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2018 | OriginalPaper | Buchkapitel

Noise Masking Recurrent Neural Network for Respiratory Sound Classification

verfasst von : Kirill Kochetov, Evgeny Putin, Maksim Balashov, Andrey Filchenkov, Anatoly Shalyto

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018

Verlag: Springer International Publishing

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Abstract

In this paper, we propose a novel architecture called noise masking recurrent neural network (NMRNN) for lung sound classification. The model jointly learns to extract only important respiratory-like frames without redundant noise and then by exploiting this information is trained to classify lung sounds into four categories: normal, containing wheezes, crackles and both wheezes and crackles. We compare the performance of our model with machine learning based models. As a result, the NMRNN model reaches state-of-the-art performance on recently introduced publicly available respiratory sound database.

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Vorheriges Kapitel Learning Trends on the Fly in Time Series Data Using Plastic CGP Evolved Recurrent Neural Networks

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Titel: Noise Masking Recurrent Neural Network for Respiratory Sound Classification
verfasst von: Kirill Kochetov
Evgeny Putin
Maksim Balashov
Andrey Filchenkov
Anatoly Shalyto
Verlag: Springer International Publishing
Buch: Artificial Neural Networks and Machine Learning – ICANN 2018
Print ISBN: 978-3-030-01423-0

Electronic ISBN: 978-3-030-01424-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01424-7_21

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