@article {Watabe:2023:0736-2935:2231,
title = "A Method for Separating Knocking Sounds from Engine Radiation Noise by Deep Learning",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2023",
volume = "265",
number = "5",
publication date ="2023-02-01T00:00:00",
pages = "2231-2238",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2023/00000265/00000005/art00029",
doi = "doi:10.3397/IN_2022_0319",
author = "Watabe, Hikaru and Kasahara, Taro",
abstract = "Knocking is the abnormal combustion of a gasoline engine, it generates a metallic noise. Engine knocking can damage the engine, so workers detect knocking by listening to the sound. There is a need to develop a way to automate this kind of work. We developed the deep learning model
which separates Knocking sound from engine radiation noise measured by a microphone. This model obtains the time-frequency mask from the paired data of engine emissions and cylinder pressure. The time-frequency mask enables the separation of knocking sound from engine radiation noise. By training
various rotation speeds, the proposed model can separate the knocking sound without training target engine speed.",
}