Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150525
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dc.contributor.authorXiong, Ziqinen_US
dc.date.accessioned2021-06-17T23:59:36Z-
dc.date.available2021-06-17T23:59:36Z-
dc.date.issued2021-
dc.identifier.citationXiong, Z. (2021). Sound recognition from machine learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150525en_US
dc.identifier.urihttps://hdl.handle.net/10356/150525-
dc.description.abstractAudio event detection has always been a hot research field of acoustics com- bined with computer science. The research of audio event detection has impor- tant academic significance and commercial value. With the development of ma- chine learning and artificial intelligence, more and more machine learning and deep learning methods have been used in audio event detection, and achieved good results. In recent years, the success of self attention mechanism in the field of computer vision and natural language processing proves that self atten- tion mechanism has great potential. In this dissertation, self attention mechanism (CBAM) is transferred to the field of audio event detection, and theoretical ex- ploration and experimental proof are carried out.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleSound recognition from machine learningen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorJiang Xudongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Signal Processing)en_US
dc.contributor.supervisoremailEXDJiang@ntu.edu.sgen_US
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