  Generative Adversarial Network (GAN) based vocoders are superior in inference
speed and synthesis quality when reconstructing an audible waveform from an
acoustic representation. This study focuses on improving the discriminator to
promote GAN-based vocoders. Most existing time-frequency-representation-based
discriminators are rooted in Short-Time Fourier Transform (STFT), whose
time-frequency resolution in a spectrogram is fixed, making it incompatible
with signals like singing voices that require flexible attention for different
frequency bands. Motivated by that, our study utilizes the Constant-Q Transform
(CQT), which owns dynamic resolution among frequencies, contributing to a
better modeling ability in pitch accuracy and harmonic tracking. Specifically,
we propose a Multi-Scale Sub-Band CQT (MS-SB-CQT) Discriminator, which operates
on the CQT spectrogram at multiple scales and performs sub-band processing
according to different octaves. Experiments conducted on both speech and
singing voices confirm the effectiveness of our proposed method. Moreover, we
also verified that the CQT-based and the STFT-based discriminators could be
complementary under joint training. Specifically, enhanced by the proposed
MS-SB-CQT and the existing MS-STFT Discriminators, the MOS of HiFi-GAN can be
boosted from 3.27 to 3.87 for seen singers and from 3.40 to 3.78 for unseen
singers.
