The Moving Picture Experts Group (MPEG) is an International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) working group that develops media coding standards. These standards include a set of ontologies for the codification of intellectual property rights (IPR) information related to media. The Media Value Chain Ontology (MVCO) facilitates rights tracking for fair, timely, and transparent payment of royalties by capturing user roles and their permissible actions on a particular IP entity. The Audio Value Chain Ontology (AVCO) extends MVCO functionality related to the description of IP entities in the audio domain, e.g., multitrack audio and time segments. The Media Contract Ontology (MCO) facilitates the conversion of narrative contracts to digital ones. Furthermore, the axioms in these ontologies can drive the execution of rights-related workflows in controlled environments, e.g., blockchains, where transparency and interoperability is favored toward fair trade of music and media. Thus, the aim of this article is to create awareness of the MPEG IPR ontologies developed in the last few years and the work currently taking place addressing the challenge identified toward the execution of such ontologies as smart contracts on blockchain environments.

While common approaches to automatic structural analysis of music typically focus on individual audio files, our approach collates audio features of large sets of related files in order to find a shared musical temporal structure. The content of each individual file and the differences between them can then be described in relation to this shared structure. We first construct a large similarity graph of temporal segments, such as beats or bars, based on self-alignments and selected pair-wise alignments between the given input files. Part of this graph is then partitioned into groups of corresponding segments using multiple sequence alignment. This partitioned graph is searched for recurring sections which can be organized hierarchically based on their co-occurrence. We apply our approach to discover shared harmonic structure in a dataset containing a large number of different live performances of a number of songs. Our evaluation shows that using the joint information from a number of files has the advantage of evening out the noisiness or inaccuracy of the underlying feature data and leads to a robust estimate of shared musical material.

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.

MPEG is a ISO/IEC working group developing media c oding standards. This includes a set of standardized ontologies for the c odifi ation of intellectual property (IP) rights information related to media. The Media Valu e Chain Ontology (MVCO) facilitates rights tracking for fair, timely and transparent ro yalties payment by capturing user roles and their permissible actions on a particular IP entity . The Audio Value Chain Ontology (AVCO) extends MVCO functionality related to description o f composite IP entities in the audio domain, e.g., multi-track audio and time-segments. The Media Contract Ontology (MCO) facilitates the conversion of narrative contracts t o digital ones. These ontologies provide the elements for the creation of generic deontic statem ents encompassing the concepts of permission, prohibition and obligation. The joint u se of MCO/MVCO/AVCO ontologies enables a number of applications where IP data is r epresented in a standard and structured manner. Furthermore, MCO/MVCO/AVCO ontologies can b e executed in a controlled environment where transparency and interoperability is favoured towards fair trade of music and media.

In this paper, we present an analysis of feedback as it occurs in classroom-based and technology supported music instrument learning. Feedback is key to learning in music education, and we have developed technology based on ideas from social media and audio annotation which aims to make feedback more effective. The analysis here aims to enhance our understanding of technology-mediated feedback. The result of this analysis is three ontologies describing feedback and feedback systems. First, we developed the \emph{teacher's ontology} using a qualitative, observational approach to describe the types of feedback that music instrument tutors give to their students. We used this ontology to inform the design of an online music annotation platform for music students. Second, we developed the \emph{grounded ontology} using a grounded theory approach, based on 2,000 annotations made by students and tutors using the annotation platform. We compare the grounded and teacher's ontologies by examining structural, semantic and expressive features. Through this comparison, we find that the grounded ontology includes elements of the teacher's ontology as well as elements relating to practical and social aspects of the annotation platform, while the teacher's ontology contains more domain knowledge. The third ontology is a formalisation of the transactional capabilities of the platform, and we refer to it as the \emph{platform ontology}. We present it using the OWL language, and we show how this allows us to develop several practical use cases, including the use of semantic web capabilities in music education contexts.

We refine and unify our previous data model for describing and linking live music artefacts. In our model, physical and digital artefacts and recordings are treated as forms of cultural heritage which can all be aligned and distributed along the same event timeline. We show how our ontology maps to existing conceptual models and we evaluate it with a number of example queries as well as in practice, embedded in an online platform dedicated to the exploration of aggregated information documenting the live music events of a specific band.

Building upon a collection with functionality for discovery and analysis has been described by Lynch as a `layered' approach to digital libraries. Meanwhile, as digital corpora have grown in size, their analysis is necessarily supplemented by automated application of computational methods, which can create layers of information as intricate and complex as those within the content itself. This combination of layers - aggregating homogeneous collections, specialised analyses, and new observations - requires a flexible approach to systems implementation which enables pathways through the layers via common points of understanding, while simultaneously accommodating the emergence of previously unforeseen layers. In this paper we follow a Linked Data approach to build a layered digital library based on content from the Internet Archive Live Music Archive. Starting from the recorded audio and basic information in the Archive, we first deploy a layer of catalogue metadata which allows an initial - if imperfect - consolidation of performer, song, and venue information. A processing layer extracts audio features from the original recordings, workflow provenance, and summary feature metadata. A further analysis layer provides tools for the user to combine audio and feature data, discovered and reconciled using interlinked catalogue and feature metadata from layers below. Finally, we demonstrate the feasibility of the system through an investigation of `key typicality' across performances. This highlights the need to incorporate robustness to inevitable `imperfections' when undertaking scholarship within the digital library, be that from mislabelling, poor quality audio, or intrinsic limitations of computational methods. We do so not with the assumption that a `perfect' version can be reached; but that a key benefit of a layered approach is to allow accurate representations of information to be discovered, combined, and investigated for informed interpretation.

We describe the publication of a linked data set exposing metadata from the Internet Archive Live Music Archive along with detailed feature analysis data of the audio files contained in the archive. The collection is linked to existing musical and geographical resources allowing for the extraction of useful or nteresting subsets of data using additional metadata. The collection is published using a ‘layered’ approach, aggregating the original information with links and specialised analyses, and forms a valuable resource for those investigating or developing audio analysis tools and workflows.

Semantic Audio is an emerging field in the intersection of signal processing, machine learning, knowledge representation and ontologies, unifying techniques involving audio analysis and the Semantic Web. These mechanisms enable the creation of new applications and user experiences for music communities. We present a case study focussing on what Semantic Audio can offer to a specific fan base, that of the Grateful Dead, characterised by a profoundly strong affinity with technology and the internet. We discuss an application that combines information drawn from existing platforms and results from the automatic analysis of audio content to infer higher-level musical information, providing novel user experiences particularly in the context of live music events.

In music production, descriptive terminology is used to define perceived sound transformations. By understanding the underlying statistical features associated with these descriptions, we can aid the retrieval of contextually relevant processing parameters using natural language, and create intelligent systems capable of assisting in audio engineering. In this study, we present an analysis of a dataset containing descriptive terms gathered using a series of processing modules, embedded within a Digital Audio Workstation. By applying hierarchical clustering to the audio feature space, we show that similarity in term representations exists within and between transformation classes. Furthermore, the organisation of terms in low-dimensional timbre space can be explained using perceptual concepts such as size and dissonance. We conclude by performing Latent Semantic Indexing to show that similar groupings exist based on term frequency.

Recordings of historical live music performances often exist in several versions, either recorded from the mixing desk, on stage, or by audience members. These recordings highlight different aspects of the performance, but they also typically vary in recording quality, playback speed, and segmentation. We present a system that automatically aligns and clusters live music recordings based on various audio characteristics and editorial metadata. The system creates an immersive virtual space that can be imported into a multichannel web or mobile application allowing listeners to navigate the space using interface controls or mobile device sensors. We evaluate our system with recordings of different lineages from the Internet Archive Grateful Dead collection.