Little research has been carried out in order to understand the mechanisms underlying the perception of polyphonic music. Perception of polyphonic music involves thematic recognition, that is, recognition of instances of theme through polyphonic voices, whether they appear unaccompanied, transposed, altered or not. There are many questions still open to debate concerning thematic recognition in the polyphonic domain. One of them, in particular, is the question of whether or not cognitive mechanisms of segmentation and thematic reinforcement facilitate thematic recognition in polyphonic music. This dissertation proposes a connectionist model to investigate the role of segmentation and thematic reinforcement in thematic recognition in polyphonic music. The model comprises two stages. The first stage consists of a supervised artificial neural model to segment musical pieces in accordance with three cases of rhythmic segmentation. The supervised model is trained and tested on sets of contrived patterns, and successfully applied to six musical pieces from J. S. Bach. The second stage consists of an original unsupervised artificial neural model to perform thematic recognition. The unsupervised model is trained and assessed on a four-part fugue from J. S. Bach. The research carried out in this dissertation contributes into two distinct fields. Firstly, it contributes to the field of artificial neural networks. The original unsupervised model encodes and manipulates context information effectively, and that enables it to perform sequence classification and discrimination efficiently. It has application in cognitive domains which demand classifying either a set of sequences of vectors in time or sub-sequences within a unique and large sequence of vectors in time. Secondly, the research contributes to the field of music perception. The results obtained by the connectionist model suggest, along with other important conclusions, that thematic recognition in polyphony is not facilitated by segmentation, but otherwise, facilitated by thematic reinforcement.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:309481 |
| Date | January 1996 |
| Creators | Carpinteiro, Otavio Augusto Salgado |
| Publisher | University of Sussex |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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