標題: Robust online music identification using spectral entropy in the compressed domain
作者: Yin, Changqing
Li, Wei
Luo, Yuanqing
Tseng, Li-Chuan
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
關鍵字: Audio identification;Internet of Things;compressed-domain;MDCT spectral entropy;robustness;fragment retrieval
公開日期: 2014
摘要: Audio identification has been an active research field with wide applications for years. However, most of previously reported methods work on the raw audio format in spite of the fact that nowadays compressed format audio, especially MP3 music, has grown into the dominant way to transmit on the Internet. So far, most of the previous methods take advantage of MDCT coefficients or derived energy type of features. As a first attempt, in this paper we propose a novel audio fingerprinting algorithm utilizing compressed-domain spectral entropy as audio features. Such fingerprint exhibits strong robustness against various audio signal distortions such as recompression, noise interference, echo addition, equalization, band-pass filtering, pitch shifting, and moderate time-scale modification etc. In addition, the algorithm for compressed-domain can be applied in Internet of Things (IoT). Experimental results show that in our test database which is composed of 9823 popular songs, a 5s music clip is able to transmit in IoT and identify its original recording, with more than 90% top five precision rate even under the above severe time-frequency audio signal distortions.
URI: http://hdl.handle.net/11536/135269
ISBN: 978-1-4799-3086-9
ISSN: 2167-8189
期刊: 2014 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE WORKSHOPS (WCNCW)
起始頁: 128
結束頁: +
顯示於類別:會議論文