Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/16115
Title: Complexity-scalable bit detection with MP3 audio bitstreams
Authors: ZHU JIA
Keywords: MP3, compressed domain, music processing, beat detection, low-power computation, memory- and energy-efficient algorithms, complexity-scalable
Issue Date: 22-Oct-2008
Source: ZHU JIA (2008-10-22). Complexity-scalable bit detection with MP3 audio bitstreams. ScholarBank@NUS Repository.
Abstract: With the growing popularity of MP3 audio format, handheld devices such as PDA and mobile phones have become important entertainment platforms. Unlike conventional audio equipments, mobile devices are characterized by limited processing power, battery life, and memory, as well as other constraints. Therefore, music processing algorithms with low complexity, such as beat detection, is essential to cope with the constraints of the mobile devices.This thesis presents a scheme of complexity scalable beat detection of pop music recordings, which can be run on different platforms, especially battery-powered handheld devices. We design a user friendly and platform adaptive scheme such that the detector complexity can be adjusted to match the constraints of the device and user requirements. The proposed algorithm provides both theoretical and practical contributions because we use the number of Huffman bits from the compressed bitstream without requiring any decoding as the sole feature for onset detection. Furthermore, we provide an efficient and robust graph-based beat induction algorithm. By applying the beat detector in the compressed domain, the system execution time can be reduced by almost three orders of magnitude. We have implemented and tested the algorithm on a PDA platform. Experimental results show that our beat detector offers significant advantages over other existing methods in execution time while maintaining satisfactory detection accuracy.
URI: http://scholarbank.nus.edu.sg/handle/10635/16115
Appears in Collections:Master's Theses (Open)

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