Efficient Transformer-Based Piano Transcription With Sparse Attention Mechanisms
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912582963560448 |
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| author | Wei, Weixing Yoshii, Kazuyoshi |
| author_facet | Wei, Weixing Yoshii, Kazuyoshi |
| contents | This paper investigates automatic piano transcription based on computationally-efficient yet high-performant variants of the Transformer that can capture longer-term dependency over the whole musical piece. Recently, transformer-based sequence-to-sequence models have demonstrated excellent performance in piano transcription. These models, however, fail to deal with the whole piece at once due to the quadratic complexity of the self-attention mechanism, and music signals are thus typically processed in a sliding-window manner in practice. To overcome this limitation, we propose an efficient architecture with sparse attention mechanisms. Specifically, we introduce sliding-window self-attention mechanisms for both the encoder and decoder, and a hybrid global-local cross-attention mechanism that attends to various spans according to the MIDI token types. We also use a hierarchical pooling strategy between the encoder and decoder to further reduce computational load. Our experiments on the MAESTRO dataset showed that the proposed model achieved a significant reduction in computational cost and memory usage, accelerating inference speed, while maintaining transcription performance comparable to the full-attention baseline. This allows for training with longer audio contexts on the same hardware, demonstrating the viability of sparse attention for building efficient and high-performance piano transcription systems. The code is available at https://github.com/WX-Wei/efficient-seq2seq-piano-trans. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09318 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Transformer-Based Piano Transcription With Sparse Attention Mechanisms Wei, Weixing Yoshii, Kazuyoshi Sound Multimedia This paper investigates automatic piano transcription based on computationally-efficient yet high-performant variants of the Transformer that can capture longer-term dependency over the whole musical piece. Recently, transformer-based sequence-to-sequence models have demonstrated excellent performance in piano transcription. These models, however, fail to deal with the whole piece at once due to the quadratic complexity of the self-attention mechanism, and music signals are thus typically processed in a sliding-window manner in practice. To overcome this limitation, we propose an efficient architecture with sparse attention mechanisms. Specifically, we introduce sliding-window self-attention mechanisms for both the encoder and decoder, and a hybrid global-local cross-attention mechanism that attends to various spans according to the MIDI token types. We also use a hierarchical pooling strategy between the encoder and decoder to further reduce computational load. Our experiments on the MAESTRO dataset showed that the proposed model achieved a significant reduction in computational cost and memory usage, accelerating inference speed, while maintaining transcription performance comparable to the full-attention baseline. This allows for training with longer audio contexts on the same hardware, demonstrating the viability of sparse attention for building efficient and high-performance piano transcription systems. The code is available at https://github.com/WX-Wei/efficient-seq2seq-piano-trans. |
| title | Efficient Transformer-Based Piano Transcription With Sparse Attention Mechanisms |
| topic | Sound Multimedia |
| url | https://arxiv.org/abs/2509.09318 |