| Summary: | 碩士 === 國立中央大學 === 通訊工程學系 === 104 === In this work, we proposed a music retrieval system which can search the similar music in large-scale database.
Large-scale similar music recognition should calculate song-to-song simi-larity that can accommodate differences in timing, key and tempo. Simple vector distance measure is not powerful enough to perform the similar music recogni-tion task, but expensive solutions such as dynamic time warping do not scale to millions of instances, making the similar music recognition inappropriate for commercial-scale application. In this work, we used the content-based music features of songs as input and transformed them into semantic vectors by 2D-Fourier transform. We even explored different machine learning approaches to learn and reinforce the pattern of these semantic vector. By projecting the songs into the sematic vector space, we can use the efficient nearest neighbor algorithm to compare the similarity of songs and retrieve the most similar songs in the large-scale database.
The proposed system is not only efficient enough to perform scalable con-tent-based music retrieval, but also develop the potential of machine learning approaches, making the similar music recognition application more fast and accurate.
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