5 accepted papers
We present a multi-channel audio signal generation scheme based on machine-learning and probabilistic modeling. We start from modeling a multi-channel single-source signal. Such signals are naturally modeled as a single-channel reference signal and a spatial-arrangement (SA) model specified by an SA…
The recent emergence of machine-learning based generative models for speech suggests a significant reduction in bit rate for speech codecs is possible. However, the performance of generative models deteriorates significantly with the distortions present in real-world input signals. We argue that thi…
Rapid advances in machine-learning based generative modeling of speech make its use in speech coding attractive. However, the current performance of such models drops rapidly with noise contamination of the input, preventing use in practical applications. We present a new speech-coding scheme that i…
In order to efficiently transmit and store speech signals, speech codecs create a minimally redundant representation of the input signal which is then decoded at the receiver with the best possible perceptual quality. In this work we demonstrate that a neural network architecture based on VQ-VAE wit…
Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used. We describe how a WaveNet generative speech model can be used to generate high quality speech from the bit stream of a standard parametric coder operatin…