Date of Graduation
5-2017
Document Type
Thesis
Degree Name
Bachelor of Science
Degree Level
Undergraduate
Department
Computer Science and Computer Engineering
Advisor/Mentor
Gauch, John
Committee Member/Reader
Gauch, John
Committee Member/Second Reader
Gashler, Michael
Committee Member/Third Reader
Patitz, Matthew
Abstract
This paper seeks to establish the validity and potential benefits of using existing computer vision techniques on audio samples rather than traditional images in order to consistently and accurately identify a song of origin from a short audio clip of potentially noisy sound. To do this, the audio sample is first converted to a spectrogram image, which is used to generate SURF features. These features are compared against a database of features, which have been previously generated in a similar fashion, in order to find the best match. This algorithm has been implemented in a system that can run as a server, processing “query” clips as they come in and returning to the end user a specific song which matches the input query audio.
Citation
Hollis, M. (2017). Music Feature Matching Using Computer Vision Algorithms. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/47
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Graphics and Human Computer Interfaces Commons, Software Engineering Commons, Theory and Algorithms Commons