6 accepted papers
Language models have been successfully used to model natural signals, such as images, speech, and music. A key component of these models is a high quality neural compression model that can compress high-dimensional natural signals into lower dimensional discrete tokens. To that end, we introduce a h…
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…
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…
This paper presents a practically efficient implementation for non-linear acoustic echo cancellation (NAEC). The echo path is modeled by a novel hybrid Taylor-Volterra pre-processor followed by a linear FIR filter. A cascaded block RLS and unconstrained FLMS adaptive algorithm is developed to jointl…
Existing post-filtering techniques for microphone array speech enhancement have two common deficiencies. First, they assume that the noise is either white or diffuse and cannot deal with point inter-ferers. Second, they estimate the post-filter coefficients using only two microphones at a time and t…