BridgeVoC: Revitalizing Neural Vocoder from a Restoration Perspective
Abstract
This paper revisits the neural vocoder task through the lens of audio restoration and propose a novel diffusion vocoder called BridgeVoC. Specifically, by rank analysis, we compare the rank characteristics of Mel-spectrum with other common acoustic degradation factors, and cast the vocoder task as a specialized case of audio restoration, where the range-space spectral (RSS) surrogate of the target spectrum acts as the degraded input. Based on that, we introduce the Schrodinger bridge framework for diffusion modeling, which defines the RSS and target spectrum as dual endpoints of the stochastic generation trajectory. Further, to fully utilize the hierarchical prior of subbands in the time-frequency (T-F) domain, we elaborately devise a novel subband-aware convolutional diffusion network as the data predictor, where subbands are divided following an uneven strategy, and convolutional-style attention module is employed with large kernels for efficient T-F contextual modeling. To enable single-step inference, we propose an omnidirectional distillation loss to facilitate effective information transfer from the teacher model to the student model, and the performance is improved by combining target-related and bijective consistency losses. Comprehensive experiments are conducted on various benchmarks and out-of-distribution datasets. Quantitative and qualitative results show that while enjoying fewer parameters, lower computational cost, and competitive inference speed, the proposed BridgeVoC yields stateof-the-art performance over existing advanced GAN-, DDPMand flow-matching-based baselines with only 4 sampling steps. And consistent superiority is still achieved with single-step inference.
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
This paper presents a significant contribution to the field of neural vocoders by framing the vocoder task as an audio restoration problem and introducing innovative methodologies that enhance performance and efficiency. The comprehensive experiments and results validate the effectiveness of the proposed approach, making it a valuable addition to the literature on audio processing.
Methodology
The paper introduces a novel approach to neural vocoding by framing it as an audio restoration problem, which is a significant shift from traditional methods. The use of the Schrödinger bridge framework for diffusion modeling is innovative and provides a fresh perspective on the vocoder task. The proposed subband-aware convolutional diffusion network (BridgeVoC) effectively leverages hierarchical prior knowledge in the time-frequency domain, which enhances the model's ability to reconstruct audio waveforms. The introduction of an omnidirectional distillation loss for single-step inference is also a noteworthy contribution, as it addresses the common challenge of information transfer in model distillation.
Results & evaluation
The experiments conducted are comprehensive, utilizing various benchmarks and out-of-distribution datasets, which strengthens the validity of the results. The quantitative and qualitative analyses demonstrate that BridgeVoC achieves state-of-the-art performance with fewer parameters and lower computational costs compared to existing models. The paper provides detailed metrics, including MCD, PESQ, and VISQOL, which are essential for evaluating audio quality.
Reproducibility
The paper includes a demo link and mentions the availability of code, which is crucial for reproducibility. However, the details regarding the implementation, such as hyperparameters and training configurations, could be more explicitly stated to facilitate easier replication of the results by other researchers.
Limitations
One limitation is the reliance on specific datasets for training and evaluation, which may affect the generalizability of the model. Additionally, while the single-step inference method shows promise, it may not achieve the same quality as multi-step methods in all scenarios, particularly in complex audio environments.
Broader impact
The proposed method has significant implications for real-time audio processing applications, such as speech synthesis and enhancement, where computational efficiency and audio quality are critical. By addressing the performance-inference dilemma, this work could lead to advancements in various fields, including telecommunications, entertainment, and assistive technologies. This paper presents a significant contribution to the field of neural vocoders by framing the vocoder task as an audio restoration problem and introducing innovative methodologies that enhance performance and efficiency. The comprehensive experiments and results validate the effectiveness of the proposed approach, making it a valuable addition to the literature on audio processing.
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