Clever Toolbox - the Art of Automated Genre Classification

Publication: ResearchInteractive production – Annual report year: 2005

Standard

Clever Toolbox - the Art of Automated Genre Classification. / Ahrendt, Peter (Author); Meng, Anders (Author); Larsen, Jan (Author); Lehmann, Sune (Author).

2005. Kgs. Lyngby : Informatics and Mathematical Modelling, Technical University of Denmark.

Publication: ResearchInteractive production – Annual report year: 2005

Harvard

Clever Toolbox - the Art of Automated Genre Classification, Ahrendt, P, Meng, A, Larsen, J & Lehmann, S Clever Toolbox - the Art of Automated Genre Classification. Informatics and Mathematical Modelling, Technical University of Denmark, Kgs. Lyngby. Interactive production.

APA

Ahrendt, P., Meng, A., Larsen, J., & Lehmann, S. (2005). Clever Toolbox - the Art of Automated Genre Classification Kgs. Lyngby: Informatics and Mathematical Modelling, Technical University of Denmark. [Interactive production].

CBE

Ahrendt P, Meng A, Larsen J, Lehmann S. 2005. Clever Toolbox - the Art of Automated Genre Classification. Kgs. Lyngby: Informatics and Mathematical Modelling, Technical University of Denmark. [Interactive production].

MLA

Ahrendt, Peter et al. Clever Toolbox - the Art of Automated Genre Classification Kgs. Lyngby: Informatics and Mathematical Modelling, Technical University of Denmark. Interactive production. 2005.

Vancouver

Ahrendt P, Meng A, Larsen J, Lehmann S. Clever Toolbox - the Art of Automated Genre Classification. Kgs. Lyngby: Informatics and Mathematical Modelling, Technical University of Denmark. [Interactive production]. 2005.

Author

Ahrendt, Peter (Author); Meng, Anders (Author); Larsen, Jan (Author); Lehmann, Sune (Author) / Clever Toolbox - the Art of Automated Genre Classification.

2005. Kgs. Lyngby : Informatics and Mathematical Modelling, Technical University of Denmark.

Publication: ResearchInteractive production – Annual report year: 2005

Bibtex

@misc{d8910189edad431fa02dc065f3870938,
title = "Clever Toolbox - the Art of Automated Genre Classification",
publisher = "Informatics and Mathematical Modelling, Technical University of Denmark",
address = "Kgs. Lyngby",
author = "Peter Ahrendt and Anders Meng and Jan Larsen and Sune Lehmann",
year = "2005",
type = "Media <importModel: MediaImportModel>",

}

RIS

TY - ADVS

T1 - Clever Toolbox - the Art of Automated Genre Classification

A2 - Ahrendt,Peter

A2 - Meng,Anders

A2 - Larsen,Jan

A2 - Lehmann,Sune

ED - Ahrendt,Peter

ED - Meng,Anders

ED - Larsen,Jan

ED - Lehmann,Sune

PB - Informatics and Mathematical Modelling, Technical University of Denmark

PY - 2005

Y1 - 2005

N2 - Automatic musical genre classification can be defined as the science of finding computer algorithms that a digitized sound clip as input and yield a musical genre as output. The goal of automated genre classification is, of course, that the musical genre should agree with the human classificasion. This demo illustrates an approach to the problem that first extract frequency-based sound features followed by a "linear regression" classifier. The basic features are the so-called mel-frequency cepstral coefficients (MFCCs), which are extracted on a time-scale of 30 msec. From these MFCC features, auto-regressive coefficients (ARs) are extracted along with the mean and gain to get a single (30 dimensional) feature vector on the time-scale of 1 second. These features have been used because they have performed well in a previous study (Meng, Ahrendt, Larsen (2005)). Linear regression (or single-layer linear NN) is subsequently used for classification. This classifier is rather simple; current research investigates more advanced methods of classification.

AB - Automatic musical genre classification can be defined as the science of finding computer algorithms that a digitized sound clip as input and yield a musical genre as output. The goal of automated genre classification is, of course, that the musical genre should agree with the human classificasion. This demo illustrates an approach to the problem that first extract frequency-based sound features followed by a "linear regression" classifier. The basic features are the so-called mel-frequency cepstral coefficients (MFCCs), which are extracted on a time-scale of 30 msec. From these MFCC features, auto-regressive coefficients (ARs) are extracted along with the mean and gain to get a single (30 dimensional) feature vector on the time-scale of 1 second. These features have been used because they have performed well in a previous study (Meng, Ahrendt, Larsen (2005)). Linear regression (or single-layer linear NN) is subsequently used for classification. This classifier is rather simple; current research investigates more advanced methods of classification.

ER -