Feature Extraction Based on the Non-Negative Matrix Factorization of Convolutional Neural Networks for Monitoring Domestic Activity With Acoustic Signals

Feature Extraction Based on the Non-Negative Matrix Factorization of Convolutional Neural Networks for Monitoring Domestic Activity With Acoustic Signals

In this paper, a feature extraction method is proposed based on the non-negative matrix factorization (NMF) for classifiers for monitoring domestic activities with acoustic signals. Most of the classifiers of the acoustic signals use data-independent spectral features (e.g., log-Mel spectrum and Mel...

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Bibliographic Details
Main Authors: Seokjin Lee, Hee-Suk Pang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Acoustic scene classification
convolutional neural networks
feature extraction
non-negative matrix factorization
Online Access:https://ieeexplore.ieee.org/document/9133398/

Internet

https://ieeexplore.ieee.org/document/9133398/

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