Speech bandwidth extension (BWE) refers to increasing the bandwidth range of speech signals, enhancing the speech quality towards brighter and fuller.
This paper proposed a generative adversarial network (GAN) based BWE model with parallel prediction of Amplitude and Phase spectra, named AP-BWE, which achieves both efficient and high-quality wideband waveform generation.
Notably, to our knowledge, AP-BWE is the first to achieve the direct extension of the high-frequency phase spectrum, which is beneficial for improving the effectiveness of existing BWE methods.
The proposed AP-BWE generator is entirely based on convolutional neural networks (CNNs), it features a dual-stream architecture with mutual interaction, where the amplitude stream and the phase stream communicate with each other and respectively extend the high-frequency components from the narrowband amplitude and phase spectra.
To improve the naturalness of the extended speech signals, we employ a multi-period discriminator at the waveform level and design a pair of multi-resolution amplitude and phase discriminators at the spectral level, respectively.
Experimental results demonstrate that our proposed AP-BWE achieves state-of-the-art performance in speech quality for both BWE tasks targeting sampling rates of 16 kHz and 48 kHz.
In terms of generation efficiency, due to the all-convolutional architecture and all-frame-level operations, the proposed AP-BWE can generate 48 kHz waveform samples 292.3 times faster than real-time on a single RTX 4090 GPU and 18.1 times faster than real-time on CPU.
