Discriminating real and synthetic super-resolved audio samples using embedding-based classifiers
Abstract
Generative adversarial networks (GANs) and diffusion models have recently achieved state-of-the-art performance in audio super-resolution (ADSR), producing perceptually convincing wideband audio from narrowband inputs. However, existing evaluations primarily rely on signal-level or perceptual metrics, leaving open the question of how closely the distributions of synthetic super-resolved and real wideband audio match. Here we address this problem by analyzing the separability of real and super-resolved audio in various embedding spaces. We consider both middle-band ($4\to 16$~kHz) and full-band ($16\to 48$~kHz) upsampling tasks for speech and music, training linear classifiers to distinguish real from synthetic samples based on multiple types of audio embeddings. Comparisons with objective metrics and subjective listening tests reveal that embedding-based classifiers achieve near-perfect separation, even when the generated audio attains high perceptual quality and state-of-the-art metric scores. This behavior is consistent across datasets and models, including recent diffusion-based approaches, highlighting a persistent gap between perceptual quality and true distributional fidelity in ADSR models.
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 makes a substantial contribution to the field of audio super-resolution by introducing a novel evaluation framework that reveals critical insights into the performance of generative models. The combination of embedding-based classifiers and traditional metrics offers a more nuanced understanding of how well these models replicate real audio distributions, paving the way for future advancements in the area.
Methodology
The paper introduces a novel approach to evaluating audio super-resolution models by employing embedding-based classifiers to assess the separability of real and synthetic audio samples. The methodology is well-structured, utilizing both linear classifiers and various embedding spaces, including those derived from GAN discriminators and external models like OpenL3 and log-Mel spectrograms. This dual approach allows for a comprehensive analysis of the performance of different ADSR models across multiple tasks, which is a significant advancement over traditional evaluation metrics that often overlook perceptual fidelity.
Results & evaluation
The experiments are robust, involving multiple datasets (VCTK and FMA-small) and a variety of models (AudioUNet, MU-GAN, HiFi-GAN, FlowHigh, and FlashSR). The use of both objective metrics (SNR, LSD) and subjective listening tests (MUSHRA) provides a well-rounded evaluation of the models' performance. The results indicate a clear distinction between real and synthetic audio, even when the latter achieves high perceptual quality, which underscores the effectiveness of the proposed evaluation framework.
Reproducibility
The paper provides sufficient implementation details, including training protocols, hyperparameters, and the use of publicly available code for models like HiFi-GAN, FlowHigh, and FlashSR. This transparency enhances reproducibility, allowing other researchers to replicate the experiments and validate the findings. The availability of the code and demo further supports this aspect.
Limitations
One limitation noted is the reliance on linear classifiers, which may not capture the full complexity of the audio data. Additionally, the study primarily focuses on specific datasets, which may limit the generalizability of the findings to other audio domains or conditions. The paper also acknowledges the persistent gap between perceptual quality and distributional fidelity, indicating that further research is needed to bridge this divide.
Broader impact
This research has significant implications for the field of audio processing, particularly in applications involving speech and music synthesis. By highlighting the limitations of current evaluation metrics, the study encourages the development of more comprehensive assessment frameworks for generative models. This could lead to improvements in audio quality and realism in various applications, including virtual assistants, music production, and entertainment. This paper makes a substantial contribution to the field of audio super-resolution by introducing a novel evaluation framework that reveals critical insights into the performance of generative models. The combination of embedding-based classifiers and traditional metrics offers a more nuanced understanding of how well these models replicate real audio distributions, paving the way for future advancements in the area.
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