STACodec: Semantic Token Assignment for Balancing Acoustic Fidelity and Semantic Information in Audio Codecs
Abstract
Neural audio codecs are widely used for audio compression and can be integrated into token-based language models. Traditional codecs preserve acoustic details well but lack semantic information. Recent hybrid codecs attempt to incorporate semantic information through distillation, but this often degrades reconstruction performance, making it difficult to achieve both. To address this limitation, we introduce STACodec, a unified codec that integrates semantic information from self-supervised learning (SSL) models into the first layer of residual vector quantization (RVQ-1) via semantic token assignment (STA). To further eliminate reliance on SSL-based semantic tokenizers and improve efficiency during inference, we propose a semantic pre-distillation (SPD) module, which predicts semantic tokens directly for assignment to the first RVQ layer during inference. Experimental results show that STACodec outperforms existing hybrid codecs in both audio reconstruction and downstream semantic tasks, demonstrating a better balance between acoustic fidelity and semantic capability.
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 STACodec, a novel audio codec that integrates semantic information through a unique token assignment mechanism, achieving a balance between acoustic fidelity and semantic capability. This work significantly advances the state-of-the-art in audio codecs by addressing the limitations of existing hybrid models and providing a clear pathway for future research in multimodal audio processing.
Methodology
The methodology presented in STACodec is innovative, integrating semantic token assignment (STA) into the first layer of residual vector quantization (RVQ-1) to enhance both acoustic fidelity and semantic information in audio codecs. The introduction of the Semantic Pre-Distillation (SPD) module is particularly noteworthy, as it reduces reliance on SSL-based tokenizers and improves inference efficiency. The methodology is well-structured, with clear explanations of the architecture and training objectives, although some equations and references to figures are incomplete in the provided text.
Results & evaluation
The experimental evaluation is robust, utilizing a comprehensive dataset (LibriSpeech) and employing multiple metrics (PESQ, STOI, ViSQOL) for audio reconstruction quality, as well as downstream tasks like ASR and intent classification. The results demonstrate that STACodec outperforms existing hybrid codecs, indicating effective integration of semantic information without significant degradation of audio quality. However, the paper could benefit from more detailed statistical analysis of results and comparisons with additional baseline methods.
Reproducibility
The paper provides a reasonable level of detail regarding the training configurations, model architectures, and evaluation metrics, which supports reproducibility. The availability of the code on GitHub further enhances the potential for other researchers to replicate the findings. However, the absence of specific hyperparameter settings and training procedures might hinder complete reproducibility.
Limitations
One limitation is the reliance on the LibriSpeech dataset, which may not fully represent the diversity of real-world audio scenarios. Additionally, while the SPD module improves efficiency, it may introduce trade-offs in reconstruction fidelity, which the authors acknowledge but do not explore in depth. The paper could also address potential scalability issues when applying STACodec to larger or more complex datasets.
Broader impact
The proposed STACodec has significant implications for the fields of audio processing and machine learning, particularly in applications involving speech recognition and multimodal language models. By effectively balancing acoustic fidelity and semantic information, STACodec could enhance the performance of various audio-related tasks, making it a valuable contribution to the development of more sophisticated audio codecs. The main contribution of this paper is the introduction of STACodec, a novel audio codec that integrates semantic information through a unique token assignment mechanism, achieving a balance between acoustic fidelity and semantic capability. This work significantly advances the state-of-the-art in audio codecs by addressing the limitations of existing hybrid models and providing a clear pathway for future research in multimodal audio processing.
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