Effect of Deep Learning Feature Inference Techniques on Respiratory Sounds

TL;DR

This paper investigates how deep learning-based feature inference techniques applied to respiratory sound images impact classification performance, comparing them with traditional methods to evaluate improvements.

Contribution

It introduces the application of image filters to audio signal features and compares deep learning and machine learning results with previous high-performing methods.

Findings

Deep learning features improved classification accuracy.

Image filtering techniques enhanced feature extraction.

Deep learning outperformed traditional methods in this context.

Abstract

Analysis of respiratory sounds increases its importance every day. Many different methods are available in the analysis, and new techniques are continuing to be developed to further improve these methods. Features are extracted from audio signals and trained using different machine learning techniques. The use of deep learning, which is a different method and has increased in recent years, also shows its influence in this field. Deep learning techniques applied to the image of audio signals give good results and continue to be developed. In this study, image filters were applied to the values obtained from audio signals and the results of the features formed from this were examined in machine learning and deep learning techniques. Their results were compared with the results of methods that had previously achieved good results.