Audio Super-Resolution with Latent Bridge Models
Abstract
Audio super-resolution (SR), i.e., upsampling the low-resolution (LR) waveform to the high-resolution (HR) version, has recently been explored with diffusion and bridge models, while previous methods often suffer from sub-optimal upsampling quality due to their uninformative generation prior. Towards high-quality audio super-resolution, we present a new system with latent bridge models (LBMs), where we compress the audio waveform into a continuous latent space and design an LBM to enable a latent-to-latent generation process that naturally matches the LR-toHR upsampling process, thereby fully exploiting the instructive prior information contained in the LR waveform. To further enhance the training results despite the limited availability of HR samples, we introduce frequency-aware LBMs, where the prior and target frequency are taken as model input, enabling LBMs to explicitly learn an any-to-any upsampling process at the training stage. Furthermore, we design cascaded LBMs and present two prior augmentation strategies, where we make the first attempt to unlock the audio upsampling beyond 48 kHz and empower a seamless cascaded SR process, providing higher flexibility for audio post-production. Comprehensive experimental results evaluated on the VCTK, ESC-50, Song-Describer benchmark datasets and two internal testsets demonstrate that we achieve state-of-the-art objective and perceptual quality for any-to-48kHz SR across speech, audio, and music signals, as well as setting the first record for any-to-192kHz audio SR. Demo at https://AudioLBM.github.io/.
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 work is the introduction of a novel audio super-resolution system utilizing Latent Bridge Models, which significantly enhances the quality of audio upsampling beyond existing methods. The comprehensive methodology, rigorous experimental validation, and potential applications highlight its significance in advancing the field of audio processing.
Methodology
The paper introduces a novel approach to audio super-resolution using Latent Bridge Models (LBMs), which compress audio waveforms into a continuous latent space. The methodology is well-structured, leveraging frequency-aware LBMs and a cascaded design to enhance the upsampling process beyond 48 kHz. The integration of informative priors from low-resolution (LR) signals into the generative framework is innovative, allowing for better quality audio synthesis. The paper also presents two prior augmentation strategies to mitigate cascading errors, which is a thoughtful addition to the overall framework. The use of variational autoencoders (VAEs) for compression and the detailed explanation of the bridge process further demonstrate the robustness of the proposed methodology.
Results & evaluation
The experimental setup is comprehensive, utilizing multiple benchmark datasets (VCTK, ESC-50, Song-Describer) and internal test sets to evaluate the performance of the proposed method. The results indicate a significant improvement over existing methods, achieving state-of-the-art performance in both objective and perceptual quality metrics. The paper effectively compares its results against various baselines, providing clear evidence of the advantages of the proposed approach. The ablation studies conducted further validate the contributions of each component of the model.
Reproducibility
The paper includes sufficient details regarding the training setup, model architecture, and evaluation metrics, which enhances reproducibility. However, the absence of a public code repository limits the ability for independent verification of results. The authors mention a demo URL, which may provide some interactive insights, but a complete code release would be beneficial for the community.
Limitations
While the proposed method shows promising results, it is important to note that the reliance on high-quality training data may limit its applicability in scenarios where such data is scarce. Additionally, the paper acknowledges potential misuse of the technology, such as unauthorized synthesis of audio, which raises ethical considerations. The cascading approach, while innovative, may still introduce artifacts that could affect the final output quality if not managed properly.
Broader impact
The implications of this research are significant for various applications, including audio restoration, music production, and hearing aids, where high-quality audio is essential. The ability to upscale audio beyond traditional limits opens new avenues for creative industries and enhances user experiences in audio consumption. However, the ethical concerns regarding misuse must be addressed to prevent potential negative impacts on the industry. The main contribution of this work is the introduction of a novel audio super-resolution system utilizing Latent Bridge Models, which significantly enhances the quality of audio upsampling beyond existing methods. The comprehensive methodology, rigorous experimental validation, and potential applications highlight its significance in advancing the field of audio processing.
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