UBGAN: Enhancing Coded Speech with Blind and Guided Bandwidth Extension
Kishan Gupta, Srikanth Korse, Andreas Brendel, Nicola Pia, Guillaume Fuchs

TL;DR
UBGAN is a GAN-based method that enhances wideband speech codecs by extending their bandwidth to super-wideband, improving perceptual quality with flexible, low-bitrate side information options, and demonstrating broad applicability.
Contribution
We introduce UBGAN, a modular GAN-based bandwidth extension method that enhances wideband speech to super-wideband, adaptable to various codecs and bitrates with minimal side information.
Findings
Subjective quality improvements in extended speech signals.
Effective generalization across multiple codecs and bitrates.
Successful operation with minimal side information.
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…
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Taxonomy
TopicsSpeech and Audio Processing · Speech Recognition and Synthesis · Advanced Data Compression Techniques
MethodsSparse Evolutionary Training
