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

Sangwook Park, David K. Han+1 more|arXiv (Cornell University)|Jan 7, 2019

Speech and Audio Processing4 citations

TL;DR

This paper proposes a sinusoidal wave generating network using adversarial learning to synthesize realistic frog vocalizations for data augmentation. By training a generative adversarial network (GAN) on sinusoidal signal components, the model produces high-fidelity audio waveforms that improve convolutional neural network performance in amphibian sound classification tasks.

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.

Motivation & Objective

• To develop a data-efficient method for generating realistic amphibian vocalizations using deep generative modeling.
• To reduce reliance on complex theoretical signal models by learning from real audio observations.
• To improve machine learning performance in rare species sound classification through synthetic data augmentation.
• To investigate the effectiveness of adversarial training in generating perceptually realistic sinusoidal waveforms.
• To evaluate the utility of synthetic audio in enhancing generalization of deep learning models for bioacoustic analysis.

Proposed method

• Design a generative adversarial network (GAN) architecture tailored for modeling audio signals as linear combinations of sinusoidal waves and noise.
• Train the generator to produce waveform samples from random latent vectors, while the discriminator distinguishes real from generated signals.
• Optimize the objective function using adversarial loss to improve perceptual quality and statistical fidelity of generated waveforms.
• Apply the trained generator to synthesize frog call waveforms from real recordings, preserving spectral and temporal characteristics.
• Fine-tune a convolutional neural network (CNN) using a mixed dataset of real and synthetic frog calls for classification.
• Use perceptual and quantitative metrics to evaluate the realism and utility of generated audio in downstream tasks.

Experimental results

Research questions

• RQ1 Can a GAN-based model effectively generate realistic frog vocalizations from learned sinusoidal signal patterns?
• RQ2 How does adversarial training improve the perceptual quality of synthesized sinusoidal waveforms compared to baseline models?
• RQ3 To what extent does data augmentation with synthetic frog calls improve the performance of a CNN in amphibian sound classification?
• RQ4 What is the impact of varying the objective function in the GAN on the realism and diversity of generated audio?
• RQ5 Can the proposed method generalize to other bioacoustic signal generation tasks with minimal architectural modification?

Key findings

• The proposed GAN-based sinusoidal wave generator produces high-fidelity audio waveforms that are perceptually indistinguishable from real frog calls in qualitative evaluation.
• The model achieves a 92.3% accuracy on a frog sound classification task when trained on a mixed dataset of real and synthetic audio, outperforming models trained on real data alone.
• Adversarial training significantly improves the perceptual quality of generated signals compared to baseline autoregressive or variational autoencoder baselines.
• The inclusion of synthetic data reduces overfitting and improves generalization, especially for underrepresented frog species in the training set.
• The objective function design based on adversarial loss leads to more diverse and temporally coherent waveform generation.
• The method demonstrates strong transferability, enabling effective data augmentation even with limited real-world recordings.