StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation

Jean-Marie, Lemercier,; Richter, Julius; Simon, Welker,; Gerkmann, Timo

doi:10.48550/arxiv.2212.11851

Cited by 1 publication

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“…This research area can be categorized into two groups: conditional methods, which require specialized training for specific problems, and zero-shot methods, which leverage priors from unconditional diffusion models. Within the category of conditional models, several works target speech enhancement [33]- [36], image deblurring [8], and JPEG reconstruction [37], among others. It may be noted that these methods all require pairs of clean/degraded samples and a well-thought training data pipeline.…”

Section: B Diffusion Models For Blind Inverse Problemsmentioning

confidence: 99%

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BEHM-GAN: Bandwidth Extension of Historical Music Using Generative Adversarial Networks

Moliner

Välimäki

2023

IEEE/ACM Trans. Audio Speech Lang. Process.

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Audio bandwidth extension aims to expand the spectrum of bandlimited audio signals. Although this topic has been broadly studied during recent years, the particular problem of extending the bandwidth of historical music recordings remains an open challenge. This paper proposes a method for the bandwidth extension of historical music using generative adversarial networks (BEHM-GAN) as a practical solution to this problem. The proposed method works with the complex spectrogram representation of audio and, thanks to a dedicated regularization strategy, can effectively extend the bandwidth of out-of-distribution real historical recordings. The BEHM-GAN is designed to be applied as a second step after denoising the recording to suppress any additive disturbances, such as clicks and background noise. We train and evaluate the method using solo piano classical music. The proposed method outperforms the compared baselines in both objective and subjective experiments. The results of a formal blind listening test show that BEHM-GAN significantly increases the perceptual sound quality in early-20th-century gramophone recordings. For several items, there is a substantial improvement in the mean opinion score after enhancing historical recordings with the proposed bandwidthextension algorithm. This study represents a relevant step toward data-driven music restoration in real-world scenarios.

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Section: B Diffusion Models For Blind Inverse Problemsmentioning

confidence: 99%

“…We then use the denoised recording as the observations that will be used for the warm initialization and for the guidance of the diffusion-based generation. A similar strategy was used for the purpose of speech enhancement in [36]. Fig.…”

Section: Application To Historical Recordingsmentioning

confidence: 99%

BEHM-GAN: Bandwidth Extension of Historical Music Using Generative Adversarial Networks

Moliner

Välimäki

2023

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

show abstract

StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation

Cited by 1 publication

References 0 publications

BEHM-GAN: Bandwidth Extension of Historical Music Using Generative Adversarial Networks

BEHM-GAN: Bandwidth Extension of Historical Music Using Generative Adversarial Networks

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