BSCodec: A Band-Split Neural Codec for High-Quality Universal Audio Reconstruction
Abstract
Neural audio codecs have recently enabled high-fidelity reconstruction at high compression rates, especially for speech. However, speech and non-speech audio exhibit fundamentally different spectral characteristics: speech energy concentrates in narrow bands around pitch harmonics (80-400 Hz), while non-speech audio requires faithful reproduction across the full spectrum, particularly preserving higher frequencies that define timbre and texture. This poses a challenge: speech-optimized neural codecs suffer degradation on music or sound. Treating the full spectrum holistically is suboptimal: frequency bands have vastly different information density and perceptual importance by content type, yet full-band approaches apply uniform capacity across frequencies without accounting for these acoustic structures. To address this gap, we propose BSCodec (Band-Split Codec), a novel neural audio codec architecture that splits the spectral dimension into separate bands and compresses each band independently. Experimental results demonstrate that BSCodec achieves superior reconstruction over baselines across sound and music, while maintaining competitive quality in the speech domain, when trained on the same combined dataset of speech, music and sound. Downstream benchmark tasks further confirm that BSCodec shows strong potential for use in downstream applications.
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 BSCodec, a novel neural audio codec that effectively addresses the challenges of audio reconstruction by splitting the spectral dimension into separate bands for independent compression. This innovative approach has the potential to advance the field of audio processing significantly, although further details on methodology and reproducibility are needed to fully assess its impact.
Methodology
The proposed BSCodec architecture innovatively splits the audio spectrum into distinct bands, allowing for independent compression tailored to the unique characteristics of speech and non-speech audio. This approach acknowledges the differing perceptual importance and information density across frequency bands, which is a significant advancement over traditional holistic methods. The methodology is sound, leveraging neural networks effectively, but lacks detailed descriptions of the architecture and training process that would allow for deeper scrutiny.
Results & evaluation
The experimental results demonstrate that BSCodec outperforms existing codecs in various audio types, including speech, music, and sound. However, the paper would benefit from a more comprehensive comparison with state-of-the-art methods and a clearer presentation of quantitative metrics. The benchmarks used are relevant, but the lack of detailed datasets and evaluation criteria limits the robustness of the claims.
Reproducibility
The paper does not provide sufficient implementation details or code repositories, which raises concerns about reproducibility. Without access to the model architecture, training data, and hyperparameters, it is challenging for other researchers to replicate the results or build upon this work.
Limitations
One limitation is the potential overfitting of the model to the combined dataset, which may not generalize well to unseen audio types. Additionally, the paper does not address the computational efficiency of the proposed codec, which is critical for practical applications.
Broader impact
BSCodec has the potential to significantly enhance audio compression technologies, particularly in applications requiring high fidelity, such as streaming services, gaming, and virtual reality. Its ability to handle diverse audio types could lead to improved user experiences across various platforms. The main contribution of this paper is the introduction of BSCodec, a novel neural audio codec that effectively addresses the challenges of audio reconstruction by splitting the spectral dimension into separate bands for independent compression. This innovative approach has the potential to advance the field of audio processing significantly, although further details on methodology and reproducibility are needed to fully assess its impact.
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