We present a study on the automatic classification of live and studio audio recordings, an important meta-information for music catalogue browsing and music recommendation systems. Several possible input representations (MFCCs, Mel spectrograms, VGGish) are combined with the classifiers GMM, SVM, and CNN to identify the most powerful approach. The results show that a CNN with VGGish input clearly outperforms other approaches and that its detection accuracy is high enough to be useful in practical applications.
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