Sound signature detection by probability density function of normalized amplitudes
Faculty Advisor
Date
2019
Keywords
probability density function of normalized amplitudes (PDFNA), classical music, kernel method
Abstract (summary)
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.
DOI
Notes
Item Type
Presentation
Language
English
Rights
All Rights Reserved