Emotion Based MIDI Files Retrieval System

Grekow, Jacek; Raś, Zbigniew W.

doi:10.1007/978-3-642-11674-2_12

Cited by 8 publications

(2 citation statements)

References 11 publications

(2 reference statements)

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“…They build accurate models relying on a large amount of labeled data. In practice, however, labeled data is very hard to obtain in most genres except for Pop music [9] (automatic indexing systems can still be built given a genre [10]). This is because, human labor and "expert time" iare expensive.…”

Section: Music Emotionmentioning

confidence: 99%

SMART: Semi-Supervised Music Emotion Recognition with Social Tagging

Horner

et al. 2013

Proceedings of the 2013 SIAM International Conference on Data Mining

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Music emotion recognition (MER) aims to recognize the affective content of a piece of music, which is important for applications such as automatic soundtrack generation and music recommendation. MER is commonly formulated as a supervised learning problem. In practice, except for Pop music, there is little labeled data in most genres. In addition, emotion is genre specific in music and thus the labeled data of Pop music cannot be used for other genres. In this paper, we aim to solve the genre-specific MER problem by exploiting two kinds of auxiliary data: unlabeled songs and social tags. However, using these two kinds of data effectively is a non-trivial task, e.g. tags are noisy and therefore cannot be treated as fully trustworthy. To build an accurate model with the help from the unlabeled songs and noisy tags, we present SMART, which stands for Semi-Supervised Music Affective Emotion Recognition with Social Tagging, combining of a graph-based semisupervised learning algorithm with a novel tag refinement method. Experiments on the Million Song Dataset show that our proposed approach, trained with only 10 labeled instances, is as accurate as Support Vector Regression trained with 750 labeled songs.

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Section: Music Emotionmentioning

confidence: 99%

SMART: Semi-Supervised Music Emotion Recognition with Social Tagging

Horner

et al. 2013

Proceedings of the 2013 SIAM International Conference on Data Mining

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“…In the era of the Internet, searching music databases for emotions has become increasingly important. Automatic emotion detection enables indexing files in terms of emotions [1]. Automatic emotion detection also enables creating visual emotion maps of musical compositions [2].…”

Section: Introductionmentioning

confidence: 99%

Audio Features Dedicated to the Detection of Four Basic Emotions

Grekow

2015

Computer Information Systems and Industrial Management

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Part 9: Music Information Processing WorkshopInternational audienceIn this paper, we decided to study the effect of extracted audio features, using the analysis tool Essentia, on the quality of constructed music emotion detection classifiers. The research process included constructing training data, feature extraction, feature selection, and building classifiers. We selected features and found sets of features that were the most useful for detecting individual emotions. We examined the effect of low-level, rhythm and tonal features on the accuracy of the constructed classifiers. We built classifiers for different combinations of feature sets, which enabled distinguishing the most useful feature sets for individual emotions

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Emotion Based MIDI Files Retrieval System

Cited by 8 publications

References 11 publications

SMART: Semi-Supervised Music Emotion Recognition with Social Tagging

SMART: Semi-Supervised Music Emotion Recognition with Social Tagging

Audio Features Dedicated to the Detection of Four Basic Emotions

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