Towards Automated Processing of Folk Song Recordings

Folk music is closely related to the musical culture of a specific nation or region. Even though folk songs have been passed down mainly by oral tradition, most musicologists study the relation between folk songs on the basis of symbolic music descriptions, which are obtained by transcribing recorded tunes into a score-like representation. Due to the complexity of audio recordings, once having the transcriptions, the original recorded tunes are often no longer used in the actual folk song research even though they still may contain valuable information. In this paper, we present various techniques for making audio recordings more easily accessible for music researchers. In particular, we show how one can use synchronization techniques to automatically segment and annotate the recorded songs. The processed audio recordings can then be made accessible along with a symbolic transcript by means of suitable visualization, searching, and navigation interfaces to assist folk song researchers to conduct large scale investigations comprising the audio material

Segmentation of Folk Songs with a Probabilistic Model

Structure is an important aspect of music. Musical structure can be recognized in different musical modalities such as rhythm, melody, harmony or lyrics and plays a crucial role in our appreciation of music. In recent years many researchers have addressed the problem of music segmentation, mainly for popular and classical music. Some of the more recent approaches are Mauch et al. (2009), Foote (2000), Serr`a et al. (2012) and McFee & Ellis (2014). Last three are included in the music structure analysis framework MSAF Nieto & Bello (2015). None of the mentioned approaches however, addresses the specifics of folk music. While commercial music is performed by professional performers and recorded with professional equipment in suitable recording conditions, this is usually not true for folk music field recordings, which are recorded in everyday environments and contain music performed by amateur performers. Thus, recordings may contain high levels of background noise, equipment induced noise (e.g. hum) and reverb, as well as performer mistakes such as inaccurate pitches, false starts, forgotten melody/lyrics or pitch drift throughout the performance. One of the most recent approaches which addressed folk music specifics was presented by M¨uller et al. (2013). The approach was designed for solo singing and was evaluated on a collection of Dutch folk music by Muller et al. (2010). In our paper, we present a novel folk music segmentation method, which also addresses folk music specifics and is designed to work well with a variety of ensemble types (solo, choir, instrumental and mixtures)

Proceedings of the 6th International Workshop on Folk Music Analysis, 15-17 June, 2016

The Folk Music Analysis Workshop brings together computational music analysis and ethnomusicology. Both symbolic and audio representations of music are considered, with a broad range of scientific approaches being applied (signal processing, graph theory, deep learning). The workshop features a range of interesting talks from international researchers in areas such as Indian classical music, Iranian singing, Ottoman-Turkish Makam music scores, Flamenco singing, Irish traditional music, Georgian traditional music and Dutch folk songs. Invited guest speakers were Anja Volk, Utrecht University and Peter Browne, Technological University Dublin

Kompozicionalni hierarhični model za pridobivanje informacij iz glasbe

In recent years, deep architectures, most commonly based on neural networks, have advanced the state of the art in many research areas. Due to the popularity and the success of deep neural-networks, other deep architectures, including compositional models, have been put aside from mainstream research. This dissertation presents the compositional hierarchical model as a novel deep architecture for music processing. Our main motivation was to develop and explore an alternative non-neural deep architecture for music processing which would be transparent, meaning that the encoded knowledge would be interpretable, trained in an unsupervised manner and on small datasets, and useful as a feature extractor for classification tasks, as well as a transparent model for unsupervised pattern discovery. We base our work on compositional models, as compositionality is inherent in music. The proposed compositional hierarchical model learns a multi-layer hierarchical representation of the analyzed music signals in an unsupervised manner. It provides transparent insights into the learned concepts and their structure. It can be used as a feature extractor---its output can be used for classification tasks using existing machine learning techniques. Moreover, the model\u27s transparency enables an interpretation of the learned concepts, so the model can be used for analysis (exploration of the learned hierarchy) or discovery-oriented (inferring the hierarchy) tasks, which is difficult with most neural network based architectures. The proposed model uses relative coding of the learned concepts, which eliminates the need for large annotated training datasets that are essential in deep architectures with a large number of parameters. Relative coding contributes to slim models, which are fast to execute and have low memory requirements. The model also incorporates several biologically-inspired mechanisms that are modeled according to the mechanisms that exists at the lower levels of human perception (eg~ lateral inhibition in the human ear) and that significantly affect perception. The proposed model is evaluated on several music information retrieval tasks and its results are compared to the current state of the art. The dissertation is structured as follows. In the first chapter we present the motivation for the development of the new model. In the second chapter we elaborate on the related work in music information retrieval and review other compositional and transparent models. Chapter three introduces a thorough description of the proposed model. The model structure, its learning and inference methods are explained, as well as the incorporated biologically-inspired mechanisms. The model is then applied to several different music domains, which are divided according to the type of input data. In this we follow the timeline of the development and the implementation of the model. In chapter four, we present the model\u27s application to audio recordings, specifically for two tasks: automatic chord estimation and multiple fundamental frequency estimation. In chapter five, we present the model\u27s application to symbolic music representations. We concentrate on pattern discovery, emphasizing the model\u27s ability to tackle such problems. We also evaluate the model as a feature generator for tune family classification. Finally, in chapter six, we show the latest progress in developing the model for representing rhythm and show that it exhibits a high degree of robustness in extracting high-level rhythmic structures from music signals. We conclude the dissertation by summarizing our work and the results, elaborating on forthcoming work in the development of the model and its future applications.S porastom globokih arhitektur, ki temeljijo na nevronskih mrežah, so se v zadnjem času bistveno izboljšali rezultati pri reševanju problemov na več področjih. Zaradi popularnosti in uspešnosti teh globokih pristopov, temelječih na nevronskih mrežah, so bili drugi, predvsem kompozicionalni pristopi, odmaknjeni od središča pozornosti raziskav. V pričujoči disertaciji se posvečamo vprašanju, ali je mogoče razviti globoko arhitekturo, ki bo presegla obstoječe probleme globokih arhitektur. S tem namenom se vračamo h kompozicionalnim modelom in predstavimo kompozicionalni hierarhični model kot alternativno globoko arhitekturo, ki bo imela naslednje značilnosti: transparentnost, ki omogoča enostavno razlago naučenih konceptov, nenadzorovano učenje in zmožnost učenja na majhnih podatkovnih bazah, uporabnost modela kot izluščevalca značilk, kot tudi zmožnost uporabe transparentnosti modela za odkrivanje vzorcev. Naše delo temelji na kompozicionalnih modelih, ki so v glasbi intuitivni. Predlagani kompozicionalni hierarhični model je zmožen nenadzorovanega učenja večnivojske predstavitve glasbenega vhoda. Model omogoča pregled naučenih konceptov skozi transparentne strukture. Lahko ga uporabimo kot generator značilk -- izhod modela lahko uporabimo za klasifikacijo z drugimi pristopi strojnega učenja. Hkrati pa lahko transparentnost predlaganega modela uporabimo za analizo (raziskovanje naučene hierarhije) pri odkrivanju vzorcev, kar je težko izvedljivo z ostalimi pristopi, ki temeljijo na nevronskih mrežah. Relativno kodiranje konceptov v samem modelu pripomore k precej manjšim modelom in posledično zmanjšuje potrebo po velikih podatkovnih zbirkah, potrebnih za učenje modela. Z vpeljavo biološko navdahnjenih mehanizmov želimo model še bolj približati človeškemu načinu zaznave. Za nekatere mehanizme, na primer inhibicijo, vemo, da so v človeški percepciji prisotni na nižjih nivojih v ušesu in bistveno vplivajo na način zaznave. V modelu uvedemo prve korake k takšnemu načinu procesiranja proti končnemu cilju izdelave modela, ki popolnoma odraža človeško percepcijo. V prvem poglavju disertacije predstavimo motivacijo za razvoj novega modela. V drugem poglavju se posvetimo dosedanjim objavljenim dosežkom na tem področju. V nadaljnjih poglavjih se osredotočimo na sam model. Sprva opišemo teoretično zasnovo modela in način učenja ter delovanje biološko-navdahnjenih mehanizmov. V naslednjem koraku model apliciramo na več različnih glasbenih domen, ki so razdeljene glede na tip vhodnih podatkov. Pri tem sledimo časovnici razvoja in implementacijam modela tekom doktorskega študija. Najprej predstavimo aplikacijo modela za časovno-frekvenčne signale, na katerem model preizkusimo za dve opravili: avtomatsko ocenjevanje harmonij in avtomatsko transkripcijo osnovnih frekvenc. V petem poglavju predstavimo drug način aplikacije modela, tokrat na simbolne vhodne podatke, ki predstavljajo glasbeni zapis. Pri tem pristopu se osredotočamo na odkrivanje vzorcev, s čimer poudarimo zmožnost modela za reševanje tovrstnih problemov, ki je ostalim pristopom še nedosegljivo. Model prav tako evalviramo v vlogi generatorja značilk. Pri tem ga evalviramo na problemu melodične podobnosti pesmi in razvrščanja v variantne tipe. Nazadnje, v šestem poglavju, pokažemo zadnji dosežek razvoja modela, ki ga apliciramo na problem razumevanja ritma v glasbi. Prilagojeni model analiziramo in pokažemo njegovo zmožnost učenja različnih ritmičnih oblik in visoko stopnjo robustnosti pri izluščevanju visokonivojskih struktur v ritmu. V zaključkih disertacije povzamemo vloženo delo in rezultate ter nakažemo nadaljnje korake za razvoj modela v prihodnosti

The Global Jukebox: a public database of performing arts and culture

Standardized cross-cultural databases of the arts are critical to a balanced scientific understanding of the performing arts, and their role in other domains of human society. This paper introduces the Global Jukebox as a resource for comparative and cross-cultural study of the performing arts and culture. The Global Jukebox adds an extensive and detailed global database of the performing arts that enlarges our understanding of human cultural diversity. Initially prototyped by Alan Lomax in the 1980s, its core is the Cantometrics dataset, encompassing standardized codings on 37 aspects of musical style for 5,776 traditional songs from 1,026 societies. The Cantometrics dataset has been cleaned and checked for reliability and accuracy, and includes a full coding guide with audio training examples (https://theglobaljukebox.org/?songsofearth). Also being released are seven additional datasets coding and describing instrumentation, conversation, popular music, vowel and consonant placement, breath management, social factors, and societies. For the first time, all digitized Global Jukebox data are being made available in open-access, downloadable format (https://github.com/theglobaljukebox), linked with streaming audio recordings (theglobaljukebox.org) to the maximum extent allowed while respecting copyright and the wishes of culture-bearers. The data are cross-indexed with the Database of Peoples, Languages, and Cultures (D-PLACE) to allow researchers to test hypotheses about worldwide coevolution of aesthetic patterns and traditions. As an example, we analyze the global relationship between song style and societal complexity, showing that they are robustly related, in contrast to previous critiques claiming that these proposed relationships were an artifact of autocorrelation (though causal mechanisms remain unresolved).Introduction Background The Global Jukebox and its data 1. The data 1.1. Datasets of the Global Jukebox 1.2. Comparison with other cross cultural datasets 1.3. Coded performance variables: Selection and reliability 1.4. Performance data sources 1.5. Selection of audio examples 1.6. Availability of audio recordings 1.7. Performance metadata 2. Sampling societies and links to other datasets 2.1. Sampling societies 2.2. Links to other cross-cultural datasets 3. Data curation, cleaning, and validation 4. Coding reliability 5. Is song style correlated with social complexity? An example of hypothesis testing using the Global Jukebox 5.1. Hypothesis testing with the Global Jukebox datasets 5.2. Methods 5.3 Results of reanalysis 5.4 Discussion 6. Ethics, rights and consent 7. Conclusio

Signal processing methods for beat tracking, music segmentation, and audio retrieval

The goal of music information retrieval (MIR) is to develop novel strategies and techniques for organizing, exploring, accessing, and understanding music data in an efficient manner. The conversion of waveform-based audio data into semantically meaningful feature representations by the use of digital signal processing techniques is at the center of MIR and constitutes a difficult field of research because of the complexity and diversity of music signals. In this thesis, we introduce novel signal processing methods that allow for extracting musically meaningful information from audio signals. As main strategy, we exploit musical knowledge about the signals\u27 properties to derive feature representations that show a significant degree of robustness against musical variations but still exhibit a high musical expressiveness. We apply this general strategy to three different areas of MIR: Firstly, we introduce novel techniques for extracting tempo and beat information, where we particularly consider challenging music with changing tempo and soft note onsets. Secondly, we present novel algorithms for the automated segmentation and analysis of folk song field recordings, where one has to cope with significant fluctuations in intonation and tempo as well as recording artifacts. Thirdly, we explore a cross-version approach to content-based music retrieval based on the query-by-example paradigm. In all three areas, we focus on application scenarios where strong musical variations make the extraction of musically meaningful information a challenging task.Ziel der automatisierten Musikverarbeitung ist die Entwicklung neuer Strategien und Techniken zur effizienten Organisation großer Musiksammlungen. Ein Schwerpunkt liegt in der Anwendung von Methoden der digitalen Signalverarbeitung zur Umwandlung von Audiosignalen in musikalisch aussagekräftige Merkmalsdarstellungen. Große Herausforderungen bei dieser Aufgabe ergeben sich aus der Komplexität und Vielschichtigkeit der Musiksignale. In dieser Arbeit werden neuartige Methoden vorgestellt, mit deren Hilfe musikalisch interpretierbare Information aus Musiksignalen extrahiert werden kann. Hierbei besteht eine grundlegende Strategie in der konsequenten Ausnutzung musikalischen Vorwissens, um Merkmalsdarstellungen abzuleiten die zum einen ein hohes Maß an Robustheit gegenüber musikalischen Variationen und zum anderen eine hohe musikalische Ausdruckskraft besitzen. Dieses Prinzip wenden wir auf drei verschieden Aufgabenstellungen an: Erstens stellen wir neuartige Ansätze zur Extraktion von Tempo- und Beat-Information aus Audiosignalen vor, die insbesondere auf anspruchsvolle Szenarien mit wechselnden Tempo und weichen Notenanfängen angewendet werden. Zweitens tragen wir mit neuartigen Algorithmen zur Segmentierung und Analyse von Feldaufnahmen von Volksliedern unter Vorliegen großer Intonationsschwankungen bei. Drittens entwickeln wir effiziente Verfahren zur inhaltsbasierten Suche in großen Datenbeständen mit dem Ziel, verschiedene Interpretationen eines Musikstückes zu detektieren. In allen betrachteten Szenarien richten wir unser Augenmerk insbesondere auf die Fälle in denen auf Grund erheblicher musikalischer Variationen die Extraktion musikalisch aussagekräftiger Informationen eine große Herausforderung darstellt

09051 Abstracts Collection -- Knowledge representation for intelligent music processing

From the twenty-fifth to the thirtieth of January, 2009, the Dagstuhl Seminar 09051 on ``Knowledge representation for intelligent music processing\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Centre for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations and demos given during the seminar as well as plenary presentations, reports of workshop discussions, results and ideas are put together in this paper. The first section describes the seminar topics and goals in general, followed by plenary `stimulus\u27 papers, followed by reports and abstracts arranged by workshop followed finally by some concluding materials providing views of both the seminar itself and also forward to the longer-term goals of the discipline. Links to extended abstracts, full papers and supporting materials are provided, if available. The organisers thank David Lewis for editing these proceedings

Music 2025 : The Music Data Dilemma: issues facing the music industry in improving data management

© Crown Copyright 2019Music 2025ʼ investigates the infrastructure issues around the management of digital data in an increasingly stream driven industry. The findings are the culmination of over 50 interviews with high profile music industry representatives across the sector and reflects key issues as well as areas of consensus and contrasting views. The findings reveal whilst there are great examples of data initiatives across the value chain, there are opportunities to improve efficiency and interoperability

Agreement among human and annotated transcriptions of global songs

Cross-cultural musical analysis requires standardized symbolic representation of sounds such as score notation. However, transcription into notation is usually conducted manually by ear, which is time-consuming and subjective. Our aim is to evaluate the reliability of existing methods for transcribing songs from diverse societies. We had 3 experts independently transcribe a sample of 32 excerpts of traditional monophonic songs from around the world (half a cappella, half with instrumental accompaniment). 16 songs also had pre-existing transcriptions created by 3 different experts. We compared these human transcriptions against one another and against 10 automatic music transcription algorithms. We found that human transcriptions can be sufficiently reliable (~90% agreement, κ ~.7), but current automated methods are not (<60% agreement, κ <.4). No automated method clearly outperformed others, in contrast to our predictions. These results suggest that improving automated methods for cross-cultural music transcription is critical for diversifying MIR