Music Genre Classification using the multivariate AR feature integration model

Publication: Research - peer-reviewReport – Annual report year: 2005


  • Imm4010

    54 KB, application/octet-stream

View graph of relations

Music genre classification systems are normally build as a feature extraction module followed by a classifier. The features are often short-time features with time frames of 10-30ms, although several characteristics of music require larger time scales. Thus, larger time frames are needed to take informative decisions about musical genre. For the MIREX music genre contest several authors derive long time features based either on statistical moments and/or temporal structure in the short time features. In our contribution we model a segment (1.2 s) of short time features (texture) using a multivariate autoregressive model. Other authors have applied simpler statistical models such as the mean-variance model, which also has been included in several of this years MIREX submissions, see e.g. Tzanetakis (2005); Burred (2005); Bergstra et al. (2005); Lidy and Rauber (2005).
Original languageEnglish
Publication date2005
Download as:
Download as PDF
Select render style:
Download as HTML
Select render style:
Download as Word
Select render style:

Download statistics

No data available

ID: 2807009