Self-supervised learning method using multiple sampling strategies for general-purpose audio representation
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916757096103936 |
|---|---|
| author | Kuroyanagi, Ibuki Komatsu, Tatsuya |
| author_facet | Kuroyanagi, Ibuki Komatsu, Tatsuya |
| contents | We propose a self-supervised learning method using multiple sampling strategies to obtain general-purpose audio representation. Multiple sampling strategies are used in the proposed method to construct contrastive losses from different perspectives and learn representations based on them. In this study, in addition to the widely used clip-level sampling strategy, we introduce two new strategies, a frame-level strategy and a task-specific strategy. The proposed multiple strategies improve the performance of frame-level classification and other tasks like pitch detection, which are not the focus of the conventional single clip-level sampling strategy. We pre-trained the method on a subset of Audioset and applied it to a downstream task with frozen weights. The proposed method improved clip classification, sound event detection, and pitch detection performance by 25%, 20%, and 3.6%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18984 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-supervised learning method using multiple sampling strategies for general-purpose audio representation Kuroyanagi, Ibuki Komatsu, Tatsuya Sound Audio and Speech Processing We propose a self-supervised learning method using multiple sampling strategies to obtain general-purpose audio representation. Multiple sampling strategies are used in the proposed method to construct contrastive losses from different perspectives and learn representations based on them. In this study, in addition to the widely used clip-level sampling strategy, we introduce two new strategies, a frame-level strategy and a task-specific strategy. The proposed multiple strategies improve the performance of frame-level classification and other tasks like pitch detection, which are not the focus of the conventional single clip-level sampling strategy. We pre-trained the method on a subset of Audioset and applied it to a downstream task with frozen weights. The proposed method improved clip classification, sound event detection, and pitch detection performance by 25%, 20%, and 3.6%. |
| title | Self-supervised learning method using multiple sampling strategies for general-purpose audio representation |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.18984 |