Document Type
Honors Thesis
Abstract
Accurate and efficient audio classification algorithms have significant applications in the modern world. Song recognition and speech recognition apps rely on complex audio processing software. Recent research in this field has focused primarily on the application of artificially intelligent systems to solve difficult audio processing problems where strictly rule-based algorithms would be untenable. The problem to be investigated in this project is a much simpler binary classification scenario, but with a much smaller sample set than is typically used for neural network training as well as limited processing resources. It will be determined whether in such a scenario a neural network approach will still outperform a strictly rule-based algorithm. Each implementation is given identical sound samples to test against. Samples include a collection of desired and undesired sounds. Accuracy is measured as a percentage of how many test samples are correctly categorized in each category. The results of this testing demonstrate that a neural network based classifier outperforms a rule-based classifier in terms of overall accuracy in this scenario.
Publication Date
5-1-2017
Language
English
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Wabbersen, Bradley, "COMPARISON OF EMBEDDED AUDIO SIGNAL CLASSIFICATION METHODS USING RULE-BASED ALGORITHMS AND ARTIFICIAL NEURAL NETWORKS" (2017). 2017 Spring Honors Capstone Projects. 9.
https://mavmatrix.uta.edu/honors_spring2017/9