LatentFlowSR: High-Fidelity Audio Super-Resolution via Noise-Robust Latent Flow Matching
Abstract
Audio super-resolution aims to recover missing high-frequency details from bandwidth-limited low-resolution audio, thereby improving the naturalness and perceptual quality of the reconstructed signal. However, most existing methods directly operate in the waveform or time-frequency domain, which not only involves high-dimensional generation spaces but is also largely limited to speech tasks, leaving substantial room for improvement on more complex audio types such as sound effects and music. To mitigate these limitations, we introduce LatentFlowSR, a new audio super-resolution approach that leverages conditional flow matching (CFM) within a latent representation space. Specifically, we first train a noise-robust autoencoder, which encodes low-resolution audio into a continuous latent space. Conditioned on the low-resolution latent representation, a CFM mechanism progressively generates the corresponding high-resolution latent representation from a Gaussian prior with a one-step ordinary differential equation (ODE) solver. The resulting high-resolution latent representation is then decoded by the pretrained autoencoder to reconstruct the high-resolution audio. Experimental results demonstrate that LatentFlowSR consistently outperforms baseline methods across various audio types and super-resolution settings. These results indicate that the proposed method possesses strong high-frequency reconstruction capability and robust generalization performance, providing compelling evidence for the effectiveness of latent-space modeling in audio super-resolution. All relevant code will be made publicly available upon completion of the paper review process.
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 LatentFlowSR, an innovative audio super-resolution method that effectively utilizes latent-space modeling and conditional flow matching to achieve high-fidelity audio reconstruction. This work represents a significant advancement in the field of audio processing, particularly in its ability to handle diverse audio types and improve computational efficiency.
Methodology
The methodology proposed in LatentFlowSR is innovative, leveraging a noise-robust autoencoder to encode low-resolution audio into a latent space, followed by a conditional flow matching (CFM) mechanism to generate high-resolution audio. The use of an ODE solver for generating high-resolution latent representations is a novel approach that enhances computational efficiency. The architecture incorporates a U-Net style for estimating the velocity field, which is well-suited for capturing both local and global audio features. The integration of noise robustness during training further strengthens the model's performance in real-world applications.
Results & evaluation
The experimental evaluation is comprehensive, utilizing diverse datasets that include speech, sound effects, and music, which allows for a thorough assessment of the model's capabilities. The results demonstrate significant improvements over baseline methods in both objective metrics (LSD, LSD-HF, ViSQOL) and subjective evaluations (MOS), indicating strong performance across various audio types and degradation levels. The paper also includes a detailed analysis of computational complexity, showcasing the efficiency of the LatentFlowSR model.
Reproducibility
The paper outlines the implementation details and training strategies clearly, which aids in reproducibility. The authors mention the use of specific datasets, training steps, and optimization techniques, which are essential for others to replicate their work. However, the lack of a public code repository at the time of review may hinder immediate reproducibility.
Limitations
One limitation is the reliance on a specific architecture and training strategy, which may not generalize well to all audio super-resolution tasks. Additionally, while the model shows strong performance on the tested datasets, further validation on more diverse and challenging datasets would be beneficial to fully assess its generalization capabilities.
Broader impact
The proposed LatentFlowSR has significant implications for various applications in audio processing, including speech synthesis, music restoration, and sound effects enhancement. Its ability to recover high-frequency details can improve the quality of audio in consumer products, entertainment, and communication technologies. The methodology could also inspire further research into latent-space modeling in other domains of machine learning. The main contribution of this paper is the introduction of LatentFlowSR, an innovative audio super-resolution method that effectively utilizes latent-space modeling and conditional flow matching to achieve high-fidelity audio reconstruction. This work represents a significant advancement in the field of audio processing, particularly in its ability to handle diverse audio types and improve computational efficiency.
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