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Machine Learning | Music Genre Classification using SVM and Empirical Mode Decomposition
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Author and Presenter: Eiman Chaudhry
Project: Musica
Background:
Classification of music classes is very tricky in the field of music information retrieval. In this article, a novel approach is proposed. First, the audio signal is denoised and region of interest is extracted using Empirical Mode Decomposition (EMD) and first eight Intrinsic Mode Functions (IMFs) are selected and time and frequency domain features are extracted then linear Support Vector Machine (SVM-L) is trained to get the best accuracy of 94.0% with 5 genre i.e. blues, classic, hip-hop, metal, and pop.
#MachineLearningProject
#MachineLearningResearch
#ClassificationofMusicGenre
#MusicGenreClassification
#IntellCity
Project: Musica
Background:
Classification of music classes is very tricky in the field of music information retrieval. In this article, a novel approach is proposed. First, the audio signal is denoised and region of interest is extracted using Empirical Mode Decomposition (EMD) and first eight Intrinsic Mode Functions (IMFs) are selected and time and frequency domain features are extracted then linear Support Vector Machine (SVM-L) is trained to get the best accuracy of 94.0% with 5 genre i.e. blues, classic, hip-hop, metal, and pop.
#MachineLearningProject
#MachineLearningResearch
#ClassificationofMusicGenre
#MusicGenreClassification
#IntellCity
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