Computational Ethnomusicology: perspectives and challenges

Herrera, Perfecto; Gómez-Martín, Francisco

doi:10.1080/09298215.2013.818038

Cited by 26 publications

(11 citation statements)

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“…In the field of MIR, large-scale comparative studies have focused mainly on Eurogenetic music [ 4 , 5 ], where Eurogenetic defines music styles of mainly Western traditions for example classical and popular repertoires. The study of non-Eurogenetic music using computational tools falls under the emerging field of Computational Ethnomusicology [ 6 , 7 ]. While several research projects have focused on the development of MIR tools for world music analysis [ 8 – 12 ], no study, to the best of our knowledge, has applied such computational methods in the analysis of a large world music corpus.…”

Section: Introductionmentioning

confidence: 99%

A computational study on outliers in world music

2017

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The comparative analysis of world music cultures has been the focus of several ethnomusicological studies in the last century. With the advances of Music Information Retrieval and the increased accessibility of sound archives, large-scale analysis of world music with computational tools is today feasible. We investigate music similarity in a corpus of 8200 recordings of folk and traditional music from 137 countries around the world. In particular, we aim to identify music recordings that are most distinct compared to the rest of our corpus. We refer to these recordings as ‘outliers’. We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation. Our findings suggest that Botswana is the country with the most distinct recordings in the corpus and China is the country with the most distinct recordings when considering spatial correlation. Our analysis includes a comparison of musical attributes and styles that contribute to the ‘uniqueness’ of the music of each country.

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Section: Introductionmentioning

confidence: 99%

A computational study on outliers in world music

2017

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“…Computational ethnomusicology is a rather young and still evolving field of research (Tzanetakis et al, 2007;Gómez et al, 2013;Tzanetakis, 2014). Its potential depends strongly on the existence of data collections which on the one hand are musically relevant, and on the other hand are of sufficient quality for the application of computational tools.…”

Section: Applications For Mir and Musicologymentioning

confidence: 99%

Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology

Rosenzweig¹,

Scherbaum²,

Shugliashvili³

et al. 2020

Transactions of the International Society for Music Information Retrieval

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The analysis of recorded audio material using computational methods has received increased attention in ethnomusicological research. We present a curated dataset of traditional Georgian vocal music for computational musicology. The corpus is based on historic tape recordings of three-voice Georgian songs performed by the the former master chanter Artem Erkomaishvili. In this article, we give a detailed overview of the audio material, transcriptions, and annotations contained in the dataset. Beyond its importance for ethnomusicological research, this carefully organized and annotated corpus constitutes a challenging scenario for music information retrieval tasks such as fundamental frequency estimation, onset detection, and score-to-audio alignment. The corpus is publicly available and accessible through score-following web-players.

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“…With ongoing digitization of ethnomusicological archives, the demand for methods for automatic extraction of metadata from folk music field recordings is growing [22][23][24]. While the aforementioned transcription methods can be applied to folk music recordings, we show in this paper that their accuracy can be improved by considering folk music specifics.…”

Section: Introductionmentioning

confidence: 95%

Transcription of Polyphonic Vocal Music with a Repetitive Melodic Structure

Bohak¹,

Marolt²

2016

J. Audio Eng. Soc.

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This paper presents a novel method for transcription of folk music that exploits its specifics to improve transcription accuracy. In contrast to most commercial music, folk music recordings may contain various inaccuracies as they are usually performed by amateur musicians and recorded in the field. If we use standard approaches for transcription, these inaccuracies are reflected in erroneous pitch estimates. On the other hand, the structure of western folk music is usually simple as songs are often composed of repeated melodic parts. In our approach we make use of these repetitions to increase transcription robustness and improve its accuracy. The proposed method fuses three sources of information: (1) frame-based multiple F0 estimates, (2) song structure, and (3) pitch drift estimates. It first selects a representative segment of the analyzed song and aligns all the other segments to it considering temporal as well as frequency deviations. Information from all segments is summarized and used in a two-layer probabilistic model based on explicit duration HMMs, to segment frame-based information into notes. The method is evaluated with state-of-the-art transcription methods where we show that significant improvement in accuracy can be achieved.

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Computational Ethnomusicology: perspectives and challenges

Cited by 26 publications

References 0 publications

A computational study on outliers in world music

A computational study on outliers in world music

Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology

Transcription of Polyphonic Vocal Music with a Repetitive Melodic Structure

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