@inproceedings{3669f50def284109983ded7a8a181366,
title = "An Efficient Visual-Based Method for Classifying Instrumental Audio using Deep Learning",
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.",
keywords = "Audio Classification, Audio Visual Transform, Deep Learning, Music Instrument, Neural Networks, Spectrograms, Transfer Learning",
author = "Justin Hall and Wesley O'Quinn and Haddad, {Rami J.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE SoutheastCon, SoutheastCon 2019 ; Conference date: 11-04-2019 Through 14-04-2019",
year = "2019",
month = apr,
doi = "10.1109/SoutheastCon42311.2019.9020571",
language = "English",
series = "Conference Proceedings - IEEE SOUTHEASTCON",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE SoutheastCon, SoutheastCon 2019",
address = "United States",
}