Automatic recognition of texture in Renaissance music

Renaissance music constitutes a resource of immense richness for Western culture, as shown by its central role in digital humanities. Yet, despite the advance of computational musicology in analysing other Western repertoires, the use of computer-based methods to automatically retrieve relevant information from Renaissance music, e. g., identifying word-painting strategies such as madrigalisms, is still underdeveloped. To this end, we propose a score-based machine learning approach for the classification of texture in Italian madrigals of the 16th century. Our outcomes indicate that Low Level Descriptors, such as intervals, can successfully convey differences in High Level features, such as texture. Furthermore, our baseline results, particularly the ones from a Convolutional Neural Network, show that machine learning can be successfully used to automatically identify sections in madrigals associated with specific textures from symbolic sources

Rhetorical Pattern Finding

In this paper, we research rhetorical patterns from a musicological and computational standpoint. First, a theoretical examination of what constitutes a rhetorical pattern is conducted. Out of that examination, which includes primary sources and the study of the main composers, a formal definition of rhetorical patterns is proposed. Among the rhetorical figures, a set of imitative rhetorical figures is selected for our study, namely, epizeuxis, palilogy, synonymia, and polyptoton. Next, we design a computational model of the selected rhetorical patterns to automatically find those patterns in a corpus consisting of masses by Renaissance composer Tomás Luis de Victoria. In order to have a ground truth with which to test out our model, a group of experts manually annotated the rhetorical patterns. To deal with the problem of reaching a consensus on the annotations, a four-round Delphi method was followed by the annotators. The rhetorical patterns found by the annotators and by the algorithm are compared and their differences discussed. The algorithm reports almost all the patterns annotated by the experts and some additional patterns. The algorithm reports almost all the patterns annotated by the experts (recall: 98.11%) and some additional patterns (precision: 71.73%). These patterns correspond to rhetorical patterns within other rhetorical patterns, which were overlooked by the annotators on the basis of their contextual knowledge. These results pose issues as to how to integrate that contextual knowledge into the computational model

Music Encoding Conference Proceedings

UIDB/00693/2020 UIDP/00693/2020publishersversionpublishe

Antonio Pistoia : The poetic world of a customs collector

The object of the present study is Antonio Pistoia (1436-1502), a jocular poet and customs collector who worked mainly in Northern Italy. Although his reputation as a notable literary figure has suffered from neglect in recent times, his work was appreciated by and known to his contemporaries including Pietro Aretino, Ludovico Ariosto, Matteo Bandello, Francesco Berni and Baldesar Castiglione. Research on his life and work came to a halt at the beginning of this century and since then he has failed to attract significant attention. The present study attempts to review and re-examine both the man and his work with a view to putting Antonio Pistoia back on the literary map. My thesis is based on the idea that a poet can be explored from various points of view and with different methodologies tailored to the objects under investigation. In the case of Pistoia a biographical history alone or an interpretation of his work alone would provide only partial results. By combining the two I have attempted to see how he and his work fitted within the cultural scene, the social and historical setting of Renaissance Italy in a period of political and military crisis. Based on archive work and on new textual material retrieved from a number of European libraries, this study challenges and tests widely held theories concerning both his biography and his literary production. By collecting fresh references and winnowing old ones, it throws new light on a series of specific issues from matters of identification relating to the poet's life, the critical fortune of his collection of sonnets, his play Panfila and other minor works, and to problems of uncertain authorship, including poems of undisputed, doubtful and arbitrary attribution; the final section is devoted to his Canzoniere, its composition and the tradition to which it belongs and a thematic and stylistic overview of his poems. A codicological analysis of the allegedly autograph manuscript and a listing of Pistoia's archival documents, manuscripts and early printed sources, completely assembled for the first time and comprehensive of additional new findings, conclude the study

Antonio Pistoia : the poetic world of a customs collector

The object of the present study is Antonio Pistoia (1436? - 1502), a jocular poet and customs collector who worked mainly in Northern Italy. Although his reputation as a notable literary figure has suffered from neglect in recent times, his work was appreciated by and known to his contemporaries including Pietro Aretino, Ludovico Ariosto, Matteo Bandello, Francesco Berni and Baldesar Castiglione. Research on his life and work came to a halt at the beginning of this century and since then he has failed to attract significant attention. The present study attempts to review and re-examine both the man and his work with a view to putting Antonio Pistoia back on the literary map. My thesis is based on the idea that a poet can be explored from various points of view and with different methodologies tailored to the objects under investigation. In the case of Pistoia a biographical history alone or an interpretation of his work alone would provide only partial results. By combining the two I have attempted to see how he and his work fitted within the cultural scene, the social and historical setting of Renaissance Italy in a period of political and military crisis. Based on archive work and on new textual material retrieved from a number of European libraries, this study challenges and tests widely held theories concerning both his biography and his literary production. By collecting fresh references and winnowing old ones, it throws new light on a series of specific issues from matters of identification relating to the poet's life, the critical fortune of his collection of sonnets, his play Panfila and other minor works, and to problems of uncertain authorship, including poems of undisputed, doubtful and arbitrary attribution; the final section is devoted to his Canzoniere, its composition and the tradition to which it belongs and a thematic and stylistic overview of his poems. A codicological analysis of the allegedly autograph manuscript and a listing of Pistoia's archival documents, manuscripts and early printed sources, completely assembled for the first time and comprehensive of additional new findings, conclude the study

Bag-of-words representations for computer audition

Computer audition is omnipresent in everyday life, in applications ranging from personalised virtual agents to health care. From a technical point of view, the goal is to robustly classify the content of an audio signal in terms of a defined set of labels, such as, e.g., the acoustic scene, a medical diagnosis, or, in the case of speech, what is said or how it is said. Typical approaches employ machine learning (ML), which means that task-specific models are trained by means of examples. Despite recent successes in neural network-based end-to-end learning, taking the raw audio signal as input, models relying on hand-crafted acoustic features are still superior in some domains, especially for tasks where data is scarce. One major issue is nevertheless that a sequence of acoustic low-level descriptors (LLDs) cannot be fed directly into many ML algorithms as they require a static and fixed-length input. Moreover, also for dynamic classifiers, compressing the information of the LLDs over a temporal block by summarising them can be beneficial. However, the type of instance-level representation has a fundamental impact on the performance of the model. In this thesis, the so-called bag-of-audio-words (BoAW) representation is investigated as an alternative to the standard approach of statistical functionals. BoAW is an unsupervised method of representation learning, inspired from the bag-of-words method in natural language processing, forming a histogram of the terms present in a document. The toolkit openXBOW is introduced, enabling systematic learning and optimisation of these feature representations, unified across arbitrary modalities of numeric or symbolic descriptors. A number of experiments on BoAW are presented and discussed, focussing on a large number of potential applications and corresponding databases, ranging from emotion recognition in speech to medical diagnosis. The evaluations include a comparison of different acoustic LLD sets and configurations of the BoAW generation process. The key findings are that BoAW features are a meaningful alternative to statistical functionals, offering certain benefits, while being able to preserve the advantages of functionals, such as data-independence. Furthermore, it is shown that both representations are complementary and their fusion improves the performance of a machine listening system.Maschinelles Hören ist im täglichen Leben allgegenwärtig, mit Anwendungen, die von personalisierten virtuellen Agenten bis hin zum Gesundheitswesen reichen. Aus technischer Sicht besteht das Ziel darin, den Inhalt eines Audiosignals hinsichtlich einer Auswahl definierter Labels robust zu klassifizieren. Die Labels beschreiben bspw. die akustische Umgebung der Aufnahme, eine medizinische Diagnose oder - im Falle von Sprache - was gesagt wird oder wie es gesagt wird. Übliche Ansätze hierzu verwenden maschinelles Lernen, d.h., es werden anwendungsspezifische Modelle anhand von Beispieldaten trainiert. Trotz jüngster Erfolge beim Ende-zu-Ende-Lernen mittels neuronaler Netze, in welchen das unverarbeitete Audiosignal als Eingabe benutzt wird, sind Modelle, die auf definierten akustischen Merkmalen basieren, in manchen Bereichen weiterhin überlegen. Dies gilt im Besonderen für Einsatzzwecke, für die nur wenige Daten vorhanden sind. Allerdings besteht dabei das Problem, dass Zeitfolgen von akustischen Deskriptoren in viele Algorithmen des maschinellen Lernens nicht direkt eingespeist werden können, da diese eine statische Eingabe fester Länge benötigen. Außerdem kann es auch für dynamische (zeitabhängige) Klassifikatoren vorteilhaft sein, die Deskriptoren über ein gewisses Zeitintervall zusammenzufassen. Jedoch hat die Art der Merkmalsdarstellung einen grundlegenden Einfluss auf die Leistungsfähigkeit des Modells. In der vorliegenden Dissertation wird der sogenannte Bag-of-Audio-Words-Ansatz (BoAW) als Alternative zum Standardansatz der statistischen Funktionale untersucht. BoAW ist eine Methode des unüberwachten Lernens von Merkmalsdarstellungen, die von der Bag-of-Words-Methode in der Computerlinguistik inspiriert wurde, bei der ein Textdokument als Histogramm der vorkommenden Wörter beschrieben wird. Das Toolkit openXBOW wird vorgestellt, welches systematisches Training und Optimierung dieser Merkmalsdarstellungen - vereinheitlicht für beliebige Modalitäten mit numerischen oder symbolischen Deskriptoren - erlaubt. Es werden einige Experimente zum BoAW-Ansatz durchgeführt und diskutiert, die sich auf eine große Zahl möglicher Anwendungen und entsprechende Datensätze beziehen, von der Emotionserkennung in gesprochener Sprache bis zur medizinischen Diagnostik. Die Auswertungen beinhalten einen Vergleich verschiedener akustischer Deskriptoren und Konfigurationen der BoAW-Methode. Die wichtigsten Erkenntnisse sind, dass BoAW-Merkmalsvektoren eine geeignete Alternative zu statistischen Funktionalen darstellen, gewisse Vorzüge bieten und gleichzeitig wichtige Eigenschaften der Funktionale, wie bspw. die Datenunabhängigkeit, erhalten können. Zudem wird gezeigt, dass beide Darstellungen komplementär sind und eine Fusionierung die Leistungsfähigkeit eines Systems des maschinellen Hörens verbessert

The Mimetic Strand in the Cello Literature

This book is the first integral study of the history of imitative or co-creative artistic work that has led to the creation of cello transcriptions and arrangements. Of an interdisciplinary character, it explores the views that have shaped approaches to the art of cello performance and describes the role of cello transcriptions and the development of instrument making. The book also addresses issues related to philosophy, history of aesthetics and visual arts, including iconography presenting historical images of the cello. The theoretical part contains definitions and systematics that make it possible to categorise the vast amount of transcriptions, as well as descriptions and suggested recordings of a selection of those transcriptions