Towards Realistic Synthetic Data for Automatic Drum Transcription
Abstract
Deep learning models define the state-of-the-art in Automatic Drum Transcription (ADT), yet their performance is contingent upon large-scale, paired audio-MIDI datasets, which are scarce. Existing workarounds that use synthetic data often introduce a significant domain gap, as they typically rely on low-fidelity SoundFont libraries that lack acoustic diversity. While high-quality one-shot samples offer a better alternative, they are not available in a standardized, large-scale format suitable for training. This paper introduces a new paradigm for ADT that circumvents the need for paired audio-MIDI training data. Our primary contribution is a semi-supervised method to automatically curate a large and diverse corpus of one-shot drum samples from unlabeled audio sources. We then use this corpus to synthesize a high-quality dataset from MIDI files alone, which we use to train a sequence-to-sequence transcription model. We evaluate our model on the ENST and MDB test sets, where it achieves new state-of-the-art results, significantly outperforming both fully supervised methods and previous synthetic-data approaches. The code for reproducing our experiments is publicly available at https://github.com/pier-maker92/ADT_STR
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper introduces a novel semi-supervised method for generating high-quality synthetic data for Automatic Drum Transcription, significantly advancing the state-of-the-art in the field. The comprehensive methodology and rigorous experimental validation highlight its potential impact on music information retrieval and related applications.
Methodology
The paper proposes a semi-supervised approach to curate a large corpus of one-shot drum samples from unlabeled audio sources, addressing the limitations of existing synthetic data methods. The methodology is well-structured, utilizing CLAP embeddings for effective sample classification and ensuring high diversity in the generated data. The systematic approach to building a standardized instrument vocabulary and the detailed description of the data generation pipeline are commendable, demonstrating a clear understanding of the challenges in Automatic Drum Transcription (ADT).
Results & evaluation
The experimental evaluation is robust, employing well-known datasets (ENST and MDB) to benchmark the proposed model. The results indicate a significant performance improvement over previous methods, validating the effectiveness of the proposed approach. The systematic comparison across different experimental settings provides strong evidence for the contributions of each component of the framework, enhancing the credibility of the findings.
Reproducibility
The authors have made their code publicly available, which is a positive aspect for reproducibility. However, the paper could benefit from more detailed documentation regarding the experimental setup, hyperparameters, and specific configurations used in training, which would further facilitate replication of the results.
Limitations
While the paper presents a novel approach, it does not thoroughly address potential limitations such as the reliance on the quality of the one-shot libraries and the inherent challenges of generalizing from synthetic data to real-world applications. Additionally, the impact of the chosen semi-supervised method on the overall performance could be explored further.
Broader impact
The proposed method has significant implications for the field of music information retrieval, particularly in enhancing the accessibility and quality of drum transcription systems. By reducing the dependency on paired datasets, this work could democratize access to high-quality transcription tools for musicians, educators, and researchers, fostering innovation in music technology. This paper introduces a novel semi-supervised method for generating high-quality synthetic data for Automatic Drum Transcription, significantly advancing the state-of-the-art in the field. The comprehensive methodology and rigorous experimental validation highlight its potential impact on music information retrieval and related applications.
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