Supervised descriptive pattern discovery in Native American music

Neubarth, Kerstin; Shanahan, Daniel; Conklin, Darrell

doi:10.1080/09298215.2017.1353637

Cited by 9 publications

(10 citation statements)

References 34 publications

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“…The study presented here has adopted Densmore's own content descriptors, thus allowing to compare results of the computational analysis with Densmore's observations. Digital encoding of the Densmore collection [56] offers opportunities to complement Densmore's features by computational feature extraction [57] and sequential pattern mining [35], both to systematically analyse aspects occasionally mentioned in Densmore's narrative analyses but not captured in her features (e.g., linking melodic and duration features) and to add further music content descriptors (e.g., aspects of melodic contour or melodic motifs [20,58]). Computational features applied to symbolically encoded music data focus on structural features and generally do not reflect aspects of performance or context.…”

Section: Discussionmentioning

confidence: 99%

“…Melodic with harmonic framework 1,2,6,10,11,17,20,21,26,27,29,30,32,40,45,57,68,71,74,75,77,79,82,89,91,101,115,121,122,124,143,147,155,165,166 Compared to classical tasks in collection-level music analysis such as clustering [22][23][24][25][26], classification [27][28][29][30][31] and pattern matching and discovery [32][33][34][35][36][37], outlier detection has attracted less attention in music computing research [38]. Outlier detection has been mainly used for cleaning datasets from noisy or erroneous examples in order to improve performance of subsequent classification, music recommendation or sound...…”

Section: Structure Serial Number Of Songsmentioning

confidence: 99%

“…In addition to transcriptions of songs and contextual information, Densmore published systematic analyses of many songs and suggested common characteristics of tribal repertoires and interesting features of individual songs (see example in Figure 1). Densmore's quantitative comparison of songs by different tribes (Figure 1 bottom) can productively be seen as an instance of contrast pattern mining [20]. Within the repertoire of a tribe, songs may be distinguished by features which are surprisingly rare, unusual or peculiar (Figure 1 top right), that is, they deviate from common characteristics of the tribe's songs and thus represent outliers [21].…”

Section: Introductionmentioning

confidence: 99%

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Identification and Description of Outliers in the Densmore Collection of Native American Music

Neubarth

Conklin

2019

Applied Sciences

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This paper presents a method for outlier detection in structured music corpora. Given a music collection organised into groups of songs, the method discovers contrast patterns which are significantly infrequent in a group. Discovered patterns identify and describe outlier songs exhibiting unusual properties in the context of their group. Applied to the collection of Native American music collated by Frances Densmore (1867–1957) during fieldwork among several North American tribes, and employing Densmore’s music content descriptors, the proposed method successfully discovers a concise set of patterns and outliers, many of which correspond closely to observations about tribal repertoires and songs presented by Densmore.

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Section: Discussionmentioning

confidence: 99%

Section: Structure Serial Number Of Songsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification and Description of Outliers in the Densmore Collection of Native American Music

Neubarth

Conklin

2019

Applied Sciences

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“…Advances in music data mining and the creation of annotated music corpora [1][2][3] have supported a renewed interest in comparative music analysis [4,5]. Analyses of class-labeled music datasets have explored a range of data mining paradigms, including descriptive methods such as subgroup discovery and emerging pattern mining [6][7][8][9][10][11][12] and predictive methods such as decision tree and classification rule induction [13][14][15][16][17][18]. These studies generally focus on identifying discriminant properties, which distinguish different classes.…”

Section: Introductionmentioning

confidence: 99%

Mining Characteristic Patterns for Comparative Music Corpus Analysis

Neubarth

Conklin

2020

Applied Sciences

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A core issue of computational pattern mining is the identification of interesting patterns. When mining music corpora organized into classes of songs, patterns may be of interest because they are characteristic, describing prevalent properties of classes, or because they are discriminant, capturing distinctive properties of classes. Existing work in computational music corpus analysis has focused on discovering discriminant patterns. This paper studies characteristic patterns, investigating the behavior of different pattern interestingness measures in balancing coverage and discriminability of classes in top k pattern mining and in individual top ranked patterns. Characteristic pattern mining is applied to the collection of Native American music by Frances Densmore, and the discovered patterns are shown to be supported by Densmore’s own analyses.

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“…For example, in [9] the influence of repeated patterns was considered for Dutch folk song classification. Computational techniques were also used in [10] to discover patterns in Native American music and identify musical differences between indigenous groups.…”

Section: Introductionmentioning

confidence: 99%

Genre Classification for Brazilian Music using Independent and Discriminant Features

Filho

Borges-Jr.

Fernandes-Jr.

2018

JCIS

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Digital music files are largely available both online and in private local collections. These databases may comprise hundreds or thousands of files, which in some cases may not carry tagged information about their content, making the search for the desired audio files very time consuming. An important task in this context is to organize the available database according to the prevailing musical genre. The purpose of this work is to develop an automatic music genre classification system able to identify international music genres (i.e. pop, rock, classic, soul, funk) and also typical Brazilian rhythms such as Samba, Forró and Brazilian Popular Music. The proposed signal processing chain comprises two stages. Initially, audio signal features are computed and their relevance for music genre identification estimated. Independent component analysis is applied to reduce mutual redundancy among the audio attributes. In the following, different classifiers based on neural networks and support vector machines are applied for music genre identification. The proposed system efficiency is evaluated using an experimental dataset.

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Supervised descriptive pattern discovery in Native American music

Cited by 9 publications

References 34 publications

Identification and Description of Outliers in the Densmore Collection of Native American Music

Identification and Description of Outliers in the Densmore Collection of Native American Music

Mining Characteristic Patterns for Comparative Music Corpus Analysis

Genre Classification for Brazilian Music using Independent and Discriminant Features

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