Detecting emotion in music

By Tao Li, Mitsunori Ogihara. International Society for Music Information Retrieval Conference, 2003

330 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b8d4e264a040ea6b2fbeabc51028f55cee0170de

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Citations in the corpus

State of the Art Report: Music Emotion Recognition: A State of the Art Review (2010, background): “In one of the ﬁrst publications on this topic, Li and Ogihara used acoustic features related to timbre, rhythm, and pitch to train support vector machines (SVMs) to classify music into one of 13 mood categories [60].”
Mining Multi-label Data (2010, background): “audio noise detection sound clip type (speech, noise) [26] emotion detection music clip emotions (relaxing-calm) [11, 14]”
Are the Emotions Expressed in Music Genre-specific? An Audio-based Evaluation of Datasets Spanning Classical, Film, Pop and Mixed Genres (2011, background): “For instance, Li and Ogihara (2003) used acoustic features related to timbre, rhythm, and pitch to train Support Vector Machines (SVMs) to classify music into 13 mood categories.”
A Survey of Audio-Based Music Classification and Annotation (2011, methodology): “• Genre Classification [1]–[19] • Mood Classification [12], [20]–[30]”
Multi-Label Classification: An Overview (2007, methodology): “Nowadays, we notice that multi-label classification methods are increasingly required by modern applications, such as protein function classification (Zhang & Zincir-Heywood, 2005), music categorization (Li & Ogihara, 2003) and semantic scene classification (Boutell et al., 2004).”
Embracing Error to Enable Rapid Crowdsourcing (2016, background): “er or not an input has a speciﬁc characteristic. Examples of these types of tasks are topic categorization (e.g., “Is this article about ﬁ- nance?”) [52], image classiﬁcation (e.g., “Is this a dog?”) [13, 38,36], audio styles [53] and emotion detection [36] in songs (e.g., “Is the music calm and soo”
Multi-Label Classification of Music into Emotions (2008, methodology): “The same 13 clusters as in [6] were used in [17], where the authors modiﬁed the k Nearest Neighbors algorithm in order to handle multilabel data directly.”
Scalable multi-label annotation (2014, background): “This technique is important in multiple domains, such as labeling actions in videos [11], news article topics [15], functional classes of genes [8], musical attributes or emotions in songs [12], semantic classes of scenes [2], product categories customers are likely to buy [21], and categories of we”
Random k -Labelsets: An Ensemble Method for Multilabel Classification (2007, background): “Multilabel classification is a challenging research problem that emerges in several modern applications such as music categorization [1], protein function classification [2–5] and semantic classification of images [6, 7].”