Benchmarking Audio Signal Representation Techniques for Classification with Convolutional Neural Networks.

Area of Science:

• Machine Learning
• Signal Processing
• Biomedical Engineering

Background:

• Convolutional Neural Networks (CNNs) excel at image classification and are increasingly applied to signal classification tasks.
• Audio signal classification has significant applications in healthcare for detecting and monitoring health conditions.
• Classifying audio signals with CNNs presents unique challenges compared to image classification, such as variable signal dimensions and the need for domain transformation.

Purpose of the Study:

• To overview and benchmark various audio signal representation techniques for classification using CNNs.
• To address challenges related to variable signal lengths and signal transformation in audio classification.
• To provide empirical evidence to guide future research in CNN-based audio signal classification.

Main Methods:

• Benchmarking diverse audio signal representation techniques.
• Implementing approaches to handle variable-length audio signals.
• Exploring methods that combine multiple signal representations to enhance classification accuracy.

Main Results:

• Identification of effective audio signal representation techniques for CNN-based classification.
• Validation of methods that manage varying signal lengths.
• Demonstration that combining multiple representations can improve classification performance.

Conclusions:

• The study provides crucial empirical evidence for selecting appropriate audio signal representations for CNN deployment.
• This research offers guidance for optimizing CNN models in healthcare audio signal analysis.
• The findings contribute to advancing the application of deep learning in audio-based health monitoring.