S ociolinguists frequently examine the nature of gradual, internal shifts in languages and dialects over time, arguing for both cognitive and cultural factors, as well as those that might be somehow internal to the language itself. Similarly, musicologists have often argued that musical genres and even specific songs can be examined through gradual diachronic shifts, which seem to be especially accelerated in traditions that rely on oral transmission. For example, Spitzer (1994) examined the stemma of “Oh! Susanna” and noticed that it tended to become more pentatonicized at cadence points by dropping scale degree seven, and suggested that this might be true with folk songs in general. To test this, we employed both experimental and corpus-based paradigms. The experimental approach attempted to simulate oral transmission in a compressed timeframe by involving singers who heard and replicated short musical excerpts, and then would teach a colleague, who in turn passed it on to another participant. Similarly, we conducted a corpus analysis that examined the prevalence of descending stepwise endings in styles of music primarily transmitted orally compared with those transmitted primarily through notation. The experimental results suggest that cadence points in Western folk music are more likely to lose scale degree seven through the act of oral transmission, and the corpus study suggests that, although stylistic constraints play a large role in folk music, there might also be a relationship between transmission and physical affordances.
Computational models of key estimation have struggled to emulate the accuracy levels of human listeners, especially with pieces in the minor mode. The current study proposes a new key-finding algorithm, which utilizes Euclidean distance, rather than correlation, and is trained on the statistical properties of a large musical sample. A model was trained on a dataset of 490 pieces encoded into the Humdrum “kern” format, in which the key was known. This model was tested on a reserve dataset of 492 pieces, and was found to have a significantly higher overall accuracy than previous models. In addition, we determined separate accuracy ratings for major mode and minor mode works for the existing key-finding models and report that most existing models provide greater accuracy for major mode rather than minor mode works. The proposed key-finding algorithm performs more accurately on minor mode works than all of the other models tested, although it does not perform significantly better than the models created by Aarden (2003), Bellman (2005), or Sapp (2011). Finally, an algorithm that combines the Aarden-Essen model (2003) and the proposed algorithm is suggested, and results in significantly more accurate key assessments than all of the other extant models.