A Tutorial on MPEG/Audio Compression
IEEE MultiMedia
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Structural analysis of musical signals for indexing and thumbnailing
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
General sound classification and similarity in MPEG-7
Organised Sound
K-BOX: a query-by-singing based music retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
The Scientific Evaluation of Music Information Retrieval Systems: Foundations and Future
Computer Music Journal
A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures
Computer Music Journal
Performance of MPEG-7 low level audio descriptors with compressed data
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Three types of viewers' favorite music videos
Proceedings of the international conference on Advances in computer entertainment technology
Approximated clustering of distributed high-dimensional data
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Forward error correction strategies for media streaming over wireless networks
IEEE Communications Magazine
Audio classification based on MPEG-7 spectral basis representations
IEEE Transactions on Circuits and Systems for Video Technology
Optimal adaptive k-means algorithm with dynamic adjustment of learning rate
IEEE Transactions on Neural Networks
A survey of packet loss recovery techniques for streaming audio
IEEE Network: The Magazine of Global Internetworking
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Enjoyment of audio has now become about flexibility and personal freedom. Digital audio content can be acquired from many sources and wireless networking allows digital media devices and associated peripherals to be unencumbered by wires. However, despite recent improvements in capacity and quality of service, wireless networks are inherently unreliable communications channels for the streaming of audio, being susceptible to the effects of range, interference, and occlusion. This time-varying reliability of wireless audio transfer introduces data corruption and loss, with unpleasant audible effects that can be profound and prolonged in duration. Traditional communications techniques for error mitigation perform poorly and in a bandwidth inefficient manner in the presence of such large-scale defects in a digital audio stream. A novel solution that can complement existing techniques takes account of the semantics and natural repetition of music. Through the use of self-similarity metadata, missing or damaged audio segments can be seamlessly replaced with similar undamaged segments that have already been successfully received. We propose a technology to generate relevant self-similarity metadata for arbitrary audio material and to utilize this metadata within a wireless audio receiver to provide sophisticated and real-time correction of large-scale errors. The primary objectives are to match the current section of a song being received with previous sections while identifying incomplete sections and determining replacements based on previously received portions of the song. This article outlines our approach to Forward Error Correction (FEC) technology that is used to “repair” a bursty dropout when listening to time-dependent media on a wireless network. Using self-similarity analysis on a music file, we can “automatically” repair the dropout with a similar portion of the music already received thereby minimizing a listener's discomfort.