Symbolic Melodic Similarity: State of the Art and Future Challenges

Fostered by the introduction of the Music Information Retrieval Evaluation eXchange (MIREX) competition, the number of systems which calculate Symbolic Melodic Similarity has recently increased considerably. In order to understand the state of the art, we provide a comparative analysis of existing algorithms. The analysis is based on eight criteria that help characterising the systems, and highlighting strengths and weaknesses. We also propose a taxonomy which classifies algorithms based on their approach. Both taxonomy and criteria are fruitfully exploited for providing input for new forthcoming research in the area

MusicXML Analyzer. Ein Analysewerkzeug für die computergestützte Identifikation von Melodie-Patterns

Der Beitrag beschreibt Aktivitäten aus einem aktuellen Digital Humanities-Projekt, das im Schnittfeld von Informations-, Musik- und Kulturwissenschaft angesiedelt ist. Dabei soll eine Sammlung von ca. 50.000 handschriftlichen Liedblättern mit deutschsprachiger Volksmusik digitalisiert und maschinenlesbar in MusicXML kodiert werden, um schließlich über ein Informationssystem quantitative Analysen des Materials zu erlauben. Wir stellen einen ersten webbasierten Prototypen vor, der es erlaubt, Musikstücke im MusicXML-Format statistisch auszuwerten und nach konkreten Melodie-Patterns zu suchen. Das Tool ist zudem in der Lage, virtuelle Partituren und Audioausgaben auf Basis des MusicXML-Markups zu erstellen und direkt im Webbrowser verfügbar zu machen

Sequential decision making in artificial musical intelligence

Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science

Music Encoding Conference Proceedings 2021, 19–22 July, 2021 University of Alicante (Spain): Onsite & Online

Este documento incluye los artículos y pósters presentados en el Music Encoding Conference 2021 realizado en Alicante entre el 19 y el 22 de julio de 2022.Funded by project Multiscore, MCIN/AEI/10.13039/50110001103

Engineering systematic musicology : methods and services for computational and empirical music research

One of the main research questions of *systematic musicology* is concerned with how people make sense of their musical environment. It is concerned with signification and meaning-formation and relates musical structures to effects of music. These fundamental aspects can be approached from many different directions. One could take a cultural perspective where music is considered a phenomenon of human expression, firmly embedded in tradition. Another approach would be a cognitive perspective, where music is considered as an acoustical signal of which perception involves categorizations linked to representations and learning. A performance perspective where music is the outcome of human interaction is also an equally valid view. To understand a phenomenon combining multiple perspectives often makes sense. The methods employed within each of these approaches turn questions into concrete musicological research projects. It is safe to say that today many of these methods draw upon digital data and tools. Some of those general methods are feature extraction from audio and movement signals, machine learning, classification and statistics. However, the problem is that, very often, the *empirical and computational methods require technical solutions* beyond the skills of researchers that typically have a humanities background. At that point, these researchers need access to specialized technical knowledge to advance their research. My PhD-work should be seen within the context of that tradition. In many respects I adopt a problem-solving attitude to problems that are posed by research in systematic musicology. This work *explores solutions that are relevant for systematic musicology*. It does this by engineering solutions for measurement problems in empirical research and developing research software which facilitates computational research. These solutions are placed in an engineering-humanities plane. The first axis of the plane contrasts *services* with *methods*. Methods *in* systematic musicology propose ways to generate new insights in music related phenomena or contribute to how research can be done. Services *for* systematic musicology, on the other hand, support or automate research tasks which allow to change the scope of research. A shift in scope allows researchers to cope with larger data sets which offers a broader view on the phenomenon. The second axis indicates how important Music Information Retrieval (MIR) techniques are in a solution. MIR-techniques are contrasted with various techniques to support empirical research. My research resulted in a total of thirteen solutions which are placed in this plane. The description of seven of these are bundled in this dissertation. Three fall into the methods category and four in the services category. For example Tarsos presents a method to compare performance practice with theoretical scales on a large scale. SyncSink is an example of a service

HiER 2015. Proceedings des 9. Hildesheimer Evaluierungs- und Retrievalworkshop

Die Digitalisierung formt unsere Informationsumwelten. Disruptive Technologien dringen verstärkt und immer schneller in unseren Alltag ein und verändern unser Informations- und Kommunikationsverhalten. Informationsmärkte wandeln sich. Der 9. Hildesheimer Evaluierungs- und Retrievalworkshop HIER 2015 thematisiert die Gestaltung und Evaluierung von Informationssystemen vor dem Hintergrund der sich beschleunigenden Digitalisierung. Im Fokus stehen die folgenden Themen: Digital Humanities, Internetsuche und Online Marketing, Information Seeking und nutzerzentrierte Entwicklung, E-Learning