UBGAN：通过盲式和引导式带宽扩展增强编码语音
UBGAN: Enhancing Coded Speech with Blind and Guided Bandwidth Extension
AI总结：
UBGAN通过模块化轻量的GAN模型实现宽带语音的带宽扩展，提升编码语音的感知质量并增强多编解码器的适应性。
摘要：
在语音编解码器的实际应用中，诸如无线电连接质量、限制硬件或所需用户体验等因素都需要在可实现的感知质量、生成比特率和计算复杂性之间进行权衡。大多数传统和神经语音编解码器通常针对宽带（WB）语音信号进行操作以达成这种权衡。为了进一步提高编码语音的感知质量，传输语音的带宽扩展（BWE）是一种有吸引力且流行的传统语音编解码技术。相比之下，神经语音编解码器通常被端到端地训练以满足特定的要求，并且通常难以适应。特别是，它们通常被训练以在单一固定采样率下运行。通过通用带宽扩展生成对抗网络（UBGAN），我们提出了一种模块化且轻量的基于GAN的解决方案，以增加广泛传统和神经编解码器的操作灵活性。我们的模型在子带域中运行，并将WB信号的带宽从8 kHz扩展到16 kHz，从而产生超宽带（SWB）信号。我们进一步引入了两种变体，即引导式UBGAN和盲式UBGAN，其中引导版本在编码器比特率之外以极低的比特率传输量化学习表示作为附加的侧信息，而盲BWE则不使用此类侧信息。我们的主观评估展示了UBGAN应用于WB编解码器的优势，并突显了我们提出的方法在多种编解码器和比特率上的泛化能力。
英文摘要：
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 neural codecs. Our model operates in the subband domain and extends the bandwidth of WB signals from 8 kHz to 16 kHz, resulting in super-wideband (SWB) signals. We further introduce two variants, guided-UBGAN and blind-UBGAN, where the guided version transmits quantized learned representation as a side information at a very low bitrate additional to the bitrate of the codec, while blind-BWE operates without such side-information. Our subjective assessments demonstrate the advantage of UBGAN applied to WB codecs and highlight the generalization capacity of our proposed method across multiple codecs and bitrates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
