Mohamed Sordo
Biographic Data
| ID | 1601044 |
|---|---|
| NAME | Mohamed Sordo |
| GIVEN NAMES | Mohamed |
| FAMILY NAME | Sordo |
| SIGNATURE | SORDO M |
| AFFILIATIONS | Universitat Pompeu Fabra |
| ORCID | 0000-0002-4608-978X |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2011 |
| LATEST PUBLICATION YEAR | 2016 |
| H-INDEX | 1 |
Knowledge Is Out There
Musicological knowledge is spread between the lines of thousands of texts stored in hundreds of music libraries. Technology, and more recently semantic technologies, may play a key role in the way information is retrieved. In this article, an analysis of the evolution of music digital libraries from a technological perspective is presented. Then, a methodology to exploit implicit knowledge present in collections of text documents is proposed. The…
Inferring Semantic Facets of a Music Folksonomy with Wikipedia
Music folksonomies include both general and detailed descriptions of music, and are usually continuously updated. These are significant advantages over music tax-onomies, which tend to be incomplete and inconsistent. However, music folksonomies have an inherent loose and open semantics, which hampers their use in many appli-cations, such as structured music browsing and recommendation. In this paper, we present a system that can (1) automatically…
Short-term Feature Space and Music Genre Classification
In music genre classification, most approaches rely on statistical characteristics of low-level features computed on short audio frames. In these methods, it is implicitly considered that frames carry equally relevant information loads and that either individual frames, or distributions thereof, somehow capture the specificities of each genre. In this paper we study the representation space defined by short-term audio features with respect to cla…
Short-term Feature Space and Music Genre Classification
In music genre classification, most approaches rely on statistical characteristics of low-level features computed on short audio frames. In these methods, it is implicitly considered that frames carry equally relevant information loads and that either individual frames, or distributions thereof, somehow capture the specificities of each genre. In this paper we study the representation space defined by short-term audio features with respect to cla…
Short-term Feature Space and Music Genre Classification
In music genre classification, most approaches rely on statistical characteristics of low-level features computed on short audio frames. In these methods, it is implicitly considered that frames carry equally relevant information loads and that either individual frames, or distributions thereof, somehow capture the specificities of each genre. In this paper we study the representation space defined by short-term audio features with respect to cla…
Inferring Semantic Facets of a Music Folksonomy with Wikipedia
Music folksonomies include both general and detailed descriptions of music, and are usually continuously updated. These are significant advantages over music tax-onomies, which tend to be incomplete and inconsistent. However, music folksonomies have an inherent loose and open semantics, which hampers their use in many appli-cations, such as structured music browsing and recommendation. In this paper, we present a system that can (1) automatically…
Knowledge Is Out There
Musicological knowledge is spread between the lines of thousands of texts stored in hundreds of music libraries. Technology, and more recently semantic technologies, may play a key role in the way information is retrieved. In this article, an analysis of the evolution of music digital libraries from a technological perspective is presented. Then, a methodology to exploit implicit knowledge present in collections of text documents is proposed. The…
Computer Science (3 works) · Music and Audio Processing (3 works) · Artificial Intelligence (2 works) · Information retrieval (2 works) · Music Technology and Sound Studies (2 works) · World Wide Web (2 works) · Art (1 work) · Artificial Intelligence (1 work) · Data science (1 work) · Digital Humanities and Scholarship (1 work)