Feature Engineering and Generation for Music Audio Data

Makarand Velankar, Parag Kulkarni

Abstract


Music is a rich harmonic audio signal with variety of forms and musical dimensions. The huge canvas of music evolved through centuries and decades involves variety of music genres and features. Different musical data representation, storage meth- ods and feature classification approaches helps to understand diversities and dimensions in musical features.This paper covers music data representation methods, musical features, feature engineering, generation, selection and learning methodologies used for musical data with example application for query by humming. The example chosen uses generic music features considering no need of familiarity with specific music genre. De- tailed discussion is provided for feature generation process with different approaches used. These feature engineering examples in music data analytics are useful in various applications of content based music information retrieval such as query by humming, similarity of music, clustering, music plagiarism etc. Enormous future growth of music data and the related challenges of feature engineering with new directions are covered in the concluding session.


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