Sound signature detection by probability density function of normalized amplitudes
probability density function of normalized amplitudes (PDFNA), classical music, kernel method
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).
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.
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