In this paper, we propose SpeechSplit, which can blindly decompose speech into its four components by introducing three carefully designed information bottlenecks. Further disentangling the remaining speech components is an under-determined problem in the absence of explicit annotations for each component, which are difficult and expensive to obtain. However, these systems can only disentangle timbre, while information about pitch, rhythm and content is still mixed together. Recently, state-of-the-art voice conversion systems have led to speech representations that can disentangle speaker-dependent and independent information. Obtaining disentangled representations of these components is useful in many speech analysis and generation applications. Speech information can be roughly decomposed into four components: language content, timbre, pitch, and rhythm. We additionally compare performance for compressive sensing to unlearned methods, such as the deep decoder, and we establish theoretical bounds on expected recovery error in the case of a вЂ¦ For compressive sensing, invertible priors can yield higher accuracy than sparsity priors across almost all undersampling ratios, and due to their lack of representation error, invertible priors can yield better reconstructions than GAN priors for images that have rare features of variation within the biased training set, including out-of-distribution natural images. Given a trained generative model, we study the empirical risk formulation of the desired inverse problem under a regularization that promotes high likelihood images, either directly by penalization or algorithmically by initialization.

In this paper, we demonstrate that invertible neural networks, which have zero representation error by design, can be effective natural signal priors at inverse problems such as denoising, compressive sensing, and inpainting. Unfortunately, these models may be unable to represent any particular image because of architectural choices, mode collapse, and bias in the training dataset. Trained generative models have shown remarkable performance as priors for inverse problems in imaging - for example, Generative Adversarial Network priors permit recovery of test images from 5-10x fewer measurements than sparsity priors.

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