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©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those systems into new, low resource environments an important topic. In this work, we present the results of adapting a speech enhancement generative adversarial network by fine-tuning the generator with small amounts of data. We investigate the minimum requirements to obtain a stable behavior in terms of several objective metrics in two very different languages: Catalan and Korean. We also study the variability of test performance to unseen noise as a function of the amount of different types of noise available for training. Results show that adapting a pre-trained English model with 10 min of data already achieves a comparable performance to having two orders of magnitude more data. They also demonstrate the relative stability in test performance with respect to the number of training noise types., Peer Reviewed, Postprint (published version)

The authors present a low-bit-rate coding system where the envelope and instantaneous phase of the residual are used. A time-varying filter (short-delay filter) is excited by a signal composed by a parametric version of the residual, multiplied by a sequence from a codebook. Two alternatives are studied for the design of the codebook: sequences formed by random pulses and sequences formed by random phases to simulate the instantaneous phase of the residual, Peer Reviewed, Postprint (published version)