Audio domain transfer is the process of modifying audio signals to match
characteristics of a different domain, while retaining the original content.
This paper investigates the potential of Gaussian Flow Bridges, an emerging
approach in generative modeling, for this problem. 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.

研究发现，利用高斯流桥，一种新兴的生成建模方法，实现音频领域转移的潜力，通过一系列确定性概率流处理不同分布的音频信号传输问题。该方法通过连续控制变量来改变目标分布的属性，实现目标域的特性操作，而且无需依赖配对样本训练。通过将基于块的小批量合适传统方法与噪声结合，以保持语音内容的一致性，我们提出了一种训练策略。在混响和失真操作任务中，我们的无监督方法与其他基准方法相比具有竞争性能。尽管存在一些限制，但本研究的有趣结果表明了进一步研究的潜力。

高斯流桥在非配对数据下的音频域转换

Gaussian Flow Bridges for Audio Domain Transfer with Unpaired Data

Invertible flow-based generative models are an effective method for learning
to generate samples, while allowing for tractable likelihood computation and
inference. However, the invertibility requirement restricts models to have the
same latent dimensionality as the inputs. This imposes significant
architectural, memory, and computational costs, making them more challenging to
scale than other classes of generative models such as Variational Autoencoders
(VAEs). We propose a generative model based on probability flows that does away
with the bijectivity requirement on the model and only assumes injectivity.
This also provides another perspective on regularized autoencoders (RAEs), with
our final objectives resembling RAEs with specific regularizers that are
derived by lower bounding the probability flow objective. We empirically
demonstrate the promise of the proposed model, improving over VAEs and AEs in
terms of sample quality.

提出了一种基于概率流的生成模型，通过对模型进行规范化，得到能够提高样本质量的最终目标，进而改进了变分自编码器和自编码器的性能。