Human listeners are able to recognize structure in music through the perception of repetition and other relationships within a piece of music. This work aims to automate the task of music analysis. Music is “explained ” in terms of embedded relationships, especially repetition of segments or phrases. The steps in this process are the transcription of audio into a representation with a similarity or distance metric, the search for similar segments, forming clusters of similar segments, and explaining music in terms of these clusters. Several transcription methods are considered: monophonic pitch estimation, chroma (spectral) representation, and polyphonic transcription followed by harmonic analysis. Also, several algorithms that search for similar segments are described. These techniques can be used to perform an analysis of musical structure, as illustrated by examples. 1.

Query-by-humming systems search a database of music for good matches to a sung, hummed, or whistled melody. Errors in transcription and variations in pitch and tempo can cause substantial mismatch between queries and targets. Thus, algorithms for measuring melodic similarity in query-by-humming systems should be robust. We compare several variations of search algorithms in an effort to improve search precision. In particular, we describe a new frame-based algorithm that significantly outperforms note-by-note algorithms in tests using sung queries and a database of MIDI-encoded music.

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.

We argue for a memory-based approach to music analysis which works with concrete musical experiences rather than with abstract rules or principles. New pieces of music are analyzed by combining fragments from structures of previously encountered pieces. The occurrence-frequencies of the fragments are used to determine the preferred analysis of a piece. We test some instances of this approach against a set of 1,000 manually annotated folksongs from the Essen Folksong Collection, yielding up to 85.9 % phrase accuracy. A qualitative analysis of our results indicates that there are grouping phenomena that challenge the commonly accepted Gestalt principles of proximity, similarity and parallelism. These grouping phenomena can neither be explained by other musical factors, such as meter and harmony. We argue that music perception may be much more memory-based than previously assumed. 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.

The automated discovery of recurrent patterns in music is a fundamental task in computational music analysis. This paper describes a new method for discovering patterns in the vertical and horizontal dimensions of polyphonic music. A formal representation of music objects is used to structure the musical surface, and several ideas for viewing pieces as successions of vertical structures are examined. A knowledge representation method is used to view pieces as sequences of relationships between music objects, and a pattern discovery algorithm is applied using this view of the Bach chorale harmonizations to find significant recurrent patterns. The method finds a small set of vertical patterns that occur in a large number of pieces in the corpus. Most of these patterns represent specific voice leading formulae within cadences.

Music is often described in terms of the structure of repeated phrases. For example, many songs have the form AABA, where each letter represents an instance of a phrase. This research aims to construct descriptions or explanations of music in this form, using only audio recordings as input. A system of programs is described that transcribes the melody of a recording, identifies similar segments, clusters these segments to form patterns, and then constructs an explanation of the music in terms of these patterns. Additional work using spectral information rather than melodic transcription is also described. Examples of successful machine "listening" and music analysis are presented.

Melodic similarity is an important concept for music databases, musicological studies, and interactive music systems. Dynamic programming is commonly used to compare melodies, often with a distance function based on pitch differences measured in semitones. This approach computes an "edit distance" as a measure of melodic dissimilarity. The problem can also be viewed in probabilistic terms: What is the probability that a melody is a "mutation" of another melody, given a table of mutation probabilities? We explain this approach and demonstrate how it can be used to search a database of melodies. Our experiments show that the probabilistic model performs better than a typical "edit distance" comparison.

This paper discusses the use of statistical models for the problem of musical style imitation. Statistical models are created from extant pieces in a stylistic corpus, and have an objective goal which is to accurately classify new pieces. The process of music generation is equated with the problem of sampling from a statistical model. In principle there is no need to make the classical distinction between analytic and synthetic models of music. This paper presents several methods for sampling from an analytic statistical model, and proposes a new approach that maintains the intra opus pattern repetition within an extant piece. A major component of creativity is the adaptation of extant art works, and this is also an efficient way to sample pieces from complex statistical models.

Abstract. Here we consider computational problems on ffi-approximate and(ffi; fl)-approximate string matching. These are two new notions of approximate matching that arise naturally in applications of computer assisted music analy-sis. We present fast, efficient and practical algorithms for these two notions of approximate string matching.