1 citations · 1 across the 2 of their papers we have counts for
3 papers
eess.AS2019★ 1 cited
Segment Relevance Estimation for Audio Analysis and Weakly-Labelled Classification
Juliano Henrique Foleiss, Tiago Fernandes Tavares
We propose a method that quantifies the importance, namely relevance, of audio segments for classification in weakly-labelled problems. It works by drawing information from a set o…
cs.SD2019
Random Projections of Mel-Spectrograms as Low-Level Features for Automatic Music Genre Classification
Juliano Henrique Foleiss, Tiago Fernandes Tavares
In this work, we analyse the random projections of Mel-spectrograms as low-level features for music genre classification. This approach was compared to handcrafted features, featur…
cs.SD2019
Texture Selection for Automatic Music Genre Classification
Juliano H. Foleiss, Tiago F. Tavares
Music Genre Classification is the problem of associating genre-related labels to digitized music tracks. It has applications in the organization of commercial and personal music co…