In this paper, we investigate the impact of machine learning algorithms in the development of automatic music classification models aiming to capture genres distinctions. The stu...
We took a collection of 100 drum beats from popular music tracks and estimated the measure length and downbeat position of each one. Using these values, we normalized each pattern...
The number of studies investigating automated genre classification is growing following the increasing amounts of digital audio data available. The underlying techniques to perfor...
In this work we present a scalable feature set which is obtained by fitting orthogonal polynomials to the normalized modulation spectrum of cepstral coefficients and which can b...
With the rising popularity of digital music archives the need for new access methods such as interactive exploration or similarity-based search become significant. In this paper ...
Robert Neumayer, Michael Dittenbach, Andreas Raube...