Abstract
In this paper, a method of emotion recognition for musical instruments based on convolution long short time memory depth neural network is proposed, and an emotion recognition music database composed of four musical instruments is established, including keyboard instruments, wind instruments, string instruments and percussion instruments. The emotional types of these four instruments are divided into happiness, anger, sadness and fear. Through the establishment of CLDNN model based musical instrument emotion recognition architecture, MFCCs, CNN and CFS are used for feature extraction and training. The experimental structure shows that the best classification effect is to use long-term memory (LSTM) and deep neural network (DNN) to extract and combine the emotional feature sets of musical instruments, which has the highest accuracy. Considering the dynamic changes of musical features in musical instruments, the modeling method is used to predict the emotional changes of musical instruments.
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
