open-source pretrained on AudioSet improves the results significantly in terms of event classification but not in terms of event segmentation. |
| title | Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2210.07856 |