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.

Comments

Georgia Southern University faculty member, Rami J. Haddad co-authored "An Efficient Visual-Based Method for Classifying Instrumental Audio using Deep Learning."

Copyright

This work is archived and distributed under the repository's Standard Copyright and Reuse License (opens in new tab). End users may copy, store, and distribute this work without restriction. For all other uses, permission must be obtained from the copyright owners or their authorized agents.

Link to Full Text

Share

COinS