Towards incorporating the notion of feature shape in music and text retrieval

Extracted feature data augment information resources with concrete characterizations of their content, but only approximate to the meaningful high-level descriptions typically expected by digital musicology scholars (domain experts with some technological affinity, but with no expertise in signal processing or feature data). Feature shapes provide abstract aggregations of feature types which share common characteristics when applied in extraction workflows. We explore the feasibility of feature shape-based filtering and querying within a large audio dataset of live music performances, employing operation sequences as specified by the Audio Feature Ontology and Vocabulary. We further implement analogous semantic structures for the HathiTrust Extracted Feature Dataset to demonstrate the general applicability of feature shapes in music and text retrieval

Playsound.space: enhancing a live music performance tool with semantic recommendations

Playsound is an experimental client for the Freesound API. Its main aim is to provide a simple and intuitive tool for the collaborative composition based on Freesound samples. In this paper, an approach based on Semantic Web technologies to provide recommendations to Playsound users is presented. A Semantic Web of Things architecture is proposed, showing loosely coupled, independent software agents interoperating by means of a semantic publish/subscribe platform and a set of ontologies to describe agents, audio contents, input/output of audio analytics tools and recommendations. Preliminary tests show that the designed architecture adapts well to environments where services can be discovered and seamlessly orchestrated on the fly, resulting in a dynamic workflow

Towards a Semantic Architecture for the Internet of Musical Things

The Internet of Musical Things is an emerging research area that relates to the network of Musical Things, which are computing devices embedded in physical objects dedicated to the production and/or reception of musical content. In this paper we propose a semantically-enriched Internet of Musical Things architecture which relies on a semantic audio server and edge computing techniques. Specifically, a SPARQL Event Processing Architecture is employed as an interoperability enabler allowing multiple heterogeneous Musical Things to cooperate, relying on a music-related ontology. We technically validate our architecture by implementing an ecosystem around it, where five Musical Thing prototypes communicate between each other

A History of Audio Effects

Audio effects are an essential tool that the field of music production relies upon. The ability to intentionally manipulate and modify a piece of sound has opened up considerable opportunities for music making. The evolution of technology has often driven new audio tools and effects, from early architectural acoustics through electromechanical and electronic devices to the digitisation of music production studios. Throughout time, music has constantly borrowed ideas and technological advancements from all other fields and contributed back to the innovative technology. This is defined as transsectorial innovation and fundamentally underpins the technological developments of audio effects. The development and evolution of audio effect technology is discussed, highlighting major technical breakthroughs and the impact of available audio effects

Encoding Scores for Electronic Music

A perspective on the specific issues of music encoding dealing with Electronic Music is presented. In many cases the works to be discussed exist in a fixed media format and hence no prescriptive score is necessary to facilitate a ‘valid’ performance. While there are a number of descriptive scores for pieces of Electronic Music, these are to be treated differently, as they are purely aimed at analysis and therefore contain a certain information bias. Data that is more comparable to instrumental scores is contained in rare examples of so-called realization scores. It is argued that these realization scores can be identified as the main subject for encoding of Electronic Music works. For this we will discuss an example from one such score by Karlheinz Stockhausen. For his piece KONTAKTE, Stockhausen released a realization score that unfolds a very detailed documentation of all steps made within the studio production of that work, including the complex patching of studio devices and the specific transformation processes achieved by the use of tape machines. The paper presents an approach to formalize and encode all these steps within the framework of a semantic database. Using technology like the semantic web standard, Linked Data and the corresponding RDF/OWL framework, an Electronic Music production setup and its usage can be encoded, stored, and analyzed

C Minor: a Semantic Publish/Subscribe Broker for the Internet of Musical Things

Semantic Web technologies are increasingly used in the Internet of Things due to their intrinsic propensity to foster interoperability among heterogenous devices and services. However, some of the IoT application domains have strict requirements in terms of timeliness of the exchanged messages, latency and support for constrained devices. An example of these domains is represented by the emerging area of the Internet of Musical Things. In this paper we propose C Minor, a CoAP-based semantic publish/subscribe broker specifically designed to meet the requirements of Internet of Musical Things applications, but relevant for any IoT scenario. We assess its validity through a practical use case

Abstract Representation of Music: A Type-Based Knowledge Representation Framework

The wholesale efficacy of computer-based music research is contingent on the sharing and reuse of information and analysis methods amongst researchers across the constituent disciplines. However, computer systems for the analysis and manipulation of musical data are generally not interoperable. Knowledge representation has been extensively used in the domain of music to harness the benefits of formal conceptual modelling combined with logic based automated inference. However, the available knowledge representation languages lack sufficient logical expressivity to support sophisticated musicological concepts. In this thesis we present a type-based framework for abstract representation of musical knowledge. The core of the framework is a multiple-hierarchical information model called a constituent structure, which accommodates diverse kinds of musical information. The framework includes a specification logic for expressing formal descriptions of the components of the representation. We give a formal specification for the framework in the Calculus of Inductive Constructions, an expressive logical language which lends itself to the abstract specification of data types and information structures. We give an implementation of our framework using Semantic Web ontologies and JavaScript. The ontologies capture the core structural aspects of the representation, while the JavaScript tools implement the functionality of the abstract specification. We describe how our framework supports three music analysis tasks: pattern search and discovery, paradigmatic analysis and hierarchical set-class analysis, detailing how constituent structures are used to represent both the input and output of these analyses including sophisticated structural annotations. We present a simple demonstrator application, built with the JavaScript tools, which performs simple analysis and visualisation of linked data documents structured by the ontologies. We conclude with a summary of the contributions of the thesis and a discussion of the type-based approach to knowledge representation, as well as a number of avenues for future work in this area

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