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Automatic Music Genre Classification using Convolution ...
https://ieeexplore.ieee.org/document/8441340/#:~:text=Feature%20Extraction%20is%20the%20most%20crucial%20task%20for,into%20various%20genres%20by%20extracting%20the%20feature%20vector.
Audio Feature Engineering for Automatic Music Genre ...
https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.107.4
Automatic classification is a central activity to model most of these processes, thus its design plays a relevant role in advanced Music Information Retrieval. In this paper, we adopted a state-of-the-art machine learning algorithm, i.e. Support Vector Machines, to design an automatic classifier of music genres.
(PDF) Audio Feature Engineering for Automatic Music …
https://www.researchgate.net/publication/221510199_Audio_Feature_Engineering_for_Automatic_Music_Genre_Classification
Audio Feature Engineering for Automatic Music Genre Classification. ... Although music information processing such as music genre classification and audio melody extraction have been studied, most ...
Audio Feature Engineering for Automatic Music Genre ...
http://disi.unitn.it/moschitti/articles/RIAO2007.pdf
As we would like to classify songs stored as audio flles, i.e. waveforms, the design of features is quite complex and requires the application of signal analysis techniques. In this paper, we experimented a state-of-the-art machine learning algorithm, i.e. Support Vector Machines, in the design of an automatic genre classifler over audio infor-
Audio Feature Reduction and Analysis for Automatic Music ...
https://www.cs.sfu.ca/~li/papers-on-line/Baniya-SMC-2014.pdf
genre classification namely audio feature extraction and classifier design. In this paper, diverse audio features set have been proposed to characterize the music contents precisely. The feature sets belong to four different groups, i.e. dynamic, rhythm, spectral, and harmony. From the features, five different
Automatic Music Genre Classification using Convolution ...
https://ieeexplore.ieee.org/document/8441340/
Mel Frequency Cepstral Coefficient (MFCC) is used as a feature vector for sound sample. The proposed system classifies music into various genres by extracting the feature vector. Our results show that the accuracy level of our system is around 76% and it will greatly improve and facilitate automatic classification of music genres.
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