SMART: Semi-Supervised Music Emotion Recognition with Social Tagging

Horner, Andrew; Hu, Derek Hao; Wu, Bin; Yang, Qiang; Zhong, Erheng

doi:10.1137/1.9781611972832.31

Cited by 6 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each of us tried to describe the excerpt in a single one-word adjective, and the 14 words we used most frequently were selected as the 14 categories. It turns out that about half of the 14 words were used in our previous related studies [79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96] and most of the others were used in studies by other researchers [9,20,21,35,47]. All the categories included in the 4-quadrant model in Figure 1 appear in Figure 4 except Angry.…”

Section: Second Test: Best Word From 14 Categoriesmentioning

confidence: 99%

An Analysis of Low-Arousal Piano Music Ratings to Uncover What Makes Calm and Sad Music So Difficult to Distinguish in Music Emotion Recognition

Yu¹,

Chau²,

Horner³

2017

J. Audio Eng. Soc.

Self Cite

View full text Add to dashboard Cite

Music emotion recognition and recommendation systems often use a simplified 4-quadrant model with categories such as Happy, Sad, Angry, and Calm. Previous research has shown that both listeners and automated systems often have difficulty distinguishing low-arousal categories such as Calm and Sad. This paper seeks to explore what makes the categories Calm and Sad so difficult to distinguish. We used 300 low-arousal excerpts from the classical piano repertoire to determine the coverage of the categories Calm and Sad in the low-arousal space, their overlap, and their balance to one another. Our results show that Calm was 40% bigger in terms of coverage than Sad, but that on average Sad excerpts were significantly more negative in mood than Calm excerpts were positive. Calm and Sad overlapped in nearly 20% of the excerpts, meaning 20% of the excerpts were about equally Calm and Sad. Calm and Sad covered about 92% of the low-arousal space, where 8% of the space were holes that were not-at-all Calm or Sad. The largest holes were for excerpts considered Mysterious and Doubtful, but there were smaller holes among positive excerpts as well. Due to the holes in the coverage, the overlaps, and imbalances the Calm-Sad model adds about 6% more errors when compared to asking users directly whether the mood of the music is positive or negative. Nevertheless, the Calm-Sad model is still useful and appropriate for applications in music emotion recognition and recommendation such as when a simple and intuitive interface is preferred or when categorization is more important than precise differentiation.

show abstract

Section: Second Test: Best Word From 14 Categoriesmentioning

confidence: 99%

An Analysis of Low-Arousal Piano Music Ratings to Uncover What Makes Calm and Sad Music So Difficult to Distinguish in Music Emotion Recognition

Yu¹,

Chau²,

Horner³

2017

J. Audio Eng. Soc.

Self Cite

View full text Add to dashboard Cite

show abstract

Emotion-aware music tower blocks (EmoMTB ): an intelligent audiovisual interface for music discovery and recommendation

Melchiorre,

Penz,

Ganhör

et al. 2023

Int J Multimed Info Retr

View full text Add to dashboard Cite

Music listening has experienced a sharp increase during the last decade thanks to music streaming and recommendation services. While they offer text-based search functionality and provide recommendation lists of remarkable utility, their typical mode of interaction is unidimensional, i.e., they provide lists of consecutive tracks, which are commonly inspected in sequential order by the user. The user experience with such systems is heavily affected by cognition biases (e.g., position bias, human tendency to pay more attention to first positions of ordered lists) as well as algorithmic biases (e.g., popularity bias, the tendency of recommender systems to overrepresent popular items). This may cause dissatisfaction among the users by disabling them to find novel music to enjoy. In light of such systems and biases, we propose an intelligent audiovisual music exploration system named EmoMTB . It allows the user to browse the entirety of a given collection in a free nonlinear fashion. The navigation is assisted by a set of personalized emotion-aware recommendations, which serve as starting points for the exploration experience. EmoMTB adopts the metaphor of a city, in which each track (visualized as a colored cube) represents one floor of a building. Highly similar tracks are located in the same building; moderately similar ones form neighborhoods that mostly correspond to genres. Tracks situated between distinct neighborhoods create a gradual transition between genres. Users can navigate this music city using their smartphones as control devices. They can explore districts of well-known music or decide to leave their comfort zone. In addition, EmoMTB integrates an emotion-aware music recommendation system that re-ranks the list of suggested starting points for exploration according to the user’s self-identified emotion or the collective emotion expressed in EmoMTB ’s Twitter channel. Evaluation of EmoMTB has been carried out in a threefold way: by quantifying the homogeneity of the clustering underlying the construction of the city, by measuring the accuracy of the emotion predictor, and by carrying out a web-based survey composed of open questions to obtain qualitative feedback from users.

show abstract

Leveraging Unlabeled Data for Emotion Recognition With Enhanced Collaborative Semi-Supervised Learning

Deng

Ringeval

et al. 2018

IEEE Access

View full text Add to dashboard Cite

SMART: Semi-Supervised Music Emotion Recognition with Social Tagging

Cited by 6 publications

References 20 publications

An Analysis of Low-Arousal Piano Music Ratings to Uncover What Makes Calm and Sad Music So Difficult to Distinguish in Music Emotion Recognition

An Analysis of Low-Arousal Piano Music Ratings to Uncover What Makes Calm and Sad Music So Difficult to Distinguish in Music Emotion Recognition

Emotion-aware music tower blocks (EmoMTB ): an intelligent audiovisual interface for music discovery and recommendation

Leveraging Unlabeled Data for Emotion Recognition With Enhanced Collaborative Semi-Supervised Learning

Contact Info

Product

Resources

About