Language production involves action sequencing to produce fluent speech in real-time, placing a computational burden on working memory that leads to sequencing biases in production. Thus, there currently appears to be a divide between the types of information that can be reliably automatically extracted from audio signals and the types of questions that interest more traditional music researchers. 2017) and Malian (London, Polak, and Jacoby 2017). 2017, and a range of world musics (Serra 2014, Holzapfel, Krebs, andSrinivasamurthy 2014), including Hindustani (Srinivasamurthy et al. ![]() Some of this recent work has taken advantage of tools developed in the music information retrieval community that allow for automatic or semi-automatic analysis of the musical signal to study both jazz (Frieler et al. This was common both in early ethnomusicology work on recordings (see the chapter by Peter Savage in this volume and the recent article by Panteli, Benetos, and Dixon (2018) for a history of some of these corpora), as well as more recent work on film (Richards 2016), performance practice in Western art music (Cook 2007, Leech-Wilkinson 2010, Timmers 2007, and popular musics (Biamonte 2014, Easley 2015, Richards 2017, De Clercq 2017, De Clercq and Temperley 2011. ![]() We propose three possible strategies to install a reliable evaluation process to mitigate some of the inherent problems. Accordingly, we found indications that assessments of origin of a solo are partly driven by aesthetic judgments. Furthermore, the level of expertise of the solo performer does matter, as solos by non-professional humans were on average rated worse than the algorithmic ones. The type of rendition is crucial when assessing artificial jazz solos because expressive and performative aspects (timbre, articulation, micro-timing and band-soloist interaction) seem to be equally if not more important than the syntactical (tone) content. ![]() Results show that jazz experts (64.4% accuracy) but not non-experts (41.7% accuracy) are able to distinguish the computer-generated solos amongst a set of real solos, but with a large margin of error. Second, as there are several issues with Turing-like evaluation processes for generative models of jazz improvisation, we decided to conduct an exploratory online study to gain further insight while testing our algorithm in the context of a variety of human generated solos by eminent masters, jazz students, and non-professionals in various performance renditions. A further ingredient is chord-scale theory to select pitches. It uses a hierarchical Markov model based on mid-level units and the Weimar Bebop Alphabet, with statistics taken from the Weimar Jazz Database. First, we present a generative model for (monophonic) jazz improvisation whose main purpose is testing hypotheses on creative processes during jazz improvisation.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |