Abstract
The ability of a listener to recognize sound sources, and in particular musical instruments from the sounds they produce, raises the question of determining the acoustical information used to achieve such a task. It is now well known that the shapes of the temporal and spectral envelopes are crucial to the recognition of a musical instrument. More recently, Modulation Power Spectra (MPS) have been shown to be a representation that potentially explains the perception of musical instrument sounds. Nevertheless, the question of which specific regions of this representation characterize a musical instrument is still open. An identification task was applied to two subsets of musical instruments: tuba, trombone, cello, saxophone, and clarinet on the one hand, and marimba, vibraphone, guitar, harp, and viola pizzicato on the other. The sounds were processed with filtered spectrotemporal modulations with 2D Gaussian windows. The most relevant regions of this representation for instrument identification were determined for each instrument and reveal the regions essential for their identification. The method used here is based on a “molecular approach,” the so-called bubbles method. Globally, the instruments were correctly identified and the lower values of spectrotemporal modulations are the most important regions of the MPS for recognizing instruments. Interestingly, instruments that were confused with each other led to non-overlapping regions and were confused when they were filtered in the most salient region of the other instrument. These results suggest that musical instrument timbres are characterized by specific spectrotemporal modulations, information which could contribute to music information retrieval tasks such as automatic source recognition.
Highlights
Automatic musical instrument recognition is one of the more complex problems in musical informatics research
Based on the “bubbles” method developed by Gosselin and Schyns (2001), we have shown that globally the most salient spectrotemporal modulations are centered on low rates and low spectral modulations
This does not concur with the general finding that globally low rates and low spectral modulations are relevant and suggests that when instruments were confused, listeners were focusing on a specific region of the Modulation Power Spectra (MPS)
Summary
Automatic musical instrument recognition is one of the more complex problems in musical informatics research. Work on how humans do this could provide important insights concerning how to get machines to do it, as well to improve automatic annotation algorithms, for example. Listeners’ ability to recognize musical instruments has animated research for many years. From several points of view, either purely computational (Brown, 1999; Brown et al, 2001) or purely perceptual (McAdams, 1993, 2013), it has been shown that the acoustic signal encompasses many indices specific to each instrument, which contribute to their recognition. In order to understand what information is essential for algorithms or for perceptual recognition processes, mathematical representations of sound signals have been developed.
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