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Binaural Acoustic Scene Classification Using Wavelet Scattering, Parallel Ensemble Classifiers and Nonlinear Fusion

Title
Binaural Acoustic Scene Classification Using Wavelet Scattering, Parallel Ensemble Classifiers and Nonlinear Fusion
Type
Article in International Scientific Journal
Year
2022-02
Authors
Vahid Hajihashemi
(Author)
Other
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Abdorreza Alavi Gharahbagh
(Author)
Other
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Pedro Miguel Cruz
(Author)
Other
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Marta Campos Ferreira
(Author)
FEUP
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José J. M. Machado
(Author)
FEUP
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João Manuel R. S. Tavares
(Author)
FEUP
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Journal
Title: SensorsImported from Authenticus Search for Journal Publications
Vol. 22 No. 4
Pages: 1-19
ISSN: 1424-3210
Publisher: MDPI
Indexing
Scientific classification
CORDIS: Technological sciences
FOS: Engineering and technology
Other information
Authenticus ID: P-00W-3MH
Abstract (EN): 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.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 19
Documents
File name Description Size
paper 1st Page 476.08 KB
sensors-22-01535 Paper 2270.07 KB
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