Effects of Convolutional Autoencoder Bottleneck Width on StarGAN-based
  Singing Technique Conversion
Tung-Cheng Su Yung-Chuan Chang Yi-Wen Liu
Singing technique conversion (STC) refers to the task of converting from one
voice technique to another while leaving the original singer identity, melody,
and linguistic components intact. Previous STC studies, as well as singing
voice conversion research in general, have utilized convolutional autoencoders
(CAEs) for conversion, but how the bottleneck width of the CAE affects the
synthesis quality has not been thoroughly evaluated. To this end, we
constructed a GAN-based multi-domain STC system which took advantage of the
WORLD vocoder representation and the CAE architecture. We varied the bottleneck
width of the CAE, and evaluated the conversion results subjectively. The model
was trained on a Mandarin dataset which features four singers and four singing
techniques: the chest voice, the falsetto, the raspy voice, and the whistle
voice. The results show that a wider bottleneck corresponds to better
articulation clarity but does not necessarily lead to higher likeness to the
target technique. Among the four techniques, we also found that the whistle
voice is the easiest target for conversion, while the other three techniques as
a source produce more convincing conversion results than the whistle.