Developing a benchmark for emotional analysis of music

Aljanaki, Anna; Yang, Yi; Soleymani, Mohammad

doi:10.1371/journal.pone.0173392

Cited by 182 publications

(117 citation statements)

References 40 publications

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“…The results in Section 3.2, though only indicative, pose some research questions that can form the basis of future work. The inconsistency in rating may be due to the small duration of the pieces: the authors of [21] ignore annotations of the first five seconds of music. With intra-rāga variation [8], the clipduration should be long enough for the emotion to be perceived, but short enough to evoke a single emotion.…”

Section: Discussionmentioning

confidence: 99%

A component-based approach to study the effect of Indian music on emotions

Viraraghavan¹,

Pal²,

Murthy³

et al. 2018

Workshop on Speech, Music and Mind (SMM 2018)

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The emotional impact of Indian music on human listeners has been studied mainly with respect to ragas. Although this approach aligns with the traditional and musicological views, some studies show that raga-specific effects may not be consistent. In this paper, we propose an alternative method of study based on the components of Indian Classical Music, which may be viewed as consisting of constant-pitch notes (CPNs) providing the context, and transients, the detail. One hundred concert pieces in four ragas each in Carnatic music (CM) and Hindustani music (HM) are analyzed to show that the transients are, on average, longer than CPNs. Further, the defined scale of the raga is not always mirrored in the CPNs for CM. We also draw upon the result that CPNs and transients scale non-uniformly when changing the tempo of CM pieces. Based on the observations and previous results on the emotional impact of the major and minor scales in Western music, we propose that the effect of CPNs and transients should be analyzed separately. We present a preliminary experiment that brings outs related challenges.

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Section: Discussionmentioning

confidence: 99%

A component-based approach to study the effect of Indian music on emotions

Viraraghavan¹,

Pal²,

Murthy³

et al. 2018

Workshop on Speech, Music and Mind (SMM 2018)

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“…The annotators were paid $8 per hour to rate the valence and arousal separately via the crowdsourcing platform Amazon's Mechanical Turk (MTurk), and the annotators' background was not considered. Each song was annotated by five to ten people, and we used the average of the annotations [37][38][39].…”

Section: Datasetmentioning

confidence: 99%

Emotion Classification System for Digital Music with a Cascaded Technique

2020

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Music selection is difficult without efficient organization based on metadata or tags, and one effective tag scheme is based on the emotion expressed by the music. However, manual annotation is labor intensive and unstable because the perception of music emotion varies from person to person. This paper presents an emotion classification system for digital music with a resolution of eight emotional classes. Russell’s emotion model was adopted as common ground for emotional annotation. The music information retrieval (MIR) toolbox was employed to extract acoustic features from audio files. The classification system utilized a supervised machine learning technique to recognize acoustic features and create predictive models. Four predictive models were proposed and compared. The models were composed by crossmatching two types of neural networks, i.e., Levenberg-Marquardt (LM) and resilient backpropagation (Rprop), with two types of structures: a traditional multiclass model and the cascaded structure of a binary-class model. The performance of each model was evaluated via the MediaEval Database for Emotional Analysis (DEAM) benchmark. The best result was achieved by the model trained with the cascaded Rprop neural network (accuracy of 89.5%). In addition, correlation coefficient analysis showed that timbre features were the most impactful for prediction. Our work offers an opportunity for a competitive advantage in music classification because only a few music providers currently tag music with emotional terms.

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“…MediaEval is a communitydriven benchmarking campaign dedicated to evaluating algorithms for social and human-centered multimedia access and retrieval [63]. Unlike MIREX, "Emotion in Music" task focused on dynamic emotion recognition in music tracking arousal and valence over time [6,115]. The data from MediaEval tasks were compiled in MediaEval Database for Emotional Analysis in Music (DEAM) which is the largest available dataset with dynamic annotations, at 2Hz, with valence and arousal annotations for 1,802 songs and song excerpts licensed under Creative Commons license.…”

Section: Datasets For Ac Of Musicmentioning

confidence: 99%

“…Mood clusters [32], dimensional representations such as arousal, tension and valence as well as music specific emotion representation can be used. An analysis of the methods proposed for MediaEval "Music in Emotion" task submissions revealed that using deep learning accounted for the superior performance for emotion recognition much more than the choice features [6]. Recent methods for emotion recognition in music rely on deep learning and often use spectrogram features that are converted to images [5].…”

Section: Affective Computing Of Musicmentioning

confidence: 99%

Affective Computing for Large-scale Heterogeneous Multimedia Data

Zhao

Wang

Soleymani

et al. 2019

ACM Trans. Multimedia Comput. Commun. Appl.

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The wide popularity of digital photography and social networks has generated a rapidly growing volume of multimedia data (i.e., image, music, and video), resulting in a great demand for managing, retrieving, and understanding these data. Affective computing (AC) of these data can help to understand human behaviors and enable wide applications. In this article, we survey the state-of-the-art AC technologies comprehensively for large-scale heterogeneous multimedia data. We begin this survey by introducing the typical emotion representation models from psychology that are widely employed in AC. We briefly describe the available datasets for evaluating AC algorithms. We then summarize and compare the representative methods on AC of different multimedia types, i.e., images, music, videos, and multimodal data, with the focus on both handcrafted features-based methods and deep learning methods. Finally, we discuss some challenges and future directions for multimedia affective computing.

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Developing a benchmark for emotional analysis of music

Cited by 182 publications

References 40 publications

A component-based approach to study the effect of Indian music on emotions

A component-based approach to study the effect of Indian music on emotions

Emotion Classification System for Digital Music with a Cascaded Technique

Affective Computing for Large-scale Heterogeneous Multimedia Data

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