In this paper we give an overview of four algorithms that we have developed for pattern matching, pattern discovery and data compression in multidimensional datasets. We show that these algorithms can fruitfully be used for processing musical data. In particular, we show that our algorithms can discover instances of perceptually signifrant musica 1 repetition that cannot be found using previous approaches. We also describe results that suggest the possibility of using our datacompression algorithm for modelling expert motivic-thematic music analysis.

An important problem in computational music analysis is the representation and automated discovery of recurrent patterns. In this paper we present a new method for pattern representation and discovery in a large corpus of music. Using the formalism of multiple viewpoints, music is viewed as multiple streams of description derived from the basic surface representation. Patterns are discovered within viewpoint sequences derived from the corpus for selected viewpoints. A statistical method is used to restrict attention to only those patterns which occur much more frequently than expected, where expectation is based on a Markov model of viewpoint elements. The concept of the longest significant patterns in a corpus is introduced. The method presented in this paper is designed to rapidly enumerate all longest significant patterns within a large corpus. An application of the method to the Bach chorales is presented.

A huge amount of audio data is accessible to everyone by on-line or off-line information services and it is necessary to develop techniques to automatically describe and deal with this data in a meaningful way. In the particular context of music content processing it is important to take into account the melodic aspects of the sound. The goal of this article is to review the different techniques proposed for melodic description and extraction. Some ideas around the concept of melody are first presented. Then, an overview of the different ways of describing melody is done. As a third step, an analysis of the methods proposed for melody extraction is made, including pitch detection algorithms. Finally, techniques for melodic pattern induction and matching are also studied, and some useful melodic transformations are reviewed. 1

In this paper a number of issues relating to the application of string processing techniques on musical sequences are discussed. A brief survey of some musical string processing algorithms is given and some issues of melodic representation, abstraction, segmentation and categorisation are presented. This paper is not intended towards providing solutions to string processing problems but rather towards highlighting possible stumbling-block areas and raising awareness of primarily music-related particularities that can cause problems in matching applications. 1.

We present a new algorithm, SIA, which discovers maximal repeated patterns in any set of points in Cartesian spaces of any dimensionality. The worst-case running time of SIA is #### ### # ## for a #-dimensional dataset of size n. SIATEC is an extension

The content-based retrieval of Western music has received increasing attention recently. Much of this research deals with monophonic music. Polyphonic music is more common, but also more difficult to represent. Music information retrieval systems must extract viable features before they can define similarity measures. We summarize and categorize representation features that have been used for polyphonic retrieval with the aim of laying standardized groundwork for future feature extraction research. Comparisons with and extensions to monophonic approaches are given, and a new feature, an extension of duration-independent pitch slices, is proposed. Key words: Music retrieval, polyphonic features 1.

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This paper reports on a system for musical information retrieval environments called SMILE. SMILE implements a methodology designed to cope with content-based musical retrieval. The innovation provided by the methodology is due to the synergic combination of sound information retrieval techniques and computer-based melodic surface extraction algorithms. The innovative corollary is that the latter extracts content-descriptive musical phrases from both documents and queries by thus permitting a clear separation between information being searched and indexes describing the semantic content. The information retrieval techniques are employed to index, retrieve, and rank by score the musical documents judged relevant to the final users' information need. Two types of retrieval functions are provided -- a querying function based on a virtual keyboard played by the user, and a browsing function to navigate an automatically constructed hyper-music which is made of documents and phrase...

Extraction of repetitive patterns of the main melody in a given music piece is investigated in this research. A dictionarybased approach is proposed to achieve the task. The input to the proposed system is a piece of music consisting of numerical music scores (e.g. the MIDI file format), and other music forms such as the sound wave have to be converted to numerical music scores first. In the system, segmentation is done based on the tempo information and a music score is decomposed into bars. Each bar is indexed, and a bar index table is built accordingly. Then, an adaptive dictionary-based algorithm known as the Lempel Ziv 78 (LZ-78) is modified and applied to the bar-represented music scores to extract repetitive patterns. The LZ78 algorithm is slightly modified to achieve better results, and the modified LZ78 is named the “Exhaustive Search with Progressive LEngth ” (ESPLE). After this step, pruning is applied to this dictionary to remove non-repeating patterns. Modified LZ78 and pruning are repetitively applied to the updated dictionary, which is generated from the previous cycle, until the dictionary converges. Experiments are performed on MIDI files to demonstrate the superior performance of the proposed algorithm.

Abstract. An algorithm that discovers the themes, motives and other perceptually significant repeated patterns in a musical work can be used, for example, in a music information retrieval system for indexing a collection of music documents so that it can be searched more rapidly. It can also be used in software tools for music analysis and composition and in a music transcription system or model of music cognition for discovering grouping structure, metrical structure and voice-leading structure. In most approaches to pattern discovery in music, the data is assumed to be in the form of strings. However, string-based methods become inefficient when one is interested in finding highly embellished occurrences of a query pattern or searching for polyphonic patterns in polyphonic music. These limitations can be avoided by representing the music as a set of points in a multidimensional Euclidean space. This point-set pattern matching approach allows the maximal repeated patterns in a passage of polyphonic music to be discovered in quadratic time and all occurrences of these patterns to be found in cubic time. More recently, Clifford et al. [1] have shown that the best match for a query point set within a text point set of size n can be found in O(n log n) time by incorporating randomised projection, uniform hashing and FFT into the point-set pattern matching approach. Also, by using appropriate heuristics for selecting compact maximal repeated patterns with many non-overlapping occurrences, the point-set pattern discovery algorithms described here can be adapted for data compression. Moreover, the efficient encodings generated when this compression algorithm is run on music data seem to resemble the motivic-thematic analyses produced by human experts. Keywords. Content-based music information retrieval, point-set pattern matching 1