The Sound of Music: Stimulus Features that Differentiate Organized Sound Sequence Categories

Abstract

Sound waves are not labelled, and no two are exactly the same. So how does the brain know if a sound wave is music, language, or something else? Our brains seem to have relatively identifiable pathways for processing these categories of sound sequences. However, the stable quantitative features of each category of sound have not been identified, so how our brain categorizes sound sequences is unknown. This thesis offers a review of cognitive science literature to argue that music is a perceptually distinct category of sound from language and organized environmental sounds, although delineating those categories can be challenging. It also includes an experimental statistics portion, in which 632 audio tracks from global databases were collected, analyzed, and compared. MIRtoolbox (a quantitative audio analysis software) was used to identify features of audio waves that might be essential for differentiating categories of sound sequences. The identified features grouped into four main factors (spectral, tonal, energy, and fluctuation) and had highly significant effects on categorization. In particular, spectral kurtosis, attack phase information, mode, and Mel-frequency cepstral coefficients (quantitative properties of sound waves) were capable of algorithmically differentiating one category of sound sequences from all the others. This preliminary test is an important step in understanding the statistical features that distinguish different classes of organized sound sequences, which is essential for understanding how those features give rise to perceptually meaningful categories through neural processing.