A Method for Separating Knocking Sounds from Engine Radiation Noise by Deep Learning

Autor: Hikaru Watabe, Taro Kasahara
Rok vydání: 2023
Zdroj: INTER-NOISE and NOISE-CON Congress and Conference Proceedings. 265:2231-2238
ISSN: 0736-2935
DOI: 10.3397/in_2022_0319
Popis: 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.
Databáze: OpenAIRE