Learning a concatenative resynthesis system for noise suppression

This paper introduces a new approach to dictionary-based source separation employing a learned non-linear metric. In contrast to existing parametric source separation systems, this model is able to utilize a rich dictionary of speech signals. In contrast to previous dictionary-based source separation systems, the system can utilize perceptually relevant non-linear features of the noisy and clean audio. This approach utilizes a deep neural network (DNN) to predict whether a noisy chunk of audio contains a given clean chunk. Speaker-dependent experiments on the small-vocabulary CHÎME2-GRID corpus show that this model is able to accurately resynthesize clean speech from noisy observations. Preliminary listening tests show that the system´s output has much higher audio quality than existing parametric systems trained on the same data, achieving noise suppression levels close to those of the original clean speech.