TUTTI: Toward generalizable audio-to-score transcription via fully synthesized data

• URL: http://arxiv.org/abs/2609.00640v1
• Date: Tue, 01 Sep 2026 03:17:03 GMT
• Title: TUTTI: Toward generalizable audio-to-score transcription via fully synthesized data
• Abstract summary: Generalizable Audio-to-Score (A2S) transcription is constrained by the severe scarcity of high-quality, real-world paired data.<n>We introduce TUTTI (Transformer for Unified audio-To-score Transcription trained on Synthetic multi-Instrumentation Data), a pre-training paradigm driven by a purely synthetic, large-scale dataset.<n>We empirically demonstrate that pre-training a unified attention-based model on generated, multi-instrumentation data yields a consistently stronger representation than single-instrumentation training.
• Score: 31.79844009849322
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Generalizable Audio-to-Score (A2S) transcription is fundamentally constrained by the severe scarcity of high-quality, real-world paired data. Relying solely on existing human-annotated datasets often restricts the generalization of A2S models, limiting their efficacy primarily to single-instrumentation domains. To break this dependency on scarce real-world data, we introduce TUTTI (Transformer for Unified audio-To-score Transcription trained on Synthetic multi-Instrumentation Data), a pre-training paradigm driven by a purely synthetic, large-scale dataset. Rather than using human-composed scores, we leverage a symbolic music generation model to generate a massive, highly scalable multi-instrumentation corpus and create audio-score pairs with expressive acoustic characteristics. Capitalizing on the generated data, we employ a standard Transformer encoder-decoder architecture. We empirically demonstrate that pre-training a unified attention-based model on generated, multi-instrumentation data yields a consistently stronger foundational representation than single-instrumentation training. When fine-tuned with downstream real-world datasets, TUTTI outperforms previous approaches, establishing new overall state-of-the-art results across various A2S baselines. Notably, TUTTI shows remarkable cross-instrument transferability, effectively adapting to unseen instruments with highly competitive performance. The source code and the TuttiCorpus dataset will be made publicly available at https://github.com/a-musiclover/TUTTI.

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