Representing Musical Genre: A State of the Art
By Jean-Julien Aucouturier, François Pachet. 2003
418 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/e352e3247cfecf9bbcc3955074c39e3654032a3a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Gino Cattani
- Joseph Porac
- Howard Thomas
- Tuomas Eerola
- Bob L. Sturm
- François Pachet
- Jean-Julien Aucouturier
- Douglas Eck
In Computer Vision and Pattern Recognition
- Douglas Eck
- Yoshua Bengio
- François Pachet
- Luc Van Gool
- Dacheng Tao
- James Bergstra
- Jean-Julien Aucouturier
- Xiaogang Wang
In Signal Processing
- Douglas Eck
- Jean-Julien Aucouturier
- Giorgio Zoia
- Nicolas Scaringella
- Daniel Mlynek
- François Pachet
- Gino Cattani
- Joseph Porac
Citations in the corpus
- Categories and Competition (2017, methodology): “Aucouturier and Pachet (2003) followed up by comparing various methods of music genre construction, both automated and human-based, and found little correspondence in the resulting category systems. Hsu (2006) studied the effects of genre labels on the box office success of films and observed that o”
- Aggregate features and ADABOOST for music classification (2006, background): “A survey by Aucouturier and Pachet (2003) describes a number of popular features for music similarity and classification, and research continues (e.g. Bello et al. (2005), Pampalk et al. (2005)).”
- Improving Timbre Similarity : How high’s the sky ? (2004, methodology): “Objective evaluation is also problematic, because of the choice of a ground truth to compare the measure to. In [ 1 ], we have projected our similarity measure on genre metadata to study its agreement to the class information, using the Fisher coefficient.”
- Automatic genre classification of music content: a survey (2006, background): “They conclude that the classification results are hardly over the baseline, which seems to confirm the negative results of [16] suggesting the need for extramusical information.”
- A Simple Method to Determine if a Music Information Retrieval System is a “Horse” (2014, background): ““Ground truth” in music genre recognition, music emotion recognition and music autotagging, for instance, has notorious ambiguity [4], [40]–[44].”
- Are the Emotions Expressed in Music Genre-specific? An Audio-based Evaluation of Datasets Spanning Classical, Film, Pop and Mixed Genres (2011, background): “However, the genres themselves—also called musical styles in the analysis and history literature—are not easy to define, since no natural genre taxonomies exist and they are defined as cultural constructs rather than by any intrinsic musical properties (e.g. Aucouturier & Pachet, 2003).”