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Multi-Label Annotation of MusicAuthors: Hiba Ahsan,Vijay Kumar, C V Jawahar Conference: Eighth International Conference on Advances in Pattern Recognition (ICAPR 2015 2015) Date: 2015-01-04 Report no: IIIT/TR/2015/49 AbstractAutomatic 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. Full paper: pdf Centre for Visual Information Technology |
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