Relationships between emotional evaluation of music and acoustic fluctuation properties

Entertainment Computing – ICEC 2011

Ito

Kato

2011

Self Cite

Abstract. Recently, we have a lot of musical pieces due to a large capacity of storage. However, it would be difficult to select the song with bibliographic data as the capacity of the music database increases. Therefore, we proposed an emotional song selection system. In this study, the Acoustic -Emotion model was composed by relating the acoustic fluctuation features that can explain the time variation of music with the emotional evaluations of music obtained through the subjective evaluation experiments. Based on the model, the emotional evaluations of music were calculated from their acoustic features. Using the proposed system, user can select the song with the adjective words and their degrees.

Section: Acoustic -Emotion Modelmentioning

confidence: 90%

“…As the acoustic features of music, the thirty six fluctuation features were prepared, which concern the time variation of pitch, volume, and rhythm. They can be extracted from fluctuation spectrums calculated from music, and detailed in our previous works [4].…”

Section: Fluctuation Featuresmentioning

confidence: 99%

Automated Song Selection System Complying with Emotional Requests

Entertainment Computing – ICEC 2011

Ito

Kato

2011

Self Cite

“…Most existing studies targeting music and affections are designed to relate acoustic features with affections [1,2]. Surely, acoustic features influence affections toward music.…”

Section: Related Workmentioning

confidence: 99%

Detecting Attractive Phrases from Musical Lyrics Focusing on Typical Expressions on Lyrics

Kagita

Nishihara

et al. 2015

IJAE

Self Cite

Lyrics influence human affection toward music as same as the acoustics. All phrases in a lyric are not remembered; there are some "attractive phrases" in lyrics. This paper proposes a method to detect attractive phrases from musical lyrics focusing on typical expressions on lyrics that are uncommon in natural document. Through the interview on impression for lyrics, "uniqueness of co-occurred terms" and "repetition" were founded as typical expressions that significantly attracted human. Therefore, these expressions were modeled as mathematical features. The proposed method detected attractive phrases using support vector machine with the modeled features. The results of the model evaluation experiments showed the 69% accuracy and 86% precision. The detected results were compared and discussed with the key sentences detected by using the existing summarization methods. As the result of the comparison, it was concluded that the proposed method detected attractive phrases more accurate than the existing summarization methods.

“…In our previous studies, we proposed the musical fluctuation features that cover sound variation on music which is confirmed to influence human affective evaluation of music [5]; we use these features as acoustic features in the proposed system. Also, we use bibliographic data such as artist and album name as meta features.…”

Section: Introductionmentioning

confidence: 99%

Interactive Music Recommendation System for Adapting Personal Affection: IMRAPA

Tada

Lecture Notes in Computer Science

Kato

2012

Self Cite

Abstract.We have so various types of entertainment, and music is one of the most popular one. In this paper, we proposed music recommendation system that interactively adapts a user's personal affection with only a simple operation, in which both acoustic and meta features are used. The more a user uses the proposed system, the better the system adapts the user's personal affection and recommends the suitable songs. Through the evaluational experiment, we confirmed that the proposed system could recommend songs adapting user's personal affection even if the personal affection variated.