Real-time Speech Frequency Bandwidth Extension

In this paper we propose a lightweight model that performs frequency bandwidth extension of speech signals, increasing the sampling frequency from 8kHz to 16kHz, while restoring the high frequency content to a level that is indistinguishable from the original samples at 16kHz. The model architecture is based on SEANet (Sound EnhAncement Network), a wave-to-wave fully convolutional model, which adopts a combination of feature losses and adversarial losses to reconstruct an enhanced version of the input speech. In addition, we propose a version of SEANet that can be deployed on device in streaming mode, achieving an architecture latency of 16ms. When profiled on a single mobile CPU, processing one 16ms frame takes only 1.5ms, so that the total latency is compatible with a deployment in bi-directional voice communication systems.

• Speech Processing

• Machine Translation