Cultural Context-Aware Models and IT Applications for the Exploitation of Musical Heritage

Information engineering has always expanded its scope by inspiring innovation in different scientific disciplines. In particular, in the last sixty years, music and engineering have forged a strong connection in the discipline known as “Sound and Music Computing”. Musical heritage is a paradigmatic case that includes several multi-faceted cultural artefacts and traditions. Several issues arise from the analog-digital transfer of cultural objects, concerning their creation, preservation, access, analysis and experiencing. The keystone is the relationship of these digitized cultural objects with their carrier and cultural context. The terms “cultural context” and “cultural context awareness” are delineated, alongside the concepts of contextual information and metadata. Since they maintain the integrity of the object, its meaning and cultural context, their role is critical. This thesis explores three main case studies concerning historical audio recordings and ancient musical instruments, aiming to delineate models to preserve, analyze, access and experience the digital versions of these three prominent examples of musical heritage. The first case study concerns analog magnetic tapes, and, in particular, tape music, a particular experimental music born in the second half of the XX century. This case study has relevant implications from the musicology, philology and archivists’ points of view, since the carrier has a paramount role and the tight connection with its content can easily break during the digitization process or the access phase. With the aim to help musicologists and audio technicians in their work, several tools based on Artificial Intelligence are evaluated in tasks such as the discontinuity detection and equalization recognition. By considering the peculiarities of tape music, the philological problem of stemmatics in digitized audio documents is tackled: an algorithm based on phylogenetic techniques is proposed and assessed, confirming the suitability of these techniques for this task. Then, a methodology for a historically faithful access to digitized tape music recordings is introduced, by considering contextual information and its relationship with the carrier and the replay device. Based on this methodology, an Android app which virtualizes a tape recorder is presented, together with its assessment. Furthermore, two web applications are proposed to faithfully experience digitized 78 rpm discs and magnetic tape recordings, respectively. Finally, a prototype of web application for musicological analysis is presented. This aims to concentrate relevant part of the knowledge acquired in this work into a single interface. The second case study is a corpus of Arab-Andalusian music, suitable for computational research, which opens new opportunities to musicological studies by applying data-driven analysis. The description of the corpus is based on the five criteria formalized in the CompMusic project of the University Pompeu Fabra of Barcelona: purpose, coverage, completeness, quality and re-usability. Four Jupyter notebooks were developed with the aim to provide a useful tool for computational musicologists for analyzing and using data and metadata of such corpus. The third case study concerns an exceptional historical musical instrument: an ancient Pan flute exhibited at the Museum of Archaeological Sciences and Art of the University of Padova. The final objective was the creation of a multimedia installation to valorize this precious artifact and to allow visitors to interact with the archaeological find and to learn its history. The case study provided the opportunity to study a methodology suitable for the valorization of this ancient musical instrument, but also extendible to other artifacts or museum collections. Both the methodology and the resulting multimedia installation are presented, followed by the assessment carried out by a multidisciplinary group of experts

Finding Tori: Self-supervised Learning for Analyzing Korean Folk Song

In this paper, we introduce a computational analysis of the field recording dataset of approximately 700 hours of Korean folk songs, which were recorded around 1980-90s. Because most of the songs were sung by non-expert musicians without accompaniment, the dataset provides several challenges. To address this challenge, we utilized self-supervised learning with convolutional neural network based on pitch contour, then analyzed how the musical concept of tori, a classification system defined by a specific scale, ornamental notes, and an idiomatic melodic contour, is captured by the model. The experimental result shows that our approach can better capture the characteristics of tori compared to traditional pitch histograms. Using our approaches, we have examined how musical discussions proposed in existing academia manifest in the actual field recordings of Korean folk songs.Comment: Accepted at 24th International Society for Music Information Retrieval Conference (ISMIR 2023

A Dataset for Greek Traditional and Folk Music: Lyra

Studying under-represented music traditions under the MIR scope is crucial, not only for developing novel analysis tools, but also for unveiling musical functions that might prove useful in studying world musics. This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data. The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre, among others. The content has been collected from a Greek documentary series that is available online, where academics present music traditions of Greece with live music and dance performance during the show, along with discussions about social, cultural and musicological aspects of the presented music. Therefore, this procedure has resulted in a significant wealth of descriptions regarding a variety of aspects, such as musical genre, places of origin and musical instruments. In addition, the audio recordings were performed under strict production-level specifications, in terms of recording equipment, leading to very clean and homogeneous audio content. In this work, apart from presenting the dataset in detail, we propose a baseline deep-learning classification approach to recognize the involved musicological attributes. The dataset, the baseline classification methods and the models are provided in public repositories. Future directions for further refining the dataset are also discussed

Statistical Machine Translation from Arab Vocal Improvisation to Instrumental Melodic Accompaniment

International audienceVocal improvisation is an essential practice in Arab music. The interactivity between the singer and the instru-mentalist(s) is a main feature of this deep-rooted musical form. As part of the interactivity, the instrumentalist re-capitulates, or translates, each vocal sentence upon its completion. In this paper, we present our own parallel corpus of instrumentally accompanied Arab vocal improvisation. The initial size of the corpus is 2779 parallel sentences. We discuss the process of building this corpus as well as the choice of data representation. We also present some statistics about the corpus. Then we present initial experiments on applying statistical machine translation to propose an automatic instrumental accompaniment to Arab vocal improvisation. The results with this small corpus, in comparison to classical machine translation of natural languages, are very promising: a BLEU of 24.62 from Vocal to instrumental and 24.07 from instrumental to vocal

Between Occitania and Al-Andalus: Reconsidering the Emergence of Troubadour Melody Through Algorithmic Analysis

How the musical and poetic traditions of the troubadours arose remains unknown, despite a century of scholarship that has attempted to account for their seemingly ex nihilo appearance in late twelfth-century Europe. Scholarly debate was particularly intense during the first half of the twentieth century and revolved around two competing theories: the Andalusi theory, which linked the troubadours to the poetic-musical traditions of medieval Muslim Iberia (also known by its Arabic name al-Andalus), and the Aquitanian theory, which argued that the troubadours were rooted in the folk and sacred traditions of the Aquitanian region. Since the 1980s, interest in the topic has mostly focused on new evidence that supports the Andalusi theory. However, because of the paucity of musical sources from al-Andalus and the first generation of troubadours, all scholarship on the topic has based itself upon isolated case studies of the lyric texts, extending conclusions drawn from textual analysis into the musical realm. This dissertation, by contrast, focuses upon the emergence of troubadour melody, using algorithmic analysis to perform a large comparative study of three repertoires: (1) the complete corpus of extant and complete troubadour melodies, (2) a sample of 158 melodies from contemporary unwritten Andalusi music of Morocco, and (3) 380 melodies from the sacred repertoires of Saint Martial of Limoges. I have used two of the most popular algorithms in bioinformatics – the Pairwise Sequence Alignment algorithm (PSA) and the Multiple Sequence Alignment (MSA) – to compare the melodies, and study common musical idioms. I found three melodic pairs between troubadour and contemporary Moroccan Andalusi melodies, thus demonstrating the existence of musical exchange between Occitania and al-Andalus. In addition, based on common musical idioms (such as common ways of beginning a melody and shared motives) found among all three traditions, I posit that the boundaries between the sacred and the secular were fluid, as both musical spheres drew on a pool of well-known unwritten melodies. Thus, I argue that the generic boundaries through which sacred and secular repertoires are theorized today are anachronistic. Finally, I reconstruct two proto-melodies of the troubadour tradition based on the common idioms found

Vocal Source Separation for Carnatic Music

Carnatic Music is a Classical music form that originates from the South of India and is extremely varied from Western genres. Music Information Retrieval (MIR) has predominantly been used to tackle problems in western musical genres and cannot be adapted to non western musical styles like Carnatic Music due to the fundamental difference in melody, rhythm, instrumentation, nature of compositions and improvisations. Due to these conceptual differences emerged MIR tasks specific for the use case of Carnatic Music. Researchers have constantly been using domain knowledge and technology driven ideas to tackle tasks like Melodic analysis, Rhythmic analysis and Structural segmentation. Melodic analysis of Carnatic Music has been a cornerstone in MIR research and heavily relies on the singing voice because the singer offers the main melody. The problem is that the singing voice is not isolated and has melodic, percussion and drone instruments as accompaniment. Separating the singing voice from the accompanying instruments usually comes with issues like bleeding of the accompanying instruments and loss of melodic information. This in turn has an adverse effect on the melodic analysis. The datasets used for Carnatic-MIR are concert recordings of different artistes with accompanying instruments and there is a lack of clean isolated singing voice tracks. Existing Source Separation models are trained extensively on multi-track audio of the rock and pop genre and do not generalize well for the use case of Carnatic music. How do we improve Singing Voice Source Separation for Carnatic Music given the above constraints? In this work, the possible contributions to mitigate the existing issue are ; 1) Creating a dataset of isolated Carnatic music stems. 2) Reusing multi-track audio with bleeding from the Saraga dataset. 3) Retraining and fine tuning existing State of the art Source Separation models. We hope that this effort to improve Source Separation for Carnatic Music can help overcome existing shortcomings and generalize well for Carnatic music datasets in the literature and in turn improve melodic analysis of this music culture