Open AccessSource Separation and Depthwise Separable Convolutions for Computer Audition (Student Abstract)
Author(s) -
Gabriel Mersy,
Jin Hong Kuan
Publication year - 2021
Publication title -
proceedings of the aaai conference on artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2374-3468
pISSN - 2159-5399
DOI - 10.1609/aaai.v35i18.17920
Subject(s) - spectrogram , source separation , computer science , representation (politics) , convolutional neural network , set (abstract data type) , speech recognition , separation (statistics) , artificial intelligence , pattern recognition (psychology) , active listening , separable space , feature (linguistics) , machine learning , mathematics , mathematical analysis , linguistics , philosophy , communication , sociology , politics , political science , law , programming language
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