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Title: Multi-label annotation of music
Authors: Ahsan, H.
Kumar, V.
Jawahar, C.V.
Issue Date: 2015
Citation: ICAPR 2015 - 2015 8th International Conference on Advances in Pattern Recognition, 2015, Vol., , pp.-
Abstract: Automatic annotation of an audio or a music piece with multiple labels helps in understanding the composition of a music. Such meta-level information can be very useful in applications such as music transcription, retrieval, organization and personalization. In this work, we formulate the problem of annotation as multi-label classification which is considerably different from that of a popular single (binary or multi-class) label classification. We employ both the nearest neighbour and max-margin (SVM) formulations for the automatic annotation. We consider K-NN and SVM that are adapted for multi-label classification using one-vs-rest strategy and a direct multi-label classification formulation using ML-KNN and M3L. In the case of music, often the signatures of the labels (e.g. instruments and vocal signatures) are fused in the features. We therefore propose a simple feature augmentation technique based on non-negative matrix factorization (NMF) with an intuition to decompose a music piece into its constituent components. We conducted our experiments on two data sets - Indian classical instruments dataset and Emotions dataset [1], and validate the methods. � 2015 IEEE.
Appears in Collections:2. Conference Papers

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