2015
DOI: 10.1007/978-3-319-24369-6_49
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Abstract: 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, rhy… Show more

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Cited by 11 publications
(8 citation statements)
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References 12 publications
(9 reference statements)
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“…Although there are some works that are focused only on prediction like [11], [29], [14] or [7], and that are some others centered only on solving classification problems like [28], [13], [44]; there are also several works that make predictions and later extend their systems to achieve classifications, such as [37] or [2]. Generally, these works that include prediction and classification use datasets annotated in dimensional models, in which a valence and arousal (V/A) coordinate system is established [34].…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Although there are some works that are focused only on prediction like [11], [29], [14] or [7], and that are some others centered only on solving classification problems like [28], [13], [44]; there are also several works that make predictions and later extend their systems to achieve classifications, such as [37] or [2]. Generally, these works that include prediction and classification use datasets annotated in dimensional models, in which a valence and arousal (V/A) coordinate system is established [34].…”
Section: Related Workmentioning
confidence: 99%
“…It can be observed that for some works no details on data balancing are incorporated, especially in prediction systems (see Table 1) in which it has no major relevance. Regarding classification systems (see Table 2), the data of the works [28], [13] and [44] are balanced and do not require any special treatment. Instead, [27] presents unbalance data, and although there is no detail of the strategy used to balance the data, apparently some songs are removed from the original dataset.…”
Section: Related Workmentioning
confidence: 99%
“…Karar ağaçları yorumlanması kolay, veri yapılarını ifade etmede başarılı, hızlı, ve güvenli olması nedeniyle yaygın kullanılan makine öğrenme algoritmalarındandır. Deneysel çalışmalar çoğu zaman Weka da gerçekleştirildiği için karar ağacı olarak J.48 algoritması [29] kullanılmaktadır [21]. Bu algoritma çalışmalarda sık karşımıza çıkan C4.5 [30] algoritmasına dayanır ki bu algoritma da ağaç oluşturma algoritması ID3'ün geliştirilmiş versiyonudur.…”
Section: Karar Ağaçlarıunclassified
“…• Cluster 1: passionate, enthusiastic, confident, bustling, noisy. Some works in which this library is applied are: in [25] for the classification of music by musical genre for a database of 120 songs, in [26] is used for the detection of 4 basic emotions through a categorical affective model that recognize emotions: happy, angry, sad, relaxed; additionally also the precision of the emotion recognition is validated, varying the features selection that are extracted and used in the classification model; and finally in [6] the library is used to detect the valence and arousal values in musical recordings, showing the importance of combining low-level features with highlevel features to achieve better results in emotional classifications of music. Table 5 presents a comparison between the three libraries selected in this article: Spotify API, jAudio and AcousticBrainz.…”
Section: Jmir/jaudiomentioning
confidence: 99%