Exploiting genre for music emotion classification

Lin, Yu‐Ching; Yang, Yi-Hsuan; Chen, Homer H.; Liao, I-Bin; Ho, Yeh-Chin

doi:10.1109/icme.2009.5202572

Cited by 13 publications

(12 citation statements)

References 5 publications

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“…Nevertheless, knowing the genre of a song can be helpful for mood classification as shown e.g. by or Lin, Yang, Chen, Liao, and Ho (2009), and therefore genre information should be added to the feature set.…”

Section: Genrementioning

confidence: 99%

‘Mister D.J., Cheer Me Up!’:Musical and Textual Features for Automatic Mood Classification

Schuller

Hage

Schuller³

et al. 2010

Journal of New Music Research

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Mass consumption of large collections of digital music asks for efficient and intuitive ways of organization. In this article, a system is presented which recognizes the evoked music mood on the basis of a wide variety of features, closely sticking to real world conditions. A twodimensional mood model is discussed in which moods resemble binary values for arousal and valence and an easy and thus user-friendly method is presented through which a fuzzy seven-class mood cluster is deducted. The songs of the 'Twenty Years of MTV Europe Most Wanted' music database consisting of recorded pop music tracks serve for evaluation of three groups of features: firstly, traditional features such as rhythm and tonal features, zero crossing rate, cepstral, and MPEG-7 Low Level Descriptors for audio content are extracted. Secondly, lyrics, chord sequences, and genre data are obtained from on-line sources. Thirdly, from all these, the high-level features, musical mode, and as a novel feature, the suited ballroom dance style, are created automatically. The features selected are data-driven, and Support Vector Machines are used for classification. Prediction accuracies of 77.4% for arousal and 72.9% for valence as well as 71.8% (including neighbours) for the seven-class cluster model are obtained pre-serving realism in terms of non-prototypical music selection and feature extraction throughout.

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Section: Genrementioning

confidence: 99%

‘Mister D.J., Cheer Me Up!’:Musical and Textual Features for Automatic Mood Classification

Schuller

Hage

Schuller³

et al. 2010

Journal of New Music Research

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“…Lee et al proposed a convolutional attention networks model to learn the features of both speech and text data, and the proposed model obtained better results for classifying emotions in the benchmark datasets [37]. Some studies considered to use other mid-level or high-level audio features, such as chord progression and genre metadata in [27,[38][39][40]. Lin et al presented the association between genre and emotion, and proposed a two-layer scheme that could exploit the correlation for emotion classification [38].…”

Section: Introductionmentioning

confidence: 99%

“…Some studies considered to use other mid-level or high-level audio features, such as chord progression and genre metadata in [27,[38][39][40]. Lin et al presented the association between genre and emotion, and proposed a two-layer scheme that could exploit the correlation for emotion classification [38]. Schuller et al [40] incorporated genre, ballroom dance style, chord progression and lyrics to classify music emotion, and the experiments showed that most of the considered factors would improve the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Bimodal Emotion Recognition Model for Minnan Songs

Xiang

Dong

et al. 2020

Information

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Most of the existing research papers study the emotion recognition of Minnan songs from the perspectives of music analysis theory and music appreciation. However, these investigations do not explore any possibility of carrying out an automatic emotion recognition of Minnan songs. In this paper, we propose a model that consists of four main modules to classify the emotion of Minnan songs by using the bimodal data—song lyrics and audio. In the proposed model, an attention-based Long Short-Term Memory (LSTM) neural network is applied to extract lyrical features, and a Convolutional Neural Network (CNN) is used to extract the audio features from the spectrum. Then, two kinds of extracted features are concatenated by multimodal compact bilinear pooling, and finally, the concatenated features are input to the classifying module to determine the song emotion. We designed three experiment groups to investigate the classifying performance of combinations of the four main parts, the comparisons of proposed model with the current approaches and the influence of a few key parameters on the performance of emotion recognition. The results show that the proposed model exhibits better performance over all other experimental groups. The accuracy, precision and recall of the proposed model exceed 0.80 in a combination of appropriate parameters.

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“…Training machines to recognize emotions from music resembles training machines to recognize genres used by music: the significant amount of subjectivity [5]; the debates about whether genre is even in the music signal [6]; the assumed existence of abstract categories that are difficult if not impossible to systematize, to define directly with a set of clear unambiguous rules, or even to define indirectly by exemplars that are indisputably representative [7]; ground truths that are difficult if not impossible to generate for datasets that are deceptively simple to assemble; and the difficulty of evaluation [8]. Genre and mood have been shown to be correlated in some cases [1,9]; and some systems proposed for MER are just adapted from MGR [1,2]. Thus, since MGR is one of the most studied areas in music information research [2,10] -MGR has been called its "flagship application" [11] -we argue that the challenges MGR research faces in evaluation inform those faced in MER.…”

Section: Introductionmentioning

confidence: 99%

Evaluating music emotion recognition: Lessons from music genre recognition?

Sturm

2013

2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

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A fundamental problem with nearly all work in music genre recognition (MGR) is that evaluation lacks validity with respect to the principal goals of MGR. This problem also occurs in the evaluation of music emotion recognition (MER). Standard approaches to evaluation, though easy to implement, do not reliably differentiate between recognizing genre or emotion from music, or by virtue of confounding factors in signals (e.g., equalization). We demonstrate such problems for evaluating an MER system, and conclude with recommendations.

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Exploiting genre for music emotion classification

Cited by 13 publications

References 5 publications

‘Mister D.J., Cheer Me Up!’:Musical and Textual Features for Automatic Mood Classification

‘Mister D.J., Cheer Me Up!’:Musical and Textual Features for Automatic Mood Classification

Bimodal Emotion Recognition Model for Minnan Songs

Evaluating music emotion recognition: Lessons from music genre recognition?

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