• Corpus ID: 220828947

Improved methods for pattern discovery in music, with applications in automated stylistic composition

@inproceedings{Collins2011ImprovedMF,
  title={Improved methods for pattern discovery in music, with applications in automated stylistic composition},
  author={Tom Collins},
  year={2011}
}
Computational methods for intra-opus pattern discovery (discovering repeated patterns within a piece of music) and stylistic composition (composing in the style of another composer or period) can offer insights into how human listeners and composers undertake such activities. Two studies are reported that demonstrate improved computational methods for pattern discovery in music. In the first, regression models are built with the aim of predicting subjective assessments of a pattern's salience… 
A Computational Evaluation of Musical Pattern Discovery Algorithms
TLDR
This work identifies some possibilities for addressing the divergent patterns extracted from algorithms and generates controllable synthesised data with predetermined patterns planted into random data, thereby leaving us better able to inspect, compare, validate, and select the algorithms.
Pattern Clustering in Monophonic Music by Learning a Non-Linear Embedding From Human Annotations
TLDR
A method of pattern discovery that models human judgement of what constitutes a significant pattern by incorporating annotations of repeated patterns, avoiding the need to design heuristics is developed.
Developing and evaluating computational models of musical style
TLDR
Two computational models of stylistic composition are described and evaluated, called Racchman-Oct2010 (random constrained chain of Markovian nodes, October 2010) and Racch maninof-Oct 2010 (Racchman with inheritance of form), which embeds this model in an analogy-based design system.
In Search of the Consensus Among Musical Pattern Discovery Algorithms
TLDR
A meta-analysis of the (dis)similarities among pattern discovery algorithms’ output and using the output in two fusion methods to combine the pattern output from ten state-of-the-art algorithms using two datasets is explored.
Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora
TLDR
The chapter does not solve the problem of inter-opus pattern discovery, but it can act as a platform for research that will further reduce the gap between what music informaticians do, and what musicologists find interesting.
SIARCT-CFP: Improving Precision and the Discovery of Inexact Musical Patterns in Point-Set Representations
TLDR
Two complementary solutions are proposed and assessed for the precision problem, one involving categorisation (hence reduction of output patterns, and the second involving a new algorithm that calculates the difference between consecutive point pairs, rather than all point pairs.
Music segmentation techniques and greedy path finder algorithm to discover musical patterns
This extended abstract describes the pattern discovery submission to MIREX 2014 of an algorithm that uses music segmentation (or music structure analysis) techniques and a refined greedy method in
Computational approaches for melodic description in indian art music corpora
TLDR
This thesis develops computational approaches for analyzing high-level melodic aspects of music performances in Indian art music (IAM), and proposes an unsupervised approach that employs time-series analysis tools to discover melodic patterns in sizable music collections.
A fuzzy hierarchy-based pattern matching technique for melody classification
TLDR
Experiments conducted on datasets containing a wide range of melodies from classical western and classical Indian background show that the proposed pattern matching technique exhibits consistently better classification success rate compared to the exact n-Gram-based approach and a widely used matching algorithm based on Levenshtein distance.
...
...

References

SHOWING 1-10 OF 185 REFERENCES
Feature Set Patterns in Music
TLDR
Pattern discovery is an important part of computational music-processing systems and patterns that are conserved across many pieces in a large corpus can represent structural building blocks and used for comparative style analysis and music genre recognition.
Representation and Discovery of Vertical Patterns in Music
TLDR
A new method for discovering patterns in the vertical and horizontal dimensions of polyphonic music is described, which finds a small set of vertical patterns that occur in a large number of pieces in the corpus.
Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music
TLDR
This work proposes a geometric approach to repetition discovery in which the music is represented as a multidimensional dataset, which allows polyphonic music to be analysed as efficiently as monophonic music and it can be used to discoverpolyphonic repeated patterns “with gaps” in the timbre, dynamic and rhythmic structure of a passage as well as its pitch structure.
Point-set algorithms for pattern discovery and pattern matching in music
TLDR
The point-set pattern discovery algorithms described here can be adapted for data compression, and the ecient encodings generated when this compression algorithm is run on music data seem to resemble the motivic-thematic analyses produced by human experts.
Using Discovered, Polyphonic Patterns to Filter Computer-generated Music
A metric for evaluating the creativity of a music-generating system is presented, the objective being to generate mazurka-style music that inherits salient patterns from an original excerpt by
Representation and Discovery of Multiple Viewpoint Patterns
TLDR
The method presented is designed to rapidly enumerate all longest significant patterns within a large corpus of music and an application of the method to the Bach chorales is presented.
Structured Polyphonic Patterns
TLDR
This dissertation develops, applies and evaluates a novel method for the representation and retrieval of patterns in musical data, and shows how the method is more restrictive than some existingpolyphonic pattern representations, hence providing a better approximation of the expressive power required for polyphonic patterns.
A Musical Pattern Discovery System Founded on a Modeling of Listening Strategies
TLDR
This chapter discusses motivic analysis as a set of partially detailed operations that carry out a top–down hierarchical segmentation of the musical work, which limits a detailed understanding of musical structure.
An approache for identifying salient repetition in multidimensional representations of polyphonic music
TLDR
This work proposes an approach, based on the generation of set-covers, which aims to identify particularly salient patterns that may be of musicological interest, and is capable of identifying principal musical themes in Bach Two-Part Inventions.
Constraint Application with Higher-Order Programming for Modeling Music Theories
TLDR
A survey of music-constraint programming in general and a detailed comparison of existing systems is provided by Anders and Miranda (in press).
...
...