Paper Info
Title
Model-Based Optimization Of A Low-Dimensional Modulation Filter Bank For Drr And T60 Estimation
Abstract
Amplitude Modulation Spectrum (AMS) features can be implemented as a cascade of two filter banks whereas the filter bandwidths can be optimized for a particular application. In this work we train AMS-based features using a combination of a model-based optimization (MBO) approach and feature selection for full-band DRR and full-band T-60 estimation. MBO replaces the computational complex data-based cost function by approximating a less complex surrogate model and thus reduces the time needed for training. We evaluate our approach on the publicly available ACE challenge corpus and achieve with only five features the best RMSE in the DRR estimation task using the single microphone configuration and upper mid-range performance for T-60 estimation. The computational complexity of our algorithm is much lower than all other submitted algorithms.
Year
DOI
Venue
2019
10.23919/EUSIPCO.2019.8902827
2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
Keywords
Field
DocType
DRR estimation, T-60 estimation, amplitude modulation spectrum, model-based optimization
Feature selection,Computer science,Filter bank,Mean squared error,Surrogate model,Algorithm,Cascade,Amplitude modulation,Microphone,Computational complexity theory
Conference
ISSN
Citations 
PageRank 
2076-1465
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Semih Agcaer131.07
Rainer Martin2102991.14