Abdallah, S

Alencar-Brayner, A

Benetos, E

Cottrell, S

Dykes, J

Gold, NE

Kachkaev, A

Mahey, M

Tidhar, D

Tovell, A

Weyde, T

Wolff, D

UCL Discovery

AUTOMATIC TRANSCRIPTION AND PITCH ANALYSIS OF THEBRITISH LIBRARYWORLD & TRADITIONAL MUSIC COLLECTIONSSamer Abdallah1, Aquiles Alencar-Brayner2, Emmanouil Benetos5, Stephen Cottrell4Jason Dykes3, Nicolas Gold1, Alexander Kachkaev3, Mahendra Mahey2, Dan Tidhar4,Adam Tovell2, Tillman Weyde3, Daniel Wolff31 Department of Computer Science, University College London, 2 The British Library,3 Department of Computer Science, City University London,4 Department of Music, City University London5 Centre for Digital Music, Queen Mary University of Londondml-owner@city.ac.uk - http://dml.city.ac.uk1. INTRODUCTIONMusic research, particularly in fields like systematic mu-sicology, ethnomusicology, or music psychology has de-veloped as “data oriented empirical research” (Parncutt,2007), which benefits from the development of comput-ing methods and infrastructure. In ethnomusicology therehas been a recent growing interest in computational meth-ods and their application to audio data collections (Go´mezet al., 2013), (Canazza et al., 2010), and in the degreeto which such methods may reveal insights into musicalpractice which may not be evident from the participant-observation paradigms that have otherwise characterisedthe discipline. For technological and legal reasons researchin this area was previously limited to small datasets, butthis is changing in part due to the contribution of researchprojects such as CompMusic 1 and cultural preservationprojects such as Europeana Sounds 2 .The authors of this paper collaborate in the UK AHRC-funded project Digital Music Lab - Analysing Big MusicData (DML) 3 to develop methods and technologies tosupport the use of Big Data in musicology (Weyde et al.,2014). As part of the project, we have developed soft-ware/hardware infrastructure for exploring and analysinglarge-scale audio collections, aiming to assist research insystematic and empirical (ethno)musicology.A major partner of the DML project is The British Li-brary (BL), which holds several million audio recordingsin its Sound Archive, spanning oral history interviews, en-vironmental & nature sounds, as well as over 3 millionrecordings from classical, popular, world and traditionalmusic (of which approximately 10% are digitized). A por-tion of these audio recordings (approx. 60k) are currentlyavailable for online streaming 4 . Through the DML project,Authors in alphabetical order. This work was supported by theUK AHRC-funded project ‘Digital Music Lab - Analysing Big MusicData’, grant no. AH/L01016X/1 and the UK AHRC funded project‘An Integrated Audio-Symbolic Model of Music Similarity’, grant no.AH/M002454/1. EB is supported by a Royal Academy of EngineeringResearch Fellowship, grant no. RF/128.1 http://compmusic.upf.edu/2 http://www.europeanasounds.eu/3 http://dml.city.ac.uk4 http://sounds.bl.uk/a computing server was installed on-site at the BL, en-abling storage and analysis for a collection of over 29krecordings from its ‘World & Traditional Music’ corpus.In this paper we present the collection of World & Tra-ditional music that was curated and analysed as part of theDML project; also presented will be methods for automatictranscription and pitch analysis that were applied to record-ings from that collection, and which were used as a basisfor creating an integrated tool/interface enabling musico-logical enquiries and research in large music collections.2. COLLECTIONThe BL Sound Archive holds one of the world’s largestcollections of recordings variously described as traditional,folk or ‘world’ music. The dataset drawn from this collec-tion for the DML project consists of 29,198 audio record-ings. It covers a large collection (8k) of English, Irish, andScottish folk songs; 1300 recordings from Oceania; 12krecordings from Africa (covering West and South Africa,as well as large collections from Uganda and Sudan); over6k recordings from Asia (mostly from Nepal, India, andPakistan); 1100 recordings from the Middle East; and asmall collection of 47 recordings from the Americas (com-prising music of indigenous Indians from Colombia). Italso contains collections of wax cylinders recorded by pi-oneering fieldworkers, as well as more recent recordingsmade on a range of formats as part of ethnographic re-search. Recording dates span from 1898 (from the ethno-graphic wax cylinders collection) up to 2014. It is worthnoting that several of these recordings contain segments ofspeech as well as music, or even overlapped speech andmusic. Information on the five largest collections can beseen in Table 1.The recordings are also accompanied with rich meta-data in METS/XML format. Information present in themetadata includes: title, collection ID, description, per-former, recording engineer, recording date and temporalinformation (e.g. Easter), language, geographic informa-tion, as well as audio file information (duration, samplingrate, resolution, URL for publicly available recordings).Title # recordings DatesBob & Jacqueline Patten EnglishFolk Music Collection 6333 1953-2002John Howson English, Irish &Scottish Folk Music Collection 3498 1930-1999Reg Hall English, Irish &Scottish Folk Music & Customs Collection 3195 1949-1996Klaus Wachsmann Uganda Collection 1538 1949-1954Peter Cooke Uganda Collection 1277 1964-1997Table 1: The five largest collections from the BL World &Traditional Music dataset used for the DML project.3. AUTOMATIC TRANSCRIPTIONAs a first step towards musicological analysis for largeaudio collections, we use automatic music transcription(AMT) technology to convert a recording into machine-readable music notation (Klapuri & Davy, 2006). The vastmajority of research in AMT technology is however lim-ited to Western/Eurogenetic music, where an audio record-ing is converted into a MIDI-like representation.For this work we used the model of Benetos & Dixon(2012), which can support the estimation of multiple pitches(along with onsets, offsets, and velocities) in a scale equalto the resolution of the input time/frequency representa-tion (in our case, 20 cent resolution given as input a log-frequency spectrogram of 60 bins/octave), and ranked firstin the MIREX 2013 evaluations for Multiple-F0 estimationand Note Tracking 5 . This method is also publicly avail-able as a VAMP plugin 6 , which can be used in conjunctionwith software such as Sonic Visualiser 7 .Using the aforementioned method, the output is a prob-ability distribution of pitches in 20 cent resolution overtime: P (f, t) (f corresponds to pitch and t to the time in-dex). This is post-processed and converted into a binaryrepresentation of note events, with a corresponding onsettime, offset time, pitch, and velocity value. Apart fromhigh-level musicological applications the resulting transcrip-tion can serve as a way to store or visualise the content of amusic recording (an example can be shown in Fig. 1). Theresulting transcription files, along with other low-level fea-tures, can be downloaded for individual files through thesemantic web server of the DML project 8 .4. PITCH HISTOGRAMSInformation extracted from the automatic transcriptions canbe used to generate information on the pitch content of arecording, or a collection of recordings. Given a transcrip-tion P (f, t), a pitch histogram for that recording can begenerated by: P (f) =∑t P (f, t). The above process canbe extended for any number of recordings, for visualisingthe pitch content of collections. As part of the DML inter-face for browsing/interacting with large music collections(Kachkaev et al., 2015), pitch histograms for audio collec-tions can be computed on-demand. The DML visualisation5 http://www.music-ir.org/mirex/wiki/MIREX HOME6 https://code.soundsoftware.ac.uk/projects/silvet/files7 http://sonicvisualiser.org/8 http://mirg.city.ac.uk/cp/Figure 1: The spectrogram for a segment of ‘The Black-bird’ from the Bob & Jacqueline Patten Collection. Over-layed as white boxes are detected pitches.Figure 2: The aggregated pitch histogram for 3,022 Ugan-dan recordings, as shown in the DML VIS interface.interface is available online 9 . An example pitch histogramtaken from that interface is shown in Fig. 2.This allows the identification of the most frequently en-countered pitches within a given collection, which in turnmay suggest pitch hierarchies prevailing within the mu-sic culture and, potentially, commonly used scales. Cross-referencing with ethnographically-grounded research is likelyto be necessary to confirm the cultural validity of the re-sults. Nevertheless, the method allows large-scale exam-ination of particular music cultures, comparison betweencollections to observe similar patterns of pitch distribu-tion and, possibly, the automatic identification of otherwiseunattributed recordings according to the pitch distributionfound in them.5. DISCUSSIONIn this paper, we presented work carried out by the DMLproject on analysing over 29k recordings from the BL Worldand Traditional Music collection. This involved producingautomatic transcriptions at a high pitch resolution and cre-ating pitch histograms both for individual recordings andcollections. These are supported by a semantic web serverfor downloading features for individual recordings and avisualisation interface for browsing through collections.Questions remain about the use of additional high-levelfeatures for describing folk and traditional music record-ings, given culturally-specific aspects such as modes, struc-ture, and instrumentation. We believe that pitch analysis ata high-frequency resolution is the first step towards that9 http://dml.city.ac.uk/vis/goal. Another issue relates to the integration of record-ings from copyright-restricted collections; the recent UKIntellectual Property Bill has enabled researchers to usecopyright-restricted data for research purposes, but com-putations still need to be done on-site. We believe this ap-proach will enable musicians and musicologists unprece-dented access to recordings, overcoming such restrictions.Finally, the problem of segmenting the recordings into speechand music parts is currently being investigated through theMuSpeak project 10 .6. REFERENCESBenetos, E. & Dixon, S. (2012). A shift-invariant latent variablemodel for automatic music transcription. Computer MusicJournal, 36(4), 81–94.Canazza, S., Camurri, A., & Fujinaga, I. (2010). Special sec-tion: Ethnic music audio documents: From the preserva-tion to the fruition. Signal Processing, 90(4), 977–1334.Go´mez, E., Herrera, P., & Go´mez-Martin, F. e. (2013). Specialissue: Computational ethnomusicology. Journal of NewMusic Research, 42(2).Kachkaev, A., Dykes, J., Abdallah, S., Barthet, M., Benetos,E., Cottrell, S., Dixon, S., Gold, N., Hargreaves, S., Tid-har, D., Wolff, D., & Weyde, T. (2015). Small multiplesfor big data a framework for comparison using open webtechnologies with music collections. In IEEE InformationVisualization Conference 2015, submitted.Klapuri, A. & Davy, M. (Eds.). (2006). Signal ProcessingMethods for Music Transcription. New York: Springer-Verlag.Parncutt, R. (2007). Systematic musicology and the historyand future of western musical scholarship. Journal ofInterdisciplinary Music Studies, 1, 1–32.Weyde, T., Cottrell, S., Dykes, J., Benetos, E., Wolff, D., Tid-har, D., Gold, N., Abdallah, S., Plumbley, M., Dixon, S.,Barthet, M., Mahey, M., Tovell, A., & Alencar-Brayner,A. (2014). Big data for musicology. In Digital Librariesfor Musicology Workshop, (pp. 85–87)., London, UK.10 http://mirg.city.ac.uk/muspeak

Automatic Transcription and Pitch Analysis of the British Library World and Traditional Music Collections

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Automatic Transcription and Pitch Analysis of the British Library World and Traditional Music Collections

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