Sound signature detection by probability density function of normalized amplitudes

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dc.contributor.authorBica, Ion
dc.contributor.authorZhai, Zhichun
dc.contributor.authorHu, Rui
dc.contributor.authorMelnyk, Mickey H.
dc.date.accessioned2020-09-25
dc.date.accessioned2022-05-31T01:15:15Z
dc.date.available2022-05-31T01:15:15Z
dc.date.issued2019
dc.description.abstractIn 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).
dc.format.extent711.09KB
dc.format.mimetypePDF
dc.identifier.citationI. 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.
dc.identifier.urihttps://hdl.handle.net/20.500.14078/1694
dc.languageEnglish
dc.language.isoen
dc.rightsAll Rights Reserved
dc.subjectprobability density function of normalized amplitudes (PDFNA)
dc.subjectclassical music
dc.subjectkernel method
dc.titleSound signature detection by probability density function of normalized amplitudes
dc.typePresentation
dspace.entity.type
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