Paper Info
Title
Gabor-Based Nonuniform Scale-Frequency Map for Environmental Sound Classification in Home Automation
Abstract
This work presents a novel feature extraction approach called nonuniform scale-frequency map for environmental sound classification in home automation. For each audio frame, important atoms from the Gabor dictionary are selected by using the Matching Pursuit algorithm. After the system disregards phase and position information, the scale and frequency of the atoms are extracted to construct a scale-frequency map. Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are then applied to the scale-frequency map, subsequently generating the proposed feature. During the classification phase, a segment-level multiclass Support Vector Machine (SVM) is operated. Experiments on a 17-class sound database indicate that the proposed approach can achieve an accuracy rate of 86.21%. Furthermore, a comparison reveals that the proposed approach is superior to the other time-frequency methods.
Year
DOI
Venue
2014
10.1109/TASE.2013.2285131
IEEE T. Automation Science and Engineering
Keywords
Field
DocType
Atomic clocks,Accuracy,Matching pursuit algorithms,Feature extraction,Dictionaries,Support vector machines,Principal component analysis
Pattern recognition,Iterative method,Computer science,Support vector machine,Home automation,Feature extraction,Artificial intelligence,Linear discriminant analysis,Audio signal processing,Principal component analysis,Environmental sound classification
Journal
Volume
Issue
ISSN
11
2
1545-5955
Citations 
PageRank 
References 
18
0.74
20
Authors
4
Name
Order
Citations
PageRank
Jia-Ching Wang151558.13
Chang-Hong Lin2344.87
Bo-Wei Chen326230.12
Min-Kang Tsai4180.74