With the growth in digital representations of music, and of music stored in these representations, it is increasingly attractive to search collections of music. One mode of search is by similarity, but, for music, similarity search presents several difficulties: in particular, deciding what part of the music is likely to be perceived as the theme by a listener, and deciding whether two pieces of music with different sequences of notes represent the same theme. In this paper we propose a three-stage framework for matching pieces of music. We use the framework to compare a range of techniques for determining whether two pieces of music are similar, by experimentally testing their ability to retrieve different transcriptions of the same piece of music from a large collection of MIDI files. These experiments show that different comparison techniques differ widely in their effectiveness; and

Large volumes of music are available online, represented in performance formats such as MIDI and, increasingly, in abstract notation such as SMDL. Many types of user would find it valuable to search collections of music via queries representing music fragments, but such searching requires a reliable technique for identifying whether a provided fragment occurs within a piece of music. The problem of matching fragments to music is made difficult by the psychology of music perception, because literal matching may have little relation to perceived melodic similarity, and by the interactions between the multiple parts of typical pieces of music. In this paper we analyse the properties of music, music perception, and music database users, and use the analysis to propose alternative techniques for extracting monophonic melodies from polyphonic music; we believe that such melodies can subsequently be used for matching of queries to data. We report on experiments with music listeners, which rank our proposed techniques for extracting melodies.

Standard MIDI files contain data that can be considered as a symbolic representation of music (a digital score), and most of them are structured as a number of tracks. One of them usually contains the melodic line of the piece, while the other tracks contain accompaniment music. The goal of this work is to identify the track that contains the melody using statistical properties of the musical content and pattern recognition techniques. Finding that track is very useful for a number of applications, like speeding up melody matching when searching in MIDI databases or motif extraction, among others. First, a set of descriptors from each track of the target file are extracted. These descriptors are the input to a random forest classifier that assigns the probability of being a melodic line to each track. The track with the highest probability is selected as the one containing the melodic line of that MIDI file. Promising results have been obtained testing a number of databases of different music styles.

We have explored methods for music information retrieval for polyphonic music stored in the MIDI format. These methods use a query, expressed as a series of notes that are intended to represent a melody or theme, to identify similar pieces. Our work has shown that a three-phase architecture is appropriate for this task, in which the first phase is melody extraction, the second is standardisation, and the third is query-to-melody matching. We have investigated and systematically compared algorithms for each of these phases. To ensure that our results are robust, we have applied methodologies that are derived from text information retrieval: we developed test collections and compared different ways of acquiring test queries and relevance judgements. In this paper we review this program of work, compare to other approaches to music information retrieval, and identify outstanding issues.

The objective of this work is to find the melodic line in MIDI files. Usually, the melodic line is stored in a single track, while the other tracks contain the accompaniment. The detection of the track that contains the melodic line can be very useful for a number of applications, such as melody matching when searching in MIDI databases. The system was developed using WEKA. First, a set of descriptors from each track of the target melody is extracted. These descriptors are the input to a random forest classifier that assigns a probability of being a melodic line to each track. The tracks with a probability under a given threhold are filtered out, and the one with the highest probability is selected as the melodic line of that melody. Promising results were obtained testing different MIDI databases.

Abstract — Listeners are thought to be capable of perceiving multiple voices in music. Adopting a perceptual view of musical ‘voice ’ that corresponds to the notion of auditory stream, a computational model is developed that splits a musical score (symbolic musical data) into different voices. A single ‘voice ’ may consist of more than one synchronous notes that are perceived as belonging to the same auditory stream; in this sense, the proposed algorithm, may separate a given musical work into fewer voices than the maximum number of notes in the greatest chord (e.g. a piece consisting of four or more concurrent notes may be separated simply into melody and accompaniment). This is paramount, not only in the study of auditory streaming per se, but also for developing MIR systems that enable pattern recognition and extraction within musically pertinent ‘voices ’ (e.g. melodic lines). The algorithm is tested qualitatively and quantitatively against a small dataset that acts as groundtruth. I.

computational perspectives The notions of ‘voice’, as well as, homophony and polyphony, are thought to be well understood by musicians. Listeners are thought to be capable of perceiving multiple ‘voices ’ in music. However, there exists no systematic theory that describes how ‘voices’ can be identified, especially, when polyphonic and homophonic elements are mixed together. The paper presents different views of what ‘voice ’ means and how the problem of voice separation can be described systematically, with a view to understanding the problem better and developing a systematic perceptually-based description of the cognitive task of segregating ‘voices’ in music. Vague (or even contradicting) treatments of this issue will be presented. Elements of a systematic theory that can be implemented as a computer program are also proposed. WHAT IS A VOICE? It appears that the term ‘voice ’ has different meanings for different research fields (traditional musicology, music cognition, computational musicology). Recently, there have been a number of attempts (e.g. Temperley, 2001;