UBGAN: Enhancing Coded Speech With Blind And Guided Bandwidth Extension

Abstract

In practical application of speech codecs, a multitude of factors such as the quality of the radio connection, limiting hardware or required user experience necessitate trade-offs between achievable perceptual quality, engendered bitrate and computational complexity. Most conventional and neural speech codecs operate on wideband (WB) speech signals to achieve this compromise. To further enhance the perceptual quality of coded speech, bandwidth extension (BWE) of the transmitted speech is an attractive and popular technique in conventional speech coding. In contrast, neural speech codecs are typically trained end-to-end to a specific set of requirements and are often not easily adaptable. In particular, they are typically trained to operate at a single fixed sampling rate. With the Universal Bandwidth Extension Generative Adversarial Network (UBGAN), we propose a modular and lightweight GAN-based solution that increases the operational flexibility of a wide range of conventional and neu

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