Modelling and prediction of perceptual segmentation
While listening to music, we somehow make sense of a multiplicity of auditory
events; for example, in popular music we are often able to recognize whether the
current section is a verse or a chorus, and to identify the boundaries between these
segments. This organization occurs at multiple levels, since we can discern motifs,
phrases, sections and other groupings. In this work, we understand segment
boundaries as instants of significant change.
Several studies on music perception and cognition have strived to understand
what types of changes are associated with perceptual structure. However, effects of
musical training, possible differences between real-time and non real-time segmentation, and the relative importance of different musical dimensions on perception
and prediction of segmentation are still unsolved problems. Investigating these
issues can lead to a better understanding of mechanisms used by different types of
listeners in different contexts, and to gain knowledge of the relationship between
perceptual structure and underlying acoustic changes in the music.
In this work, we collected segmentation responses from musical pieces in two
listening experiments, a real-time task and a non real-time task. Boundary data
was obtained from 18 non-musicians in the real-time task and from 18 musicians
in both tasks. We used kernel density estimation to aggregate boundary responses
from multiple participants into a perceptual segment density curve, and novelty
detection to obtain computational models based on audio musical features extracted from the musical stimuli.
Overall, our findings provide evidence for an effect of experimental task on perceptual segmentation and its prediction, and clarify the contribution of local and
global musical characteristics. However, the findings do not resolve discrepancies
in the literature regarding musicianship. Furthermore, this investigation highlights the role of local musical change between homogeneous regions in boundary
perception, the impact of boundary indication delays on segmentation, and the
problem of segmentation time scales on modelling.
...
Julkaisija
University of JyväskyläISBN
978-951-39-6903-5ISSN Hae Julkaisufoorumista
1459-4331Asiasanat
Metadata
Näytä kaikki kuvailutiedotKokoelmat
- Väitöskirjat [3574]
Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Interaction features for prediction of perceptual segmentation : Effects of musicianship and experimental task
Hartmann, Martin; Lartillot, Olivier; Toiviainen, Petri (Routledge, 2017)As music unfolds in time, structure is recognised and understood by listeners, regardless of their level of musical expertise. A number of studies have found spectral and tonal changes to quite successfully model boundaries ... -
Musical Feature and Novelty Curve Characterizations as Predictors of Segmentation Accuracy
Hartmann, Martin; Lartillot, Olivier; Toiviainen, Petri (Aalto-yliopisto, 2017)Novelty detection is a well-established method for analyzing the structure of music based on acoustic descriptors. Work on novelty-based segmentation prediction has mainly concentrated on enhancement of features and ... -
Move the way you feel : effects of musical features, perceived emotions, and personality on music-induced movement
Burger, Birgitta (University of Jyväskylä, 2013) -
Effects of musicianship and experimental task on perceptual segmentation
Hartmann, Martin; Lartillot, Oliver; Toiviainen, Petri (Royal Northern College of Music; European Society for the Cognitive Sciences of Music, 2015)The perceptual structure of music is a fundamental issue in music psychology that can be systematically addressed via computational models. This study estimated the contribution of spectral, rhythmic and tonal descriptors ... -
Segmentation boundaries in accelerometer data of arm motion induced by music : online computation and perceptual assessment
Mendoza Garay, Juan Ignacio (Centre of Sociological Research, 2022)Segmentation is a cognitive process involved in the understanding of information perceived through the senses. Likewise, the automatic segmentation of data captured by sensors may be used for the identification of patterns. ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.