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- W3049691031 abstract "The use of mel-frequency cepstral coeffcients (MFCCs) has proven to be a powerful tool in music and voice recognition, and sound recognition in general. This paper is focused on investigating what data we can use along with MFCCs to increase the accuracy of music genre classification. The results of this process are analyzed to gain insight into the characteristics of different music genres. In this paper, MFCCs are considered for music genre classification using a multilayer perceptron (MLP) neural network. Measuring the effect of augmenting MFCCs with additional audio features at the input of the MLP. Following this there is an analysis of the effects different features, e.g. zero-crossing rate, spectral bandwidth etc., have on the accuracy of classifying genres and what the results show about the similarities and relatedness of music genres. Finally, an analysis of the results of classifying a selection of songs from the metal genre." @default.
- W3049691031 created "2020-08-21" @default.
- W3049691031 creator A5035470696 @default.
- W3049691031 date "2020-01-01" @default.
- W3049691031 modified "2023-09-27" @default.
- W3049691031 title "Enhancing music genre classification with neural networks by using extracted musical features" @default.
- W3049691031 hasPublicationYear "2020" @default.
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