Drum Synthesis from Expressive Drum Grids via Neural Audio Codecs
Abstract
Generating realistic drum audio directly from symbolic representations is a challenging task at the intersection of music perception and machine learning. We propose a system that transforms an expressive drum grid, a time-aligned MIDI representation with microtiming and velocity information, into drum audio by predicting discrete codes of a neural audio codec. Our approach uses a Transformer-based model to map the drum grid input to a sequence of codec tokens, which are then converted to waveform audio via a pre-trained codec decoder. We experiment with multiple state-of-the-art neural codecs, namely EnCodec, DAC, and X-Codec, to assess how the choice of audio representation impacts the quality of the generated drums. The system is trained and evaluated on the Expanded Groove MIDI Dataset, E-GMD, a large collection of human drum performances with paired MIDI and audio. We evaluate the fidelity and musical alignment of the generated audio using objective metrics. Overall, our results establish codec-token prediction as an effective route for drum grid-to-audio generation and provide practical insights into selecting audio tokenizers for percussive synthesis.
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
The paper presents a novel approach to drum synthesis that effectively combines symbolic MIDI representations with neural audio codecs. This work contributes to the field by providing a structured methodology for generating high-quality drum audio, with implications for both music technology and machine learning research.
Methodology
The paper introduces a novel approach to drum synthesis by leveraging a Transformer-based model to predict neural audio codec tokens from expressive drum grids. This method is innovative as it employs a two-stage process where the first stage involves mapping MIDI-derived grids to token sequences, and the second stage decodes these tokens into audio waveforms. The use of state-of-the-art neural codecs (EnCodec, DAC, and X-Codec) for this task is particularly noteworthy, as it allows for a controlled comparison of different audio representations and their impact on synthesis quality. The methodology is well-structured, with clear definitions of the expressive drum grid representation and the training process.
Results & evaluation
The experiments are comprehensive, utilizing the Expanded Groove MIDI Dataset (E-GMD), which is a substantial dataset with aligned MIDI and audio data. The evaluation metrics are robust, including both token-level and audio-level assessments, which provide a multifaceted view of the model's performance. The results demonstrate that EnCodec consistently outperforms the other codecs in terms of audio quality and token accuracy, indicating the effectiveness of the proposed approach. However, the paper could benefit from additional qualitative evaluations, such as user studies, to complement the objective metrics.
Reproducibility
The paper provides detailed information about the methodology, including the architecture of the Transformer model, training procedures, and evaluation metrics. However, the absence of a direct implementation or demo could hinder reproducibility for researchers who wish to replicate the results. The project URL does provide access to the code, which is a positive aspect for reproducibility.
Limitations
One limitation is the lack of subjective evaluations, such as listening tests, which are crucial for assessing the perceptual quality of the generated audio. Additionally, the paper notes that increasing model capacity can lead to instability, which is a significant concern for future work. The reliance on objective metrics alone may not fully capture the nuances of audio quality and musicality.
Broader impact
The proposed system has the potential to significantly impact music production by automating the generation of realistic drum audio from MIDI inputs, thereby facilitating creative processes for musicians and producers. The insights gained from codec comparisons could also inform future developments in audio synthesis and machine learning applications in music. The paper presents a novel approach to drum synthesis that effectively combines symbolic MIDI representations with neural audio codecs. This work contributes to the field by providing a structured methodology for generating high-quality drum audio, with implications for both music technology and machine learning research.
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