Chord sequences play a fundamental role in music analysis and generation. This paper considers the task of chord sequence generation for electronic dance music, specifically for pieces in the sub-genre of trance. These pieces contain repetitive harmonic sequences with a specific semiotic structure and coherence. The paper presents a formal representation for the semiotic structure of chord sequences, and describes a method for ranking the instances of a semiotic pattern using a statistical model trained on a corpus. Examples of generated chord sequences for a full trance piece are presented.

Music analysis can be seen from the perspective of data mining, aiming to discover succinct patterns that are distinctive: occurring with significantly higher probability in an analysis piece as compared with an anticorpus. In this paper, a computational inductive method for maximally general distinctive pattern discovery in music is developed. The method is applied to the first movement of Brahms’ String Quartet No. 1 in C minor, with the first movements of String Quartets Nos. 2 and 3 used as an anticorpus. The set of patterns discovered represent most of the structures independently proposed by Forte [A. Forte, Motivic design and structural levels in the first movement of Brahms's string quartet in C minor, Music. Quart. 69 (1983), pp. 471–502], including a proposed musical cryptogram, and three new motives are also discovered. The results show that pattern discovery can be a powerful tool for computational music analysis.

Here we present a computational approach to identifying melodic patterns in a dataset of 145 MusicXML scores with the aim of contributing to centonization theory in the Moroccan tradition of Arab-Andalusian Music – a theory in development by expert performer and researcher of this tradition, Amin Chaachoo. Central to his work is the definition of a set of characteristic patterns, or centos, for each ṭab‘, or melodic mode. We apply three methods: TF-IDF, Maximally General Distinctive Patterns (MGDP) and the Structure Induction Algorithm (SIA) to identify characteristic patterns at the level of ṭab‘. A substantial number of the centos proposed by Chaachoo are identified and new melodic patterns are retrieved. A discussion with Chaachoo about the obtained results promoted the elicitation of other categories of recurrent patterns in the tradition different from the centos, contributing to a deeper musicological knowledge of the tradition.