Abstract
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).
