Fundamentals of speech recognition
Fundamentals of speech recognition
An overview of audio information retrieval
Multimedia Systems - Special issue on audio and multimedia
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Multimedia content screening using a dual watermarking and fingerprinting system
Proceedings of the tenth ACM international conference on Multimedia
Time-series active search for quick retrieval of audio and video
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Robust spread-spectrum audio watermarking
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Digital Watermarks for Audio Signals
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
| Hi-index | 0.02 |
In this paper, we describe music monitoring in TV broadcasting based on content-based retrieval. A part of audio signals is sequentially extracted from TV broadcasting as a retrieval key, and a music DB that stores a great number of musical pieces is retrieved by this key based on content-based retrieval, and a musical piece is identified sequentially. In this way, we are able to carry out music monitoring. There are three necessary requirements important for realization of the music monitoring. They are robustness against non-stationary noise, real-time processing of large-scale music DB retrieval, and high granularity of the retrieval key. As a method of realizing robustness against non-stationary noise, we propose a partially similar retrieval method which improves retrieval accuracy by using the moment in which no superfluous noise is produced during the existence of non-stationary noise. In order to realize real-time processing of large-scale music DB retrieval, we adopt a coarse-to-fine strategy, and propose a spectral peaks hashing method which performs high-speed refining by using hashing. To calculate a hash value in this hashing, frequency channel numbers of the spectral peaks are used. In order to realize high granularity of the retrieval key, it is necessary to solve the problem of retrieval accuracy degradation associated with heightening the granularity. To improve this accuracy, we propose a detection-by-continuity method which uses music continuity. Moreover, by using music continuity to correct the starting point and the terminal point of a musical piece in TV broadcasting, the retrieval accuracy is improved further. In order to evaluate the effectiveness of the proposed methods, we performed experiments using a music DB which stores over 28,000 musical pieces (over 1800 hours) and TV broadcasting audio signals containing music and background music (BGM). The granularity of the retrieval key was set at about 0.5 seconds. Through these experiments, We verified that music monitoring was possible for over 90% of the total time of music and BGM used in TV broadcasting, and that real-time processing was possible.