Electrical & Computer Engineering: Faculty Publications
An Efficient Visual-Based Method for Classifying Instrumental Audio using Deep Learning
Document Type
Conference Proceeding
Publication Date
4-1-2019
Publication Title
SoutheastCon 2019 Proceedings
DOI
10.1109/SoutheastCon42311.2019.9020571
Abstract
In this paper, an efficient method for classifying and identifying instrumental audio is proposed via utilizing a deep learning image classification algorithm. The method of classification will involve analyzing the visual equivalent of an audio sample with a neural network to identify the generating musical instrument. Audio samples are converted into a logarithmic spectrogram format, which allows visual classifiers to attempt the identification of the audio source. The primary focus is on developing an efficient method for analyzing audio spectrograms using various forms of neural networks and analysis techniques. The use of deep learning convolutional neural networks in analyzing visually formatted audio data provides an enhanced classification method over traditional schemes. A classification accuracy of 73.7% was achieved with a limited data set and minimal manipulation of network architecture.
Recommended Citation
Hall, Justin, Wesley O'Quinn, Rami J. Haddad.
2019.
"An Efficient Visual-Based Method for Classifying Instrumental Audio using Deep Learning."
SoutheastCon 2019 Proceedings: Institute of Electrical and Electronics Engineers Inc..
doi: 10.1109/SoutheastCon42311.2019.9020571
https://digitalcommons.georgiasouthern.edu/electrical-eng-facpubs/194
Copyright
See the publisher's copyright and reuse policies.
Comments
Georgia Southern University faculty member, Rami J. Haddad co-authored "An Efficient Visual-Based Method for Classifying Instrumental Audio using Deep Learning."