Music Genre Classification using Auto-Associative Neural Networks
نویسندگان
چکیده
II. REVIEW OF STATE OF THE ART Abstract—Classification of musical genres gives a useful measure of similarity and is often the most useful descriptor of a musical piece. Principal Component Analysis (PCA) has been generally applied on raw music signals to capture the major components for each genre. As a large number of principal components are obtained for different genres, the purpose of applying PCA is not satisfied. This led to, in the proposed work, feature vector extraction directly from the music signal and building an alternative model to capture the feature vector distribution of a music genre. Timbre modeling is done using Mel Frequency Cepstral Coefficients (MFCCs). The modeling of the decision logic is based on Auto Associative Neural Network (AANN) where the models perform an identity mapping on the input space. The property of a five layer AANN model to capture the feature vector distribution is used to build a music genre classification system. The focus of the survey is on the approaches followed at the feature, model and decision levels for music genre classification
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