An approach for identifying salient repetition in multidimensional representations of polyphonic music

SIATEC is an algorithm for discovering patterns in multidimensional datasets (Meredith et al., 2002). This algorithm has been shown to be particularly useful for analysing musical works. However, in raw form, the results generated by SIATEC are large and difficult to interpret. We propose an approach, based on the generation of set-covers, which aims to identify particularly salient patterns that may be of musicological interest. Our method is capable of identifying principal musical themes in Bach Two-Part Inventions, and is able to offer a human analyst interesting insight into the structure of a musical work

Deep neural networks with voice entry estimation heuristics for voice separation in symbolic music representations

In this study we explore the use of deep feedforward neural networks for voice separation in symbolic music representations. We experiment with different network architectures, varying the number and size of the hidden layers, and with dropout. We integrate two voice entry estimation heuristics that estimate the entry points of the individual voices in the polyphonic fabric into the models. These heuristics serve to reduce error propagation at the beginning of a piece, which, as we have shown in previous work, can seriously hamper model performance. The models are evaluated on the 48 fugues from Johann Sebastian Bach’s The Well-Tempered Clavier and his 30 inventions—a dataset that we curated and make publicly available. We find that a model with two hidden layers yields the best results. Using more layers does not lead to a significant performance improvement. Furthermore, we find that our voice entry estimation heuristics are highly effective in the reduction of error propagation, improving performance significantly. Our best-performing model outperforms our previous models, where the difference is significant, and, depending on the evaluation metric, performs close to or better than the reported state of the art

Perception based approach on pattern discovery and organisation of point-set data

The general topic of the thesis is computer aided music analysis on point-set data utilising theories outlined in Timo Laiho’s Analytic-Generative Methodology (AGM) [19]. The topic is in the field of music information retrieval, and is related to previous work on both pattern discovery and computational models of music. The thesis aims to provide analysis results that can be compared to existing studies. AGM introduces two concepts based on perception, sensation and cognitive processing: interval–time complex (IntiC) and musical vectors (muV). These provide a mathematical framework for the analysis of music. IntiC is a value associated with the velocity, or rate of change, between musical notes. Musical vectors are the vector representations of these rates of change. Laiho explains these attributes as meaningful for both music analysis and as tools for music generation. Both of these attributes can be computed from a point-set representation of music data. The concepts in AGM can be viewed as being related to geometric methods for pattern discovery algorithmsof Meredith, Lemström et al.[24] whointroduce afamily of ‘Structure Induction Algorithms’. These algorithms are used to find repeating patterns in multidimensional point-set data. Algorithmic implementations of intiC and muV were made for this thesis and examined in the use of rating and selecting patterns output by the pattern discovery algorithms. In addition software tools for using these concepts of AGM were created. The concepts of AGM and pattern discovery were further related to existing work in computer aided musicology

Discovering Musical Pattern through Perceptive Heuristics.

This paper defends the view that the intricate difficulties challenging the emerging domain of Musical Pattern Discovery, which is dedicated to the automation of motivic analysis, will be overcome only through a thorough taking into account of the specificity of music as a perceptive object. Actual musical patterns, although constantly transformed, are nevertheless perceived by the listener as musical identities. Such dynamical properties of human perception, not reducible to geometrical models, will only be explained with the notions of contexts and expectations. This paper sketches the general principles of a new approach that attempts to build such a general perceptual system. On a sub-cognitive level, patterns are discovered through the detection, by an associative memory, of local similarities. On a cognitive level, patterns are managed by a general logical framework that avoids irrelevant inferences and combinatorial explosion. In this way, actual musical patterns that convey musical significance are discovered. This approach, offering promising results, is a first step toward a complete system of automated music analysis and an explicit modeling of basic mechanisms for music understanding

Rhetorical Pattern Finding

In this paper, we research rhetorical patterns from a musicological and computational standpoint. First, a theoretical examination of what constitutes a rhetorical pattern is conducted. Out of that examination, which includes primary sources and the study of the main composers, a formal definition of rhetorical patterns is proposed. Among the rhetorical figures, a set of imitative rhetorical figures is selected for our study, namely, epizeuxis, palilogy, synonymia, and polyptoton. Next, we design a computational model of the selected rhetorical patterns to automatically find those patterns in a corpus consisting of masses by Renaissance composer Tomás Luis de Victoria. In order to have a ground truth with which to test out our model, a group of experts manually annotated the rhetorical patterns. To deal with the problem of reaching a consensus on the annotations, a four-round Delphi method was followed by the annotators. The rhetorical patterns found by the annotators and by the algorithm are compared and their differences discussed. The algorithm reports almost all the patterns annotated by the experts and some additional patterns. The algorithm reports almost all the patterns annotated by the experts (recall: 98.11%) and some additional patterns (precision: 71.73%). These patterns correspond to rhetorical patterns within other rhetorical patterns, which were overlooked by the annotators on the basis of their contextual knowledge. These results pose issues as to how to integrate that contextual knowledge into the computational model

Structural Segmentation using Set Accented Tones

An approach which efficiently segments Irish Traditional Music into its constituent structural segments is presented. The complexity of the segmentation process is greatly increased due to melodic variation existent within this music type. In order to deal with these variations, a novel method using ‘set accented tones’ is introduced. The premise is that these tones are less susceptible to variation than all other tones. Thus, the location of the accented tones is estimated and pitch information is extracted at these specific locations. Following this, a vector containing the pitch values is used to extract similar patterns using heuristics specific to Irish Traditional Music. The robustness of the approach is evaluated using a set of commercially available Irish Traditional recordings

06171 Abstracts Collection -- Content-Based Retrieval

From 23.04.06 to 28.04.06, the Dagstuhl Seminar 06171 `Content-Based Retrieval\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Computer Aided Statistical Analysis of Motive Use and Compositional Idiom

This thesis discusses the creation of a means of pitch-based data representation which allows automated logging and analysis of melodic motivic material. This system also allows analysis of a number of attributes of a composition which are not readily apparent to human analysis. By using a numerical data format which treats motivically related material as equivalent, groups of tonally equivalent intervals (n-tuples) can be logged and have statistical procedures carried out on them. This thesis looks at four applications of this approach: measuring the most commonly occurring motivic material; creating a transition matrix showing probabilities of movement between intervals; measuring the extent of disjunct or conjunct writing; and measuring concentration of motivic writing (the extent to which motives are reused). Following the discussion of the data representation system, a set of expositions taken from the piano sonatas of Haydn, Mozart, and Clementi are converted to this method of data representation, and results are collected for the above four applications. The implications of the results of this analysis are discussed, and further potential applications of the system are explored