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Room acoustic parameter extraction from music signals

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conference contribution
posted on 02.12.2009, 12:33 by Paul Kendrick, Trevor J. Cox, Yonggang Zhang, Jonathon Chambers, Francis F. Li
A new method, employing machine learning techniques and a modified low frequency envelope spectrum estimator, for estimating important room acoustic parameters including reverberation time (RT) and early decay time (EDT) from received music signals has been developed. It overcomes drawbacks found in applying music signals directly to the envelope spectrum detector developed for the estimation of RT from speech signals. The octave band music signal is first separated into sub bands corresponding to notes on the equal temperament scale and the level of each note normalised before applying an envelope spectrum detector. A typical artificial neural network is then trained to map these envelope spectra onto RT or EDT. Significant improvements in estimation accuracy were found and further investigations confirmed that the non-stationary nature of music envelopes is a major technical challenge hindering accurate parameter extraction from music and the proposed method to some extent circumvents the difficulty

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School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

KENDRICK, P. ... et al, 2006. Room acoustic parameter extraction from music signals. IN: Proceedings of the 2006 IEEE Conference on Acoustics, Speech and Signal Processing. ICASSP 2006, Toulouse, 14-19 May 2006, Vol 5

Publisher

© IEEE

Version

VoR (Version of Record)

Publication date

2006

Notes

This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISBN

142440469X

ISSN

1520-6149

Language

en

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