DocumentCode :
1037680
Title :
A Quick Search Method for Audio Signals Based on a Piecewise Linear Representation of Feature Trajectories
Author :
Kimura, Akisato ; Kashino, Kunio ; Kurozumi, Takayuki ; Murase, Hiroshi
Author_Institution :
NTT Commun. Sci. Labs., NTT Corp., Atsugi
Volume :
16
Issue :
2
fYear :
2008
Firstpage :
396
Lastpage :
407
Abstract :
This paper presents a new method for a quick similarity-based search through long unlabeled audio streams to detect and locate audio clips provided by users. The method involves feature-dimension reduction based on a piecewise linear representation of a sequential feature trajectory extracted from a long audio stream. Two techniques enable us to obtain a piecewise linear representation: the dynamic segmentation of feature trajectories and the segment-based Karhunen-Loeve (KL) transform. The proposed search method guarantees the same search results as the search method without the proposed feature-dimension reduction method in principle. Experimental results indicate significant improvements in search speed. For example, the proposed method reduced the total search time to approximately 1/12 that of previous methods and detected queries in approximately 0.3 s from a 200-h audio database.
Keywords :
Karhunen-Loeve transforms; audio signal processing; feature extraction; piecewise linear techniques; signal representation; audio signals; audio streams; dynamic segmentation; feature-dimension reduction; long unlabeled audio streams; piecewise linear representation; quick similarity-based search; segment-based Karhunen-Loeve transform; sequential feature trajectory; Content based retrieval; Feature extraction; Fingerprint recognition; Hidden Markov models; Music information retrieval; Piecewise linear approximation; Piecewise linear techniques; Robustness; Search methods; Streaming media; Audio fingerprinting; audio retrieval; content identification; dynamic segmentation; feature trajectories; piecewise linear representation;
fLanguage :
English
Journal_Title :
Audio, Speech, and Language Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1558-7916
Type :
jour
DOI :
10.1109/TASL.2007.912362
Filename :
4432644
Link To Document :
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