Sinusoidal wave generating network based on adversarial learning and its application: synthesizing frog sounds for data augmentation

TL;DR

This paper introduces a sinusoidal wave generative network based on adversarial learning, capable of synthesizing realistic frog sounds for data augmentation, which improves signal processing and classification tasks.

Contribution

The paper presents a novel adversarial learning-based sinusoidal wave generator specifically designed for audio synthesis and data augmentation in bioacoustic applications.

Findings

The proposed network produces realistic sinusoidal wave signals.

Synthetic sounds enhance amphibian sound classification accuracy.

The model outperforms traditional methods in generating audio signals.

Abstract

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.