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Query based relevant music genre retrieval using adaptive artificial neural network for multimedia applications

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Abstract

In the case of digital music industry, current major internet stores contain millions of tracks, which complicate search, retrieval and discovery of music relevant for a user. To facilitate the advancement in multimedia applications, an efficient Query based Music Genre Retrieval (Q-MGR) strategy constructed by AANN (Adaptive Artificial Neural Network) is followed in this paper. Here, the proposed Q-MGR approach is done in three steps. Firstly, the few relevant features that are capable of distinguishing variety of signals are extracted. In second step, the AANN is trained with few music query signals to produce the prediction model for enabling the query based music retrieval. Here, the AANN is modelled to develop dynamic prediction model using Grasshopper Optimization algorithm (GOA), where, the optimal number of hidden layers and its neurons are found. Finally, the retrieval step is done with the predicted network model. Moreover, the proposed methodology is implemented in the working platform of MATLAB and the results are analysed with the recent literature works.

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Authors and Affiliations

  1. Department of Electronics & Telecommunication, SGGSIE&T, Nanded, Maharashtra, India

    Allabakash Isak Tamboli

  2. Department of Instrumentation & Control Engineering, College of Engineering, Pune, Maharashtra, India

    Rajendra D. Kokate

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  1. Allabakash Isak Tamboli
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  2. Rajendra D. Kokate
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Correspondence to Allabakash Isak Tamboli.

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Tamboli, A.I., Kokate, R.D. Query based relevant music genre retrieval using adaptive artificial neural network for multimedia applications. Multimed Tools Appl 81, 31603–31629 (2022). https://doi.org/10.1007/s11042-022-12351-y

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  • DOI: https://doi.org/10.1007/s11042-022-12351-y

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