تقرير
ElasticAST: An Audio Spectrogram Transformer for All Length and Resolutions
| العنوان: | ElasticAST: An Audio Spectrogram Transformer for All Length and Resolutions |
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| المؤلفون: | Feng, Jiu, Erol, Mehmet Hamza, Chung, Joon Son, Senocak, Arda |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Sound, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | Transformers have rapidly overtaken CNN-based architectures as the new standard in audio classification. Transformer-based models, such as the Audio Spectrogram Transformers (AST), also inherit the fixed-size input paradigm from CNNs. However, this leads to performance degradation for ASTs in the inference when input lengths vary from the training. This paper introduces an approach that enables the use of variable-length audio inputs with AST models during both training and inference. By employing sequence packing, our method ElasticAST, accommodates any audio length during training, thereby offering flexibility across all lengths and resolutions at the inference. This flexibility allows ElasticAST to maintain evaluation capabilities at various lengths or resolutions and achieve similar performance to standard ASTs trained at specific lengths or resolutions. Moreover, experiments demonstrate ElasticAST's better performance when trained and evaluated on native-length audio datasets. Comment: Interspeech 2024. Code is available at https://github.com/JiuFengSC/ElasticAST |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.08691 |
| رقم الأكسشن: | edsarx.2407.08691 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |