Automatic genre classification of music content: a survey
By Nicolas Scaringella, Giorgio Zoia, Daniel Mlynek. IEEE Signal Processing Magazine, 2006
378 citations recorded by the source corpus.
This paper reviews the state-of-the-art in automatic genre classification of music collections through three main paradigms: expert systems, unsupervised classification, and supervised classification. The paper discusses the importance of music genres with their definitions and hierarchies. It also presents techniques to extract meaningful information from audio data to characterize musical excerpts. The paper also presents the results of new emerging research fields and techniques that investigate the proximity of music genres
Subfields: Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/7e7a9899a839eda239ebb001bc6b45002fc1a2b7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Emmanouil Benetos
- Mathieu Lagrange
- Xavier Bresson
- Mark D. Plumbley
- Yi-Hsuan Yang
- Dan Stowell
- Michaël Defferrard
- Dimitrios Giannoulis
In Signal Processing
- Emmanouil Benetos
- Mathieu Lagrange
- Mark D. Plumbley
- Dan Stowell
- Xavier Bresson
- Yi-Hsuan Yang
- Dimitrios Giannoulis
- Homer H. Chen
Citations in the corpus
- A Survey of Audio-Based Music Classification and Annotation (2011, methodology): “Scaringella [67] followed a more standard taxonomy”
- FMA: A Dataset for Music Analysis (2016, methodology): “While there is no agreement on a taxonomy of genres [30], we followed the hierarchy used by our source.”
- Detection and Classification of Acoustic Scenes and Events (2015, background): “It therefore has parallels with audio classification tasks such as music genre recognition [12] or speaker recognition [13], and with classification tasks in other time-based media such as video.”
- Machine Recognition of Music Emotion: A Review (2012, methodology): “2008], by conducting music structure analysis to extract the chorus section [Cheng et al. 2009; Maddage et al. 2004], or simply by selecting the middle 30-second [Hu et al. 2008] or the 30-second segment starting from the 30th second of a song [Scaringella et al. 2006; Yang et al. 2007].”