  Simulators that generate observations based on theoretical models can be
important tools for development, prediction, and assessment of signal
processing algorithms. In order to design these simulators, painstaking effort
is required to construct mathematical models according to their application.
Complex models are sometimes necessary to represent a variety of real
phenomena. In contrast, obtaining synthetic observations from generative models
developed from real observations often require much less effort. This paper
proposes a generative model based on adversarial learning. Given that
observations are typically signals composed of a linear combination of
sinusoidal waves and random noises, sinusoidal wave generating networks are
first designed based on an adversarial network. Audio waveform generation can
then be performed using the proposed network. Several approaches to designing
the objective function of the proposed network using adversarial learning are
investigated experimentally. In addition, amphibian sound classification is
performed using a convolutional neural network trained with real and synthetic
sounds. Both qualitative and quantitative results show that the proposed
generative model makes realistic signals and is very helpful for data
augmentation and data analysis.
