Recapturing the musical performance data in Seashore’s published performance scores
Bibliographic Data
| ID | 20408130 |
|---|---|
| Authors | Johanna Devaney (0000-0002-7353-5271, The Ohio State University, corresponding author) |
| Year | 2015 |
| Volume | 19 |
| Issue | 2 |
| Pages | 214-222 |
| Publication date | 2015-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Musicae Scientiae (JOURNAL) |
| Journal identifiers | ISSN: 1029-8649 • E-ISSN: 2045-4147 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/1029864915580472 |
| OpenAlex | W2166687051 |
| Language | EN |
| Citations received | 3 |
| References cited | 8 |
In the first half of the 20th century, Carl Seashore and colleagues undertook extensive work in performance analysis of a variety of instruments. Their data were embodied in so-called performance scores, which still exist as illustrations of laborious work undertaken by the early pioneers of music performance research and, in their original form, offer today’s researchers the opportunity to only visually examine the experimental data. This article describes the use of image-processing methods to accurately convert the visual data in Seashore’s performance scores into data points. This use of technology offers researchers the opportunity to directly engage with the data collected in Seashore’s laboratory, either for the purposes of validation or performing analyses for which the researchers in Seashore’s time did not have the computational facilities or algorithms to perform. This paper also presents a proof-of-concept study of the vocal performance scores in Harold Seashore’s “An Objective Analysis of Artistic Singing” (1936). Specifically, this study analyzes extracted fundamental frequency data in regards to vibrato rate and depth. Discrepancies between the statistics calculated on the digitized data and those reported in H. Seashore’s publication are discussed, as well as other types of analysis that may be performed on the extracted data
Art · Data science · Embodied cognition · Musical · Singing · Vibrato · Visual arts · Computer Science · Music and Audio Processing · Musicology and Musical Analysis · Neuroscience and Music Perception · Artificial Intelligence
| Unique citing works | 3 |
|---|---|
| Citations per year | 0.3 |
| Citation span | 2016 - 2023 (8) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |