Skip to the content

Room acoustic parameter extraction from music signals

Kendrick, P, Cox, TJ, Zhang, Y, Chambers, JA and Li, FF 2006, 'Room acoustic parameter extraction from music signals' , Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on (Volume:2 ), 5 , V-801.

[img] PDF - Published Version
Restricted to Repository staff only

Download (129kB) | Request a copy

Abstract

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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on (Volume:2 )
Publisher: IEEE
ISSN: 1520-6149
Related URLs:
Funders: University of Salford
Depositing User: FF Li
Date Deposited: 11 May 2016 14:24
Last Modified: 11 May 2016 14:24
URI: http://usir.salford.ac.uk/id/eprint/38945

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)

Downloads

Downloads per month over past year