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Content-based retrieval has emerged in the face of content explosion as a promising approach to information access. In this paper, we focus on the challenging issue of recognizing the emotion content of music signals, or music emotion recognition (MER). Specifically, we formulate MER as a regression problem to predict the arousal and valence values (AV(More)
The performance of categorical music emotion classification that divides emotion into classes and uses audio features alone for emotion classification has reached a limit due to the presence of a semantic gap between the object feature level and the human cognitive level of emotion perception. Motivated by the fact that lyrics carry rich semantic(More)
Determining the emotion of a song that best characterizes the affective content of the song is a challenging issue due to the difficulty of collecting reliable ground truth data and the semantic gap between human's perception and the music signal of the song. To address this issue, we represent an emotion as a point in the Cartesian space with valence and(More)
Due to the subjective nature of human perception, classification of the emotion of music is a challenging problem. Simply assigning an emotion class to a song segment in a deterministic way does not work well because not all people share the same feeling for a song. In this paper, we consider a different approach to music emotion classification. For each(More)
It has been realized in the music emotion recognition (MER) community that personal difference, or individuality, has significant impact on the success of an MER system in practice. However, no previous work has explicitly taken individuality into consideration in an MER system. In this paper, the group-wise MER approach (GWMER) and personalized MER(More)
Music is composed to be emotionally expressive, and emotional associations provide an especially natural domain for indexing and recommendation in today's vast digital music libraries. But such libraries require powerful automated tools, and the development of systems for automatic prediction of musical emotion presents a myriad challenges. The perceptual(More)
The Emotion in Music task is held for the third consecutive year at the MediaEval benchmarking campaign. The unceasing interest towards the task shows that the music emotion recognition (MER) problem is truly important to the community, and there is a lot remaining to be discovered about it. Automatic MER methods could greatly improve the accessibility of(More)
The emergence of social tagging websites such as Last.fm has provided new opportunities for learning computational models that automatically tag music. Researchers typically obtain music tags from the Internet and use them to construct machine learning models. Nevertheless, such tags are usually noisy and sparse. In this paper, we present a preliminary(More)
The main objective of this work is to develop a music emotion recognition technique using Mel frequency cepstral coefficient (MFCC), Auto associative neural network (AANN) and support vector machine (SVM). The emotions taken are anger, happy, sad, fear, and neutral. Music database is collected at 44. 1 KHz with 16 bits per sample from various movies and(More)