Abstract
Music Information Retrieval (MIR) is a particular research area of great interest because there are various strategies to retrieve music. To retrieve music, it is important to find a similarity between the input query and the matching music. Several solutions have been proposed that are currently being used in the application domain(s) such as Query- by-Example (QBE) which takes a sample of an audio recording playing in the background and retrieves the result. However, there is no efficient approach to solve this problem in a Query-by-Humming (QBH) application. In a Query-by-Humming application, the aim is to retrieve music that is most similar to the hummed query in an efficient manner. In this paper, I shall discuss the different music information retrieval techniques and their system architectures. Moreover, I will discuss the Query-by-Humming approach and its various techniques that allow for a novel method for music retrieval. Lastly, we conclude that the proposed system was effective combined with the MIDI dataset and custom hummed queries that were recorded from a sample of people. Although, the MRR was measured at 0.82 – 0.90 for only 100 songs in the database, the retrieval time was very high. Therefore, improving the retrieval time and Deep Learning approaches are suggested for future work.
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