Abstract
This paper presents a method for extracting novel spectral features based on a sinusoidal model. The method is focused on characterizing the spectral shapes of audio signals using spectral peaks in frequency sub-bands. The extracted features are evaluated for predicting the levels of emotional dimensions, namely arousal and valence. Principal component regression, partial least squares regression, and deep convolutional neural network (CNN) models are used as prediction models for the levels of the emotional dimensions. The experimental results indicate that the proposed features include additional spectral information that common baseline features may not include. Since the quality of audio signals, especially timbre, plays a major role in affecting the perception of emotional valence in music, the inclusion of the presented features will contribute to decreasing the prediction error rate.
Highlights
Practical applications of music emotion recognition (MER) in modern electronic systems are becoming more prevalent
We applied the recent techniques in deep learning for classifying emotional states based on the arousal-valence 2D plane, and the classification accuracy of state-of-the-art deep learning models are reported
To enable fast and reliable emotion detection from music, the spectral features were extracted based on a sinusoidal model and evaluated for predicting the levels of arousal and valence in music
Summary
Practical applications of music emotion recognition (MER) in modern electronic systems are becoming more prevalent. One such practical application is improving human-robot interaction (HRI). A robot can perceive the emotional state or mood of a user via the facial expressions of the user and the types of music the user is listening to. Different chord progressions are used in different musical genres and associated with different emotional effects. Different songs with the same chord progression may have different emotional effects due to the different arrangements of musical instruments. A rock version of Mozart’s Symphony No 40 may be perceived differently in terms of emotions from the original version
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
