Paper Info
Title
Binaural Acoustic Scene Classification Using Wavelet Scattering, Parallel Ensemble Classifiers and Nonlinear Fusion
Abstract
The analysis of ambient sounds can be very useful when developing sound base intelligent systems. Acoustic scene classification (ASC) is defined as identifying the area of a recorded sound or clip among some predefined scenes. ASC has huge potential to be used in urban sound event classification systems. This research presents a hybrid method that includes a novel mathematical fusion step which aims to tackle the challenges of ASC accuracy and adaptability of current state-of-the-art models. The proposed method uses a stereo signal, two ensemble classifiers (random subspace), and a novel mathematical fusion step. In the proposed method, a stable, invariant signal representation of the stereo signal is built using Wavelet Scattering Transform (WST). For each mono, i.e., left and right, channel, a different random subspace classifier is trained using WST. A novel mathematical formula for fusion step was developed, its parameters being found using a Genetic algorithm. The results on the DCASE 2017 dataset showed that the proposed method has higher classification accuracy (about 95%), pushing the boundaries of existing methods.
Year
DOI
Venue
2022
10.3390/s22041535
SENSORS
Keywords
DocType
Volume
urban sounds classification, stereo signal, sound base intelligent system, machine learning, genetic algorithm
Journal
22
Issue
ISSN
Citations 
4
1424-8220
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Vahid Hajihashemi100.34
Abdorreza Alavi Gharahbagh200.34
Pedro Miguel Cruz300.34
Marta Campos Ferreira400.34
José J M Machado501.35
João Manuel R. S. Tavares660362.85