Paper Info
Title
A Data-Driven Approach For Estimating The Time-Frequency Binary Mask
Abstract
The ideal binary mask, often used in robust speech recognition applications, requires an estimate of the local SNR in each time-frequency (T-F) unit. A data-driven approach is proposed for estimating the instantaneous SNR of each T-F unit. By assuming that the a priori SNR and a posteriori SNR are uniformly distributed within a small region, the instantaneous SNR is estimated by minimizing the localized Bayes risk. The binary mask estimator derived by the proposed approach is evaluated in terms of hit and false alarm rates. Compared to the binary mask estimator that uses the decision-directed approach to compute the SNR, the proposed data-driven approach yielded substantial improvements (up to 40%) in classification performance, when assessed in terms of a sensitivity metric which is based on the difference between the hit and false alarm rates.
Year
Venue
Keywords
2009
INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5
ideal binary mask, SNR estimation, Bayes risk
Field
DocType
Citations 
Data-driven,Pattern recognition,Computer science,Speech recognition,Time–frequency analysis,Artificial intelligence,Binary number
Conference
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Gibak Kim11037.38
Philipos C. Loizou299171.00