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

Rosenzweig, Sebastian; Scherbaum, Frank; Shugliashvili, David; Arifi-Müller, Vlora; Müller, Meinard

doi:10.5334/tismir.44

Cited by 19 publications

(17 citation statements)

References 19 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In some approaches, standard spectral and temporal features such as spectral centroid, spectral flux, and zero-crossing rate were used (Knight et al, 2011). In others, features aimed at capturing certain aspects of music perception were hand-designed using either musical intuition or expert knowledge (Nakano et al, 2006;Abeßer et al, 2014b;Romani Picas et al, 2015;Li et al, 2015). For instance, Nakano et al (2006) used features measuring pitch stability and vibrato as inputs to a simple classifier to rate the quality of vocal performances.…”

Section: Assessment Methodsmentioning

confidence: 99%

“…Tools for performance assessment evaluate one or more performance parameters typically related to the accuracy of the performance in terms of pitch and timing (Wu et al, 2016;Vidwans et al, 2017;Pati et al, 2018;Luo, 2015), or quality of sound (timbre) (Knight et al, 2011;Romani Picas et al, 2015;Narang and Rao, 2017). In building an assessment tool, the choice of parameters may depend on the proficiency level of the performer being assessed.…”

Section: Assessment Parametersmentioning

confidence: 99%

“…Tools may simply classify a performance as 'good' or 'bad' (Knight et al, 2011;Nakano et al, 2006), or grade it on a scale, e.g., from 1 to 10 . Systems may provide fine-grained note-by-note assessments (Romani Picas et al, 2015;Schramm et al, 2015) or analyze entire performances and report a single assessment score (Nakano et al, 2006;Pati et al, 2018;Huang and Lerch, 2019).…”

Section: Assessment Parametersmentioning

confidence: 99%

See 2 more Smart Citations

An Interdisciplinary Review of Music Performance Analysis

Lerch¹,

Arthur²,

Pati³

2020

Transactions of the International Society for Music Information Retrieval

View full text Add to dashboard Cite

A musical performance renders an acoustic realization of a musical score or other representation of a composition. Different performances of the same composition may vary in terms of performance parameters such as timing or dynamics, and these variations may have a major impact on how a listener perceives the music. The analysis of music performance has traditionally been a peripheral topic for the MIR research community, where often a single audio recording is used as representative of a musical work. This paper surveys the field of Music Performance Analysis (MPA) from several perspectives including the measurement of performance parameters, the relation of those parameters to the actions and intentions of a performer or perceptual effects on a listener, and finally the assessment of musical performance. This paper also discusses MPA as it relates to MIR, pointing out opportunities for collaboration and future research in both areas.

show abstract

Section: Assessment Methodsmentioning

confidence: 99%

Section: Assessment Parametersmentioning

confidence: 99%

Section: Assessment Parametersmentioning

confidence: 99%

See 1 more Smart Citation

An Interdisciplinary Review of Music Performance Analysis

Lerch¹,

Arthur²,

Pati³

2020

Transactions of the International Society for Music Information Retrieval

View full text Add to dashboard Cite

show abstract

“…The first larger music dataset compiled specifically for research purposes is the RWC music database (Goto, 2004). More recent examples are the multitrack dataset MedleyDB (Bittner et al, 2014) or the Erkomaishvili dataset for ethnomusicological research (Rosenzweig et al, 2020). 1 Serra (2014) discussed the role of datasets for the MIR community.…”

Section: Datasetsmentioning

confidence: 99%

MTD: A Multimodal Dataset of Musical Themes for MIR Research

Zalkow

Balke

Arifi-Müller

et al. 2020

Transactions of the International Society for Music Information Retrieval

Self Cite

View full text Add to dashboard Cite

Musical themes are essential elements in Western classical music. In this paper, we present the Musical Theme Dataset (MTD), a multimodal dataset inspired by "A Dictionary of Musical Themes" by Barlow and Morgenstern from 1948. For a subset of 2067 themes of the printed book, we created several digital representations of the musical themes. Beyond graphical sheet music, we provide symbolic music encodings, audio snippets of music recordings, alignments between the symbolic and audio representations, as well as detailed metadata on the composer, work, recording, and musical characteristics of the themes. In addition to the data, we also make several parsers and web-based interfaces available to access and explore the different modalities and their relations through visualizations and sonifications. These interfaces also include computational tools, bridging the gap between the original dictionary and music information retrieval (MIR) research. The dataset is of relevance for various subfields and tasks in MIR, such as cross-modal music retrieval, music alignment, optical music recognition, music transcription, and computational musicology.

show abstract

“…The dataset includes 216 songs recorded with video cameras, portable stereo recorders as well as multiple close-up microphones attached to each of the singers. Furthermore, the Erkomaishvlili Dataset is a publicly available corpus based on historic tape recordings of three-voice traditional Georgian songs performed by the former master chanter Artem Erkomaishvili (Rosenzweig et al, 2020). The dataset includes digital sheet music, F0-and onset annotations of the three voices as well as annotations of the overdubbingbased recording structure.…”

Section: Prior Workmentioning

confidence: 99%

Dagstuhl ChoirSet: A Multitrack Dataset for MIR Research on Choral Singing

Rosenzweig

Cuesta

Weiß

et al. 2020

Transactions of the International Society for Music Information Retrieval

Self Cite

View full text Add to dashboard Cite

Choral singing is a central part of musical cultures across the world, yet many facets of this widespread form of polyphonic singing are still to be explored. Music information retrieval (MIR) research on choral singing benefits from multitrack recordings of the individual singing voices. However, there exist only few publicly available multitrack datasets on polyphonic singing. In this paper, we present Dagstuhl ChoirSet (DCS), a multitrack dataset of a cappella choral music designed to support MIR research on choral singing. The dataset includes recordings of an amateur vocal ensemble performing two choir pieces in full choir and quartet settings. The audio data was recorded during an MIR seminar at Schloss Dagstuhl using different close-up microphones to capture the individual singers' voices. In this article, we give detailed insights into all stages of creating DCS: recording process, data preparation, generation of annotations as well as development of suitable interfaces for publicly accessing and reusing the data. Furthermore, we demonstrate the potential of the dataset for MIR research by discussing case studies on choral intonation assessment and multiple fundamental frequency (F0) estimation.

show abstract

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

Cited by 19 publications

References 19 publications

An Interdisciplinary Review of Music Performance Analysis

An Interdisciplinary Review of Music Performance Analysis

MTD: A Multimodal Dataset of Musical Themes for MIR Research

Dagstuhl ChoirSet: A Multitrack Dataset for MIR Research on Choral Singing

Contact Info

Product

Resources

About