Computer Science

The recognition of melodic structure depends on both the segmentation into structural units, the melodic motifs, and relations of motifs which are mainly determined by similarity. Existing models and studies of segmentation and motivic similarity cover only certain aspects and do not provide a comprehensive or coherent theory.

This paper gives a survey of the infrastructure currently being developed in the MUSITECH project. The aim of this project is to conceptualize and implement a computational environment for navigation and interaction in internet-based musical applications. This comprises the development of data models, exchange formats, interface modules and a software framework. Our approach is to integrate different information and media types like MIDI, audio, text based codes and metadata and their relations, especially to provide means to describe arbitrary musical structures. We attempt to connect different musical domains to support cooperations and synergies. To establish platform independence Java, Extensible Markup Language (XML), and other open standards are used. The object model, a framework and various components for visualization, playback and other common tasks and the technical infrastructure are being developed and will be evaluated within the project.

The recognition of melodic structure depends on both the segmentation into structural units, the melodic motifs, and relations of motifs which are mainly determined by similarity. Existing models and studies of segmentation and motivic similarity cover only certain aspects and do not provide a comprehensive or coherent theory.

This paper gives a survey of the infrastructure currently being developed in the MUSITECH project. The aim of this project is to conceptualize and implement a computational environment for navigation and interaction in internet-based musical applications. This comprises the development of data models, exchange formats, interface modules and a software framework. Our approach is to integrate different information and media types like MIDI, audio, text based codes and metadata and their relations, especially to provide means to describe arbitrary musical structures. We attempt to connect different musical domains to support cooperations and synergies. To establish platform independence Java, Extensible Markup Language (XML), and other open standards are used. The object model, a framework and various components for visualization, playback and other common tasks and the technical infrastructure are being developed and will be evaluated within the project.

This paper describes the use of motif contour classes for efficient retrieval of melodies from music collections. Instead of extracting incipits or themes, complete monophonic pieces are indexed for their motifs, using classes of motif contours. Similarity relations between these classes can be used for a very efficient search. This can serve as a first level search, which can be refined by using more computationally intensive comparisons on its results. The model introduced has been implemented and tested using the MUSITECH framework. We present empirical and analytical results on the retrieval quality, the complexity, and quality/efficiency trade-off.

The task of recognizing patterns and assigning rhythmic structure to unquantized musical input is a fundamental one for interactive musical systems and for searching musical databases since melody is based on rhythm. We use a combination of combinatorial pattern matching and structural interpretation with a match quality rating by a neuro-fuzzy system that incorporates musical knowledge and operates on perceptually relevant features extracted from the input data. It can learn from relatively few expert examples by using iterative training by relative samples. It shows good recognition results and the used methods of pre-filtering and optimization facilitate efficient computation.

Recognition of musical structure is based on grouping and similarity of groups which are interdependent. A system for the recognition of rhythmic motif structure of note sequences is introduced, which determines grouping and group relations between two sequences (comparison) or within one sequence (analysis). The system makes use of knowledge and learning from data and it is open for the integration of different features and rules.

The task of recognizing patterns and assigning rhythmic structure to unquantized musical input is a fundamental one for interactive musical systems and for search in music databases since melody is based on rhythm. We use a combination of combinatorial pattern matching with a match quality rating by a Neuro-Fuzzy system that incorporates musical knowledge and operates on perceptually relevant features extracted from the input data. It can learn form relatively few expert examples and shows improved recognition results compared to conventional quantization and matching methods.

This paper describes a preliminary study on the structure of atonal music. In the same way as sequential association rules of chords can be found in tonal music, sequential association rules of pitch class set categories can be found in atonal music. It has been noted before that certain pitch class sets can be grouped into 6 different categories . In this paper we calculate those categories in a different way and show that virtually all possible pitch class sets can be grouped into these categories. Each piece in a corpus of atonal music was segmented at the bar level and of each segment it was calculated to which category it belongs. The percentages of occurrence of the different categories in the corpus were tabulated, and it turns out that these statistics may be useful for distinguishing tonal from atonal music. Furthermore, sequential association rules were sought within the sequence of categories. The category transition matrix shows how many times it happens that one specific category is followed by another. The statistical significance of each progression can be calculated, and we present the significant progressions as sequential association rules for atonal music.

Melodic segmentation is an important topic for music information retrieval, because it divides melodies into musically relevant units. Most influential theories on melodic segmentation of the last decades have stressed the role of pitch for melodic segmentation. The general assumption was, that relatively large changes or distances in any musical parameter like pitch, time, dynamics, or melodic movement mark segment boundaries. This has generally been accepted despite the lack of empirical studies. Here an empirical study is presented, that investigates the influence of inter-onset-intervals (IOI), intensity accents, pitch intervals, and pitch interval direction changes. The results show a significant influence only for IOIs and intensity, but neither for pitch intervals nor for changes in interval direction. The validity of the results and possible explanations are discussed and directions of further investigations are outlined.

We present a formal description of a neurofuzzy system capable of aligning two sequences recognizing their internal structure. The alignment is done on two levels: grouping of the elements and alignment of groups. The system incorporates expert knowledge on both levels. Furthermore, it can be optimized by a learning algorithm adapted from the neural network domain. The system has been applied to the analysis of musical rhythm in the program Rhythm-Scan which aligns rhythm patterns, e.g. for music tuition. This application is trained with data collected from real-life scenarios and it performs adequately on 97% of the used sample set.

This paper introduces an architecture for rhythm recognition and comparative analysis. A neuro-fuzzy system is used to rate segmentation and structural assignment produced by combinatorial pattern-matching. The system can be trained by examples. It provides fault tolerant, context sensitive and adaptive recognition of musical rhythm with a description of temporal, structural and timing deviations. 2 E.g. , , ]. 3 This system is an extension of a system presented in .

This paper presents a project called i-Maestro (www.i-maestro.org) which develops interactive multimedia environments for technology-enhanced education for music. The project explores novel solutions for music training in both theory and performance. It builds on recent innovations resulting from the development of computer and information technologies, and exploits new pedagogical paradigms with cooperative and interactive self-learning environments, gesture interfaces, and augmented instruments. This paper provides an introduction to the project, discusses the general context along with the background and current developments, together with an overview of the framework and a number of selected tools to support technology-enhanced music learning and teaching.

The similarity judgement of two sequences is often decomposed in similarity judgements of the sequence events with an alignment process. However, in some domains like speech or music, sequences have an internal structure which is important for intelligent processing like similarity judgements. In an alignment task, this structure can be reflected more appropriately by using two levels instead of aligning event by event. This idea is related to the structural alignment framework by Markman and Gentner. Our aim is to align sequences by modelling the segmenting and matching of groups in an input sequence in relation to a target sequence, detecting variations or errors. This is realised as an integrated process, using a neuro-fuzzy system. The selection of segmentations and alignments is based on fuzzy rules which allow the integration of expert knowledge via feature definitions, rule structure, and rule weights. The rule weights can be optimised effectively with an algorithm adapted from neural networks. Thus, the results from the optimisation process are still interpretable. The system has been implemented and tested successfully in a sample application for the recognition of musical rhythm patterns.

The inclusion of Symbolic Music Representation into the MPEG standard and the definition and selection of an appropriate technology are currently worked on in the MPEG Ad-Hoc-Group on Symbolic Music Representation. In this context, the work of the MUSITECH project may be of interest. In MUSITECH an integrated representation of musical information, including score, performance, audio, metadata, and music collections has been developed. The development has shown that a decomposition of musical knowledge is necessary to maximize expressiveness, usefulness, and flexibility of such a representation. Based on this representation, an infrastructure for interactive applications in music consisting of a framework, a component model, and standard components are being developed. Some results are presented in this paper.