Harvesting and Structuring Social Data in Music Information Retrieval

Abstract. An exponentially growing amount of music and sound resources are being shared by communities of users on the Internet. Social media content can be found with different levels of structuring, and the contributing users might be experts or non-experts of the domain. Harvesting and structuring this information semantically would be very useful in context-aware Music Information Retrieval (MIR). Until now, scant research in this field has taken advantage of the use of formal knowledge representations in the process of structuring information. We propose a methodology that combines Social Media Mining, Knowledge Extraction and Natural Language Processing techniques, to extract meaningful context information from social data. By using the extracted information we aim to improve retrieval, discovery and annotation of music and sound resources. We define three different scenarios to test and develop our methodology

Investigating Networked Music Performances in Pedagogical Scenarios for the InterMUSIC Project

With the big improvement of digital communication networks, Networked Music Performances (NMP) received a great interest from music live performance and music recording industry. The positive impact of NMP in pedagogical appli- cations, instead, has been only preliminary explored. Within the InterMUSIC project, we aim to investigate NMP from a pedagogical perspective, that has considerable differences with respect to music performances, and to develop tools to improve distance learning experiences. In this paper, we introduce a conceptual framework designed to be the foundation for all the experiments conducted in the project. We also present two preliminary experiments that investigate the sense of presence of geographically-distant musicians in a distance learning scenario. We discuss the comments provided by the musicians as a set of requirements and guidelines for future experiments