27 citations · 140 across the 21 of their papers we have counts for
6 papers · 1 filter
Towards Deep Modeling of Music Semantics using EEG Regularizers
Francisco Raposo, David Martins de Matos, Ricardo Ribeiro +2
Modeling of music audio semantics has been previously tackled through learning of mappings from audio data to high-level tags or latent unsupervised spaces. The resulting semantic…
An Information-theoretic Approach to Machine-oriented Music Summarization
Francisco Raposo, David Martins de Matos, Ricardo Ribeiro
Music summarization allows for higher efficiency in processing, storage, and sharing of datasets. Machine-oriented approaches, being agnostic to human consumption, optimize these a…
Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach
Luís Marujo, Ricardo Ribeiro, David Martins de Matos +3
The increasing amount of online content motivated the development of multi-document summarization methods. In this work, we explore straightforward approaches to extend single-docu…
Using Generic Summarization to Improve Music Information Retrieval Tasks
Francisco Raposo, Ricardo Ribeiro, David Martins de Matos
In order to satisfy processing time constraints, many MIR tasks process only a segment of the whole music signal. This practice may lead to decreasing performance, since the most i…
On the Application of Generic Summarization Algorithms to Music
Francisco Raposo, Ricardo Ribeiro, David Martins de Matos
Several generic summarization algorithms were developed in the past and successfully applied in fields such as text and speech summarization. In this paper, we review and apply the…
Centrality-as-Relevance: Support Sets and Similarity as Geometric Proximity
Ricardo Ribeiro, David Martins de Matos
In automatic summarization, centrality-as-relevance means that the most important content of an information source, or a collection of information sources, corresponds to the most…