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This paper proposes a DNA sticker algorithm for parallel reduction over finite field GF(2 n

Melody extraction from polyphonic music is a valuable but difficult problem in music information retrieval. This paper proposes a system for automatic vocal melody extraction from polyphonic music recordings. Our approach is based on the pitch salience and the creation of the pitch contours. In the calculation of pitch salience, we reduce the peaks number of the spectral transform using a two-level filter and shrink the pitch range in accordance with the experiment to improve the efficiency of the system. In the singing voice detection, we adopt a three-step filter using the pitch contour characteristics and their distributions. The quantitative evaluation shows that our system not only keeps the overall accuracy compared with the state-of-the-art approaches submitted to MIREX, but also achieves high algorithm efficiency.

With the increasing scale of the melody database, the query-by-humming system faces the trade-offs between response speed and retrieval accuracy. Melody accurate matching is the key factor to restrict the response speed. In this paper, we present a GPU acceleration method for melody accurate matching, in order to improve the response speed without reducing retrieval accuracy. The method develops two parallel strategies (intra-task parallelism and inter-task parallelism) to obtain accelerated effects. The efficiency of our method is validated through extensive experiments. Evaluation results show that our single GPU implementation achieves 20x to 40x speedup ratio, when compared to a typical general purpose CPU's execution time.

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