Melodic Grouping in Music Information Retrieval: New Methods and Applications

Pearce, Marcus T.; Müllensiefen, Daniel; Wiggins, Geraínt A.

doi:10.1007/978-3-642-11674-2_16

Cited by 29 publications

(31 citation statements)

References 53 publications

(86 reference statements)

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“…These results are broadly comparable with those of Pearce et al (2010a) who found that the models ranked in the same order when tested against the expert segmentations of a musicologist on 1705 German folk songs. However, the performance of all models was worse in that study, perhaps owing to the fact that here models were tested on the optimal cluster of participants.…”

Section: Discussionsupporting

confidence: 85%

The Role of Expectation and Probabilistic Learning in Auditory Boundary Perception: A Model Comparison

2010

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Introduction Grouping and boundary perception are central to the understanding and modelling of core tasks in many areas of cognitive science. They are fundamental processes in, for example, natural language processing (eg speech segmentation and word discoveryöBrent 1999b; Jusczyk 1997), motor learning (eg identifying behavioural episodes öReynolds et al 2007; Newtson 1973), memory storage and retrieval (eg chunkingöKurby and Zacks 2007) and visual perception (eg analysing spatial organisation öMarr 1982). Our focus in this paper is on the perception and cognition of music (Krumhansl 1990; Temperley 2001), where the process by which the human perceptual system groups sequential musical elements together is one of the most fundamental issues. In particular, we examine the grouping of musical elements into contiguous segments that occur sequentially in time or, to put it another way, the identification of boundaries between the final element of one segment and the first element of the subsequent one. This way of structuring a musical surface is usually referred to as grouping (Lerdahl and Jackendoff 1983) or segmentation (Cambouropoulos 2006). We distinguish this kind of perceptual aggregation of auditory elements from the integration, or fusion, of auditory elements that occur simultaneously in time and also from the segregation of parallel auditory streams (Bregman 1990). In musical terms, the kinds of groups we consider correspond with motifs, phrases, sections and other aspects of musical form. We use the term grouping structure to refer to a piece of music structured in this way. It is taken that, just as speech is perceptually segmented into phonemes, and then words which subsequently provide the building blocks for the perception of phrases and complete utterances (Brent 1999b; Jusczyk 1997), motifs or phrases in music are identified by listeners, stored in memory and made available for inclusion in higher-level structural groups (Lerdahl and Jackendoff 1983; Peretz 1989; Tan et al 1981). The lowlevel organisation of the musical surface into groups allows the use of these primitive perceptual units in more complex structural processing and may alleviate demands on memory.

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Section: Discussionsupporting

confidence: 85%

The Role of Expectation and Probabilistic Learning in Auditory Boundary Perception: A Model Comparison

2010

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“…In several studies (Bruderer, 2008;Neve & Orio, 2005;Nooijer, Wiering, Volk, & Tabachneck-Schijf, 2008;Pearce, Müllensiefen & Wiggins, 2010), the automatic melodic segmentation literature is reviewed in detail. As suggested by Pearce et al (2010) melodic segmentation studies can be classified into three main groups: 'Music-theoretic Approaches', 'Psychological Studies' and 'Computational Models'.…”

Section: The Automatic Segmentation Literaturementioning

confidence: 99%

“…As suggested by Pearce et al (2010) melodic segmentation studies can be classified into three main groups: 'Music-theoretic Approaches', 'Psychological Studies' and 'Computational Models'. To avoid repeating the detailed surveys in the abovementioned papers, we will limit ourselves here to studies closely linked with our objective and the methodology that falls into the 'Computational models' category.…”

Section: The Automatic Segmentation Literaturementioning

confidence: 99%

“…Lartillot and Ayari (2009) also presented a comparison of the Temporal Gestalt Units and LBDM. Pearce et al (2010) compares the above-mentioned algorithms with their own proposal, IDyOM (Information Dynamics Of Music).…”

Section: The Automatic Segmentation Literaturementioning

confidence: 99%

“…This approach uses the information content defined in MacKay (2003) as a feature (within a multiple viewpoint representation (Conklin, 2001) including pitch and duration features) and a combination of several text compression and statistical language modelling (mainly n-grams) methods. In addition to comparing the mentioned algorithm, Pearce et al (2010) tested a hybrid approach that combines features of different algorithms and concluded that the hybrid approach has better performance than any of the components. Previously, Bod (2001) used a data-driven approach where the probabilities of phrase boundaries had been computed from pitch-class, interval and duration distributions from a database of segment boundaries in the Essen folk song collection.…”

Section: The Automatic Segmentation Literaturementioning

confidence: 99%

See 2 more Smart Citations

Usul and Makam driven automatic melodic segmentation for Turkish music

Bozkurt

Karaosmanoğlu

Karaçali

et al. 2014

Journal of New Music Research

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Automatic melodic segmentation is a topic studied extensively, aiming at developing systems that perform grouping of musical events. Here, we consider the problem of automatic segmentation via supervised learning from a dataset containing segmentation labels of an expert. We present a statistical classification-based segmentation system developed specifically for Turkish makam music. The proposed system uses two novel features, a makam-based and an usul-based feature, together with features commonly used in literature. The makam-based feature is defined as the probability of a note to appear at the phrase boundary, computed from the distributions of boundaries with respect to the piece's makam pitches. Likewise, the usul-based feature is computed from the distributions of boundaries with respect to beats in the rhythmic cycle, usul of the piece. Several experimental setups using different feature groups are designed to test the contribution of the proposed features on three datasets. The results show that the new features carry complementary information to existing features in the literature within the Turkish makam music segmentation context and that the inclusion of new features resulted in statistically significant performance improvement.

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Symbolic Segmentation: A Corpus-Based Analysis of Melodic Phrases

Rodríguez-López

Volk

2014

Lecture Notes in Computer Science

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Abstract. Gestalt-based segmentation models constitute the current state of the art in automatic segmentation of melodies. These models commonly assume that segment boundary perception is mainly triggered by local discontinuities, i.e. by abrupt changes in pitch and/or duration of neighbouring note events. This paper presents a statistical study of melodic phrase segments to test this assumption. The study focuses on the analysis of pitch and duration in the neighbourhood of annotated phrase boundaries. Our analysis reveals stability in duration information (specifically inter-onset-intervals), and variability in pitch information (specifically chromatic intervals). We conclude that pitch discontinuities, while important for segmentation, can not be addressed as a local, idiom independent phenomenon.

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Melodic Grouping in Music Information Retrieval: New Methods and Applications

Cited by 29 publications

References 53 publications

The Role of Expectation and Probabilistic Learning in Auditory Boundary Perception: A Model Comparison

The Role of Expectation and Probabilistic Learning in Auditory Boundary Perception: A Model Comparison

Usul and Makam driven automatic melodic segmentation for Turkish music

Symbolic Segmentation: A Corpus-Based Analysis of Melodic Phrases

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