Modeling Music as a Time-Frequency Image: A 2D Tokenizer for Music Generation
Abstract
Autoregressive music generation depends strongly on the audio tokenizer. Existing high-fidelity codecs often use residual multi-codebook quantization, which preserves reconstruction quality but complicates language modeling after sequence flattening, as the residual hierarchy imposes strong sequential dependencies and can amplify error accumulation. We propose BandTok, a generation-oriented 2D Mel-spectrogram tokenizer that represents each frame with Mel-frequency band tokens from a single shared codebook. This design yields a physically interpretable time-frequency token grid with a more independent token structure, making it better suited for autoregressive modeling. BandTok improves reconstruction with a multi-scale PatchGAN objective and EMA codebook updates. We further introduce an autoregressive language model with 2D Rotary Position Embedding (2D RoPE) to preserve temporal and frequency-band structure during generation. Experiments show that BandTok improves over residual-codebook tokenizers and achieves strong results in a data-limited setting. The source code and generation demos for this work are publicly available.
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 main contribution of this paper is the introduction of BandTok, a novel 2D Mel-spectrogram tokenizer that enhances autoregressive music generation through improved token independence and reconstruction fidelity. This work significantly advances the field by addressing limitations of existing tokenization methods and providing a robust framework for future research in audio generation.
Methodology
The paper presents BandTok, a novel 2D Mel-spectrogram tokenizer specifically designed for autoregressive music generation. The methodology is well-structured, focusing on improving token independence and reducing error propagation through a shared codebook of Mel-frequency band tokens. The use of a multi-scale PatchGAN discriminator and EMA codebook updates enhances reconstruction fidelity, while the introduction of 2D Rotary Position Embedding (RoPE) effectively preserves the temporal and frequency-band structure during generation. The approach is innovative, leveraging a unique tokenization strategy that contrasts with traditional residual multi-codebook methods.
Results & evaluation
The experiments are comprehensive, comparing BandTok against existing tokenizers and evaluating both reconstruction quality and generation performance. The use of objective metrics like FAD and CLAP scores, alongside subjective assessments, provides a robust evaluation framework. The results indicate that BandTok outperforms residual-codebook tokenizers, demonstrating its effectiveness in a data-limited setting. However, the paper could benefit from more extensive ablation studies to isolate the impact of each component of the proposed method.
Reproducibility
The paper provides sufficient implementation details, including training configurations, datasets, and evaluation metrics, which should facilitate reproducibility. The source code and generation demos are publicly available, further supporting the reproducibility of the results. However, the lack of a clear description of the datasets used for training and evaluation could pose challenges for researchers attempting to replicate the study.
Limitations
One limitation is the reliance on specific datasets, which may affect the generalizability of the results. The paper also does not address potential biases in the training data, which could influence the quality of generated music. Additionally, while the proposed method shows improvements over existing approaches, the paper does not explore the scalability of BandTok with larger datasets or more complex music generation tasks.
Broader impact
The proposed method has significant implications for the field of music generation, particularly in enhancing the quality and fidelity of generated audio. By improving tokenization strategies, BandTok could facilitate advancements in various applications, including music composition, sound design, and interactive audio systems. The integration of multimodal aspects, such as text conditioning, opens avenues for more sophisticated music generation frameworks that could benefit artists and content creators. The main contribution of this paper is the introduction of BandTok, a novel 2D Mel-spectrogram tokenizer that enhances autoregressive music generation through improved token independence and reconstruction fidelity. This work significantly advances the field by addressing limitations of existing tokenization methods and providing a robust framework for future research in audio generation.
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