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We present a new method for musical genre classification based on high-level melodic features that are extracted directly from the audio signal of polyphonic music. The features are obtained through the automatic characterisation of pitch contours describing the predominant melodic line, extracted using a state-of-the-art audio melody extraction algorithm. Using standard machine learning algorithms...
In this paper, we will propose an automatic music genre classification approach based on long-term modulation spectral analysis on the static and transitional information of spectral (OSC and MPEG-7 NASE) as well as cepstral (MFCC) features. An information fusion approach which integrates both feature level fusion and decision level combination is employed to improve the classification accuracy. Experiments...
In this paper, we constructed the noise robust content-based music retrieval system. The performance of the proposed system was verified with ZCPA feature, which is known to have noise robust characteristic in speech recognition application. In addition, new indexing and fast retrieval method were proposed to improve retrieval speed about 99% compare to exhaustive retrieval for large music DB. From...
This paper presents the results of the application of a feature selection procedure to an automatic music genre classification system. The classification system is based on the use of multiple feature vectors and an ensemble approach, according to time and space decomposition strategies. Feature vectors are extracted from music segments from the beginning, middle and end of the original music signal...
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