  Influenced by the field of Computer Vision, Generative Adversarial Networks
(GANs) are often adopted for the audio domain using fixed-size two-dimensional
spectrogram representations as the "image data". However, in the (musical)
audio domain, it is often desired to generate output of variable duration. This
paper presents VQCPC-GAN, an adversarial framework for synthesizing
variable-length audio by exploiting Vector-Quantized Contrastive Predictive
Coding (VQCPC). A sequence of VQCPC tokens extracted from real audio data
serves as conditional input to a GAN architecture, providing step-wise
time-dependent features of the generated content. The input noise z
(characteristic in adversarial architectures) remains fixed over time, ensuring
temporal consistency of global features. We evaluate the proposed model by
comparing a diverse set of metrics against various strong baselines. Results
show that, even though the baselines score best, VQCPC-GAN achieves comparable
performance even when generating variable-length audio. Numerous sound examples
are provided in the accompanying website, and we release the code for
reproducibility.
