Alternative Representation of Information for Acoustics (ARIA)
Abstract
Classification of objects based on acoustic or electromagnetic measurements is performed in many remote sensing applications underwater and in-air. Active sonar in sonification induces multiple types of acoustic scattering phenomena including direct reflection as well as structural resonance. It is observed that the choice of representation for the raw measurement affects the shape and strength of discriminatory features and the classification performance that utilize them. Using in-air acoustic measurements collected in a noise-controlled laboratory setting, this work develops a statistical model for discriminatory features and a framework to identify the discriminatory pixels in multiple representations as well as an approach to quantify their discriminatory power in the presence of additive noise of varying levels. This framework is used to compare the relative classification performance bounds under independent pixels assumption as well as conventional feature energy detector.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 28, 2022
- Accession Number
- AD1183900
Entities
People
- J. Daniel Park
Organizations
- Pennsylvania State University