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About this paper
Yongyi Zang, Yi Zhong, Frank Cwitkowitz, Zhiyao Duan
Researchers at the University of Rochester created SYNTHTAB, a synthesized dataset of over 13,000 hours of guitar audio with detailed tablature annotations. Pre-training guitar tablature transcription models on SYNTHTAB significantly enhances their performance and ability to generalize to real-world guitar recordings, addressing the scarcity of annotated data.
Key insights
- Synthetic data can effectively mitigate the problem of data scarcity in specialized music information retrieval tasks like Guitar Tablature Transcription (GTT).
- Pre-training GTT models on a diverse synthetic dataset like SYNTHTAB enables them to learn more generalizable timbral features, leading to improved robustness and reduced overfitting when transferred to real-world data.
- The developed synthesis pipeline offers a reusable and scalable approach for researchers to generate custom or expanded guitar audio-tablature datasets, fostering future advancements in the field.
Summary by alphaXiv.
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