A Novel Score-CAM based Denoiser for Spectrographic Signature Extraction without Ground Truth

TL;DR

This paper introduces a Score-CAM based denoiser and a GAN architecture to improve underwater audio spectrogram classification without requiring ground truth data, achieving state-of-the-art noise reduction and classification accuracy.

Contribution

It presents a novel Score-CAM based denoiser and a GAN framework for training on noisy spectrograms without ground truth, enhancing underwater audio classification.

Findings

Achieved state-of-the-art noise reduction accuracy.

Improved classification accuracy over existing methods.

Effective denoising across various real-world acoustic data distributions.

Abstract

Sonar based audio classification techniques are a growing area of research in the field of underwater acoustics. Usually, underwater noise picked up by passive sonar transducers contains all types of signals that travel through the ocean and is transformed into spectrographic images. As a result, the corresponding spectrograms intended to display the temporal-frequency data of a certain object often include the tonal regions of abundant extraneous noise that can effectively interfere with a 'contact'. So, a majority of spectrographic samples extracted from underwater audio signals are rendered unusable due to their clutter and lack the required indistinguishability between different objects. With limited clean true data for supervised training, creating classification models for these audio signals is severely bottlenecked. This paper derives several new techniques to combat this problem by developing a novel Score-CAM based denoiser to extract an object's signature from noisy spectrographic data without being given any ground truth data. In particular, this paper proposes a novel generative adversarial network architecture for learning and producing spectrographic training data in similar distributions to low-feature spectrogram inputs. In addition, this paper also a generalizable class activation mapping based denoiser for different distributions of acoustic data, even real-world data distributions. Utilizing these novel architectures and proposed denoising techniques, these experiments demonstrate state-of-the-art noise reduction accuracy and improved classification accuracy than current audio classification standards. As such, this approach has applications not only to audio data but for countless data distributions used all around the world for machine learning.