SuMoTED: An intuitive edit distance between rooted unordered uniquely-labelled trees

McVicar, Matt; Sach, Benjamin; Mesnage, Cédric; Lijffijt, Jefrey; Spyropoulou, Eirini; Bie, Tijl De

doi:10.1016/j.patrec.2016.04.012

Cited by 11 publications

(12 citation statements)

References 18 publications

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“…The lack of an exact task specification, the differences in the annotators' experiences, musical background, skill level, and instrumental preference, or the use of different annotation tools are some of the possible causes of disagreement between annotators (Balke et al, 2016;Salamon, Gómez, Ellis, & Richard, 2014;Salamon & Urbano, 2012). Annotator disagreement has previously been studied in the contexts of genre classification (Lippens, Martens, & De Mulder, 2004;McVicar et al, 2016;Seyerlehner, Widmer, & Knees, 2011), audio music similarity (Flexer, 2014;Flexer & Grill, 2016;Jones, Downie, & Ehmann, 2007), music structure analysis (Nieto, Farbood, Jehan, & Bello, 2014;Paulus & Klapuri, 2009;Smith, Burgoyne, Fujinaga, De Roure, & Downie, 2011), melody extraction (Balke et al, 2016;Bosch & Gómez, 2014), musical tempo extraction and beat tracking (McKinney, Moelants, Davies, & Klapuri, 2007), ratings of guitar tabs (Macrae & Dixon, 2011) and human harmony annotations (Ni et al, 2013). Nevertheless, the extent of human disagreement and their impact on these tasks is commonly not taken into account when creating new music information retrieval methods.…”

Section: Related Work In Analysis Of Human Judgements In Music Informmentioning

confidence: 99%

Annotator subjectivity in harmony annotations of popular music

Koops

Haas²,

Burgoyne

et al. 2019

Journal of New Music Research

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Reference annotation datasets containing harmony annotations are at the core of a wide range of studies in music information retrieval (MIR) and related fields. The majority of these datasets contain single reference annotations describing the harmony of each piece. Nevertheless, studies showing differences among annotators in many other MIR tasks make the notion of a single 'ground-truth' reference annotation a tenuous one. In this paper, we introduce and analyse the Chordify Annotator Subjectivity Dataset (CASD) containing chord labels for 50 songs from 4 expert annotators in order to gain a better understanding of the differences between annotators in their chord label choice. Our analysis reveals that annotators use distinct chord-label vocabularies, with low chord-label overlap across all annotators. Between annotators, we find only 73 percent overlap on average for the traditional major-minor vocabulary and 54 percent overlap for the most complex chord labels. A factor analysis reveals the relative importance of triads, sevenths, inversions and other musical factors for each annotator on their choice of chord labels and reported difficulty of the songs. Our results further substantiate the existence of a harmonic 'subjectivity ceiling': an upper bound for evaluations in computational harmony research. Current state-of-the-art chord-estimation systems perform beyond this subjectivity ceiling by about 10 percent. This suggests that current ACE algorithms are powerful enough to tune themselves to particular annotators' idiosyncrasies. Overall, our results show that annotator subjectivity is an important factor in harmonic transcriptions, which should inform future studies into harmony perception and computational models of harmony.

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Section: Related Work In Analysis Of Human Judgements In Music Informmentioning

confidence: 99%

Annotator subjectivity in harmony annotations of popular music

Koops

Haas²,

Burgoyne

et al. 2019

Journal of New Music Research

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“…A difference in a vertex with many descendants should then contribute more to a distance measure than one in a vertex with few descendants, since it affects more clonal populations. Thus, a tumor evolution distance measure that simply counts the differences between trees (often referred to as a tree edit distance, as proposed by [28,30], and others) does not address the impact any given label change may have. A distance measure should assign different weights to disagreements in different locations in order to appropriately address the relationship between topology and mutation labeling.…”

Section: Tumor Evolution Distancesmentioning

confidence: 99%

Distance Measures for Tumor Evolutionary Trees

DiNardo

Tomlinson

Ritz

et al. 2019

Preprint

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In recent years, there has been increased interest in studying cancer by using algorithmic methods to infer the evolutionary tree underlying a tumor's developmental history. Quantitative measures that compare such trees are then vital to benchmarking these algorithmic tree inference methods, understanding the structure of the space of possible trees for a given dataset, and clustering together similar trees in order to evaluate inheritance patterns. However, few appropriate distance measures exist, and those that do exist have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and how the mutations that label that topology are inherited. Here we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to two simulated and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. Implementations of CASet and DISC are available at: https://bitbucket.org/oesperlab/stereodist.

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“…Other distance metrics like tree edit (Bille, 2005) and graph edit distances (Gao et al, 2010) were also explored. A graph similarity approach comparing taxonomies by defining edit distance on taxonomies was proposed (McVicar et al, 2016). However, the input taxonomies were assumed to be trees, which is not generally true (Bordea et al, 2016;Kozareva and Hovy, 2010;Velardi et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

“…• Similarity measures for tree structures or undirected graphs (Koutra et al, 2016;McVicar et al, 2016) are unable to model the structural complexity (like multiple parents) within DAGs or consider the semantic coherence of parent-child links that characterizes hierarchical knowledge sources. Further, none of the above approaches are scalable in practice for comparing huge taxonomies, and also do not provide tunability to characterize the degree of similarity for different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Automated assessment of knowledge hierarchy evolution: comparing directed acyclic graphs

et al. 2018

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Automated construction of knowledge hierarchies from huge data corpora is gaining increasing attention in recent years, in order to tackle the infeasibility of manually extracting and semantically linking millions of concepts. As a knowledge hierarchy evolves with these automated techniques, there is a need for measures to assess its temporal evolution, quantifying the similarities between different versions and identifying the relative growth of different subgraphs in the knowledge hierarchy. In this paper, we focus on measures that leverage structural properties of the knowledge hierarchy graph to assess the temporal changes. We propose a principled and scalable similarity measure, based on Katz similarity between concept nodes, for comparing different versions of a knowledge hierarchy, modeled as a generic Directed Acyclic Graph. We present theoretical analysis to depict that the proposed measure accurately captures the salient properties of taxonomic hierarchies, assesses changes in the ordering of nodes, along with the logical subsumption of relationships among concepts. We also present a linear time variant of the measure, and show that our measures, unlike previous approaches, are tunable to cater to diverse application needs. We further show that our measure provides interpretability, thereby identifying the key structural and logical difference in the hierarchies. Experiments on a real DBpedia and biological knowledge hierarchy showcase that our measures accurately capture structural similarity, while providing enhanced scalability and tunability. Also, we demonstrate that the the temporal evolution of different subgraphs in this knowledge hierarchy, as captured purely by our structural measure, corresponds well with the known disruptions in the related subject areas.

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SuMoTED: An intuitive edit distance between rooted unordered uniquely-labelled trees

Cited by 11 publications

References 18 publications

Annotator subjectivity in harmony annotations of popular music

Annotator subjectivity in harmony annotations of popular music

Distance Measures for Tumor Evolutionary Trees

Automated assessment of knowledge hierarchy evolution: comparing directed acyclic graphs

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