Rochester Institute of Technology RIT Scholar Works Theses 2010 Perceptual audio classification using principal component analysis Zak Burka Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Burka, Zak, "Perceptual audio classification using principal component analysis" (2010). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact
[email protected]. Perceptual Audio Classification Using Principal Component Analysis Zak Burka A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Science Department of Computer Science Golisano College of Computing and Information Sciences Rochester Institute of Technology Approved May 6, 2010 Abstract The development of robust algorithms for the recognition and classifi- cation of sensory data is one of the central topics in the area of intelligent systems and computational vision research. In order to build better intelli- gent systems capable of processing environmental data accurately, current research is focusing on algorithms which try to model the types of process- ing that occur naturally in the human brain. In the domain of computer vision, these approaches to classification are being applied to areas such as facial recognition, object detection, motion tracking, and others. This project investigates the extension of these types of perceptual clas- sification techniques to the realm of acoustic data. As part of this effort, an algorithm for audio fingerprinting using principal component analysis for feature extraction and classification was developed and tested.