Audio domain transfer is the process of modifying audio signals to match characteristics of a different domain, while retaining the original content. Examples include transferring room acoustics or altering audio effects such as distortion. This paper investigates the potential of Gaussian Flow Bridges, an emerging approach in generative modeling, for these problems. The presented framework addresses the transport problem across different distributions of audio signals through the implementation of a series of two deterministic probability flows. The proposed framework facilitates manipulation of the target distribution properties through a continuous control variable, which defines a certain aspect of the target domain. Notably, this approach does not rely on paired examples for training. To address identified challenges on maintaining the speech content consistent, we recommend a training strategy that incorporates chunk-based minibatch Optimal Transport couplings of data samples and noise. Comparing our unsupervised method with established baselines, we find competitive performance in tasks of reverberation and distortion manipulation. Despite encoutering limitations, the intriguing results obtained in this study underscore potential for further exploration.<\/p>\n","protected":false},"excerpt":{"rendered":"
Audio domain transfer is the process of modifying audio signals to match characteristics of a different domain, while retaining the original content. Examples include transferring room acoustics or altering audio effects such as distortion. This paper investigates the potential of Gaussian Flow Bridges, an emerging approach in generative modeling, for these problems. The presented framework 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