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Published on: May 23, 2017
Acoustic Analysis of Blues, Country, Folk, Italian Opera, and Rock Singing
Grayson J Bienhold1, David P Meyer2, Owen P Wischhoff1
1Department of Otolaryngology-Head and Neck Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI.
Objective:
This study uses acoustic and nonlinear dynamic analysis metrics to investigate the distinct vocal characteristics of five music genres-blues, country, folk, Italian opera, and rock. This research uses cepstral peak prominence (CPP) and correlation dimension (D2) values to quantify the levels of chaos and aperiodicity in the singing voices across these genres, providing insights into the acoustic makeups and vocal behaviors unique to each singing style. The aim is to determine if these measures are sufficiently sensitive to develop a comprehensive Voice Type Component Profile (VTCP), a measure of the portions of voice types in an acoustic signal, for each genre.
Methods:
Fifty voice samples of the vowel /i/ from the Saarbrucken Voice Database were collected and analyzed as a control group representing healthy and typical phonation. These samples were compared with vocal samples extracted from performances in five musical genres: twenty nine from a popular blues music song, 29 from a popular country music song, 24 from a popular folk music song, 23 from a popular Italian opera song, and 29 from a popular rock music song. In addition to being compared to speech phonation, each musical genre was also compared to the others to explore genre-specific vocal characteristics. The singing samples were randomly selected from YouTube and analyzed for CPP, D2, and VTCP.
Results:
Many significant differences in acoustic metrics between the genres were found. Rock singing exhibited higher D2 values and lower CPP values compared with blues, country, and folk singing, indicating greater levels of aperiodicity. Blues singing had lower CPP values than the other genres. VTCP analysis revealed that rock singing had the lowest proportion of Type 1 signals and the highest proportion of Type 3 signals.
Conclusion:
The findings highlight the unique vocal demands associated with each genre. Understanding these acoustic differences can inform singing instruction and strategies for optimizing vocal health, particularly for genres that exhibit higher levels of chaos (eg, rock). Future research should involve controlled studies with trained singers and advanced vocal analysis techniques to validate and refine these findings.
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