Abstract not found

In this paper I introduce a new algorithm called ps13 that reliably computes the correct pitch names (e.g., C#4, B#5 etc.) of the notes in a passage of tonal music, when given only the onset-time and MIDI note number of each note in the passage. ps13 correctly predicts the pitch names of 99.81% of the notes in a test corpus containing 41544 notes and consisting of all the pieces in the first book of J. S. Bach's Das Wohltemperirte Klavier (BWV 846--869). Three previous algorithms (those of Cambouropoulos (1996, 1998, 2002), Longuet-Higgins (1987) and Temperley (2001)) were run on the same corpus of 41544 notes. On this corpus, Cambouropoulos's algorithm made 2599 mistakes, LonguetHiggins 's algorithm made 265 mistakes and Temperley's algorithm made 122 mistakes. As ps13 made only 81 mistakes on the same corpus, this suggests that ps13 may be more robust than previous algorithms that attempt to perform the same task.

Abstract not found

an important factor for musical segmentation, there have been relatively few systematic attempts to describe exactly how it affects grouping processes. The main problem is that musical parallelism itself is difficult to formalize. In this study, a computational model that extracts melodic patterns from a given melodic surface is presented. Following the assumption that the beginning and ending points of “significant ” repeating musical patterns influence the segmentation of a musical surface, the discovered patterns are used as ameans to determine probable segmentation points of the melody. “Significant ” patterns are defined primarily in terms of frequency of occurrence and length of pattern. The special status of nonoverlapping, immediately repeating patterns is examined. All the discovered patterns merge into a single “pattern ” segmentation profile that signifies points in the surface most likely to be perceived as points of segmentation. The effectiveness of the proposed melodic representations and algorithms is tested against a series of melodic surfaces illustrating both strengths and weaknesses of the approach.

This paper presents a representation for melodic segment classes and applies it to music data mining. Melody is modeled as a sequence of segments, each segment being a sequence of notes. These segments are assigned to classes through a knowledge representation scheme which allows the flexible construction of abstract views of the music surface. The representation is applied to sequential pattern discovery and to the statistical modeling of musical style.

We present MSM, a new maximal subset matching algorithm, for MIR at score level with polyphonic texts and patterns. First, we argue that the problem MSM and its ancestors, the SIA family of algorithms, solve is 3SUM-hard and, therefore, subquadratic solutions must involve approximation. MSM is such a solution; we describe it, and argue that, at O(n log n) time with no large constants, it is orders of magnitude more time-efficient than its closest competitor. We also evaluate MSM’s performance on a retrieval problem addressed by the OMRAS project, and show that it outperforms OMRAS on this task by a considerable margin.

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

In this paper, we study transposition-invariant content-based music retrieval (TI-CBMR) in polyphonic music. The aim is to find transposition invariant occurrences of a given query pattern called a template, in a database of polyphonic music called a dataset. Between the musical events (represented by points) in the dataset that have been found to match points in the template, there may be any finite number of other intervening musical events. For this task, we introduce an algorithm, called SIA(M)ESE, which is based on the SIA pattern induction algorithm [11]. The algorithm is first introduced in abstract mathematical form, then we show how we have implemented it using sophisticated techniques and equipped it with appropriate heuristics. The resulting efficient algorithm has a worst case running time of O(mn log(mn)), where m and n are the size of the template and the dataset, respectively. Moreover, the algorithm is generalizable to any arbitrary, multidimensional translation invariant pattern matching problem, where the events considered can be represented by points in a multidimensional dataset.

This Music Information Retrieval (MIR) study investigates the use of n-grams and textual In-formation Retrieval (IR) approaches for the retrieval and access of polyphonic music data. IR, synonymous with text IR, implies the task of retrieving documents or texts with information content that is relevant to a user’s information need. With music retrieval, the use of n-grams has largely been confined to monophonic musical sequences. The few studies that have investigated its use with polyphonic music collections typically reduce a polyphonic file into a monophonic sequence for n-gram construction. Tech-niques for full-music indexing of polyphonic music data with n-grams are investigated. A method to obtain n-grams from polyphonic music data is introduced. The information con-tent of ‘musical n-grams ’ is extended to include rhythmic information in addition to intervallic information. For this, ratios of onset times between two adjacent pairs of pitch events are used. To encode ‘musical n-grams ’ to obtain ‘musical words ’ for indexing, a function that maps interval classes to text characters is formulated, and ranges of ratio bins are defined. These encoding approaches enable encoding of the pitch and rhythm information at vari-

Recent research in music retrieval has shown that a combinatorial approach to the problem could be fruitful. Three distinguishing requirements of this particular problem are (a) approximate searching permitting missing, extra, and distorted notes, (b) transposition invariance, to allow matching a sequence that appears in a different scale, and (c) handling polyphonic music. These combined requirements make up a complex combinatorial problem that is currently under research. On the other hand, bit-parallelism has proved a powerful practical tool for combinatorial pattern matching, both flexible and efficient. In this paper we use bit-parallelism to search for several transpositions at the same time, and obtain speedups of O(w = log k) over the classical algorithms, where the computer word has w bits and k is the error threshold allowed in the match. Although not the best solution for the easier approximation measures, we show that our technique can be adapted to complex cases where no competing method exists, and that are the most interesting in terms of music retrieval.