- Tytuł:
- Automatic Genre Classification Using Fractional Fourier Transform Based Mel Frequency Cepstral Coefficient and Timbral Features
- Autorzy:
-
Bhalke, D. G.
Rajesh, B.
Bormane, D. S. - Powiązania:
- https://bibliotekanauki.pl/articles/177599.pdf
- Data publikacji:
- 2017
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
feature extraction
Timbral features
MFCC
Mel Frequency Cepstral Coefficient
FrFT
fractional Fourier transform
Fractional MFCC
Tamil Carnatic music - Opis:
- This paper presents the Automatic Genre Classification of Indian Tamil Music and Western Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). In this work, the performance of various features extracted from music excerpts has been analysed, to identify the appropriate feature descriptors for the two major genres of Indian Tamil music, namely Classical music (Carnatic based devotional hymn compositions) & Folk music and for western genres of Rock and Classical music from the GTZAN dataset. The results for Tamil music have shown that the feature combination of Spectral Roll off, Spectral Flux, Spectral Skewness and Spectral Kurtosis, combined with Fractional MFCC features, outperforms all other feature combinations, to yield a higher classification accuracy of 96.05%, as compared to the accuracy of 84.21% with conventional MFCC. It has also been observed that the FrFT based MFCC effieciently classifies the two western genres of Rock and Classical music from the GTZAN dataset with a higher classification accuracy of 96.25% as compared to the classification accuracy of 80% with MFCC.
- Źródło:
-
Archives of Acoustics; 2017, 42, 2; 213-222
0137-5075 - Pojawia się w:
- Archives of Acoustics
- Dostawca treści:
- Biblioteka Nauki
Artykuł