Sound signature detection by probability density function of normalized amplitudes

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Title

Sound signature detection by probability density function of normalized amplitudes

Author(s) or Creator(s)
Bica, Ion
Zhai, Zhichun
Hu, Rui
Melnyk, Mickey H.
Date

2019

Peer Reviewed?

Yes

Keywords

probability density function of normalized amplitudes (PDFNA)
classical music
kernel method

Description

In this paper, we propose to use the probability density function of normalized amplitudes (PDFNA) to detect distinctive sounds in classical music. Based on data sets generated by waveform audio files (WAV files), we use the kernel method to estimate the probability density function. The confidence interval of the kernel density estimator is also given. In order to illustrate our method, we used the audio data collected from recordings of three composers; Johann Sebastian Bach (1686-1750), Ludwig van Beethoven (1770-1827) and Franz Schubert (1797-1828).

Publication Information

I. Bica, Z. Zhai, R. Hu, M. H. Melnyk, Sound Signature Detection by Probability Density Function of Normalized Amplitudes, Bridges 2019 Conference Proceedings, Tessellations Publishing, Phoenix, Arizona, USA (© 2019 Tessellations), ISBN: 978-1-938664-30-4, 287-294.

Type of Item

Conference Presentations

Language

English

Location

https://roam.macewan.ca/islandora/object/gm:2225

Rights

All Rights Reserved

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