Emotion in the singing voice—a deeperlook at acoustic features in the light ofautomatic classification

Eyben, Florian; Salomão, Gláucia Laís; Sundberg, Johan; Scherer, Klaus R.; Schuller, Björn

doi:10.1186/s13636-015-0057-6

Cited by 26 publications

(19 citation statements)

References 19 publications

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“…Previous research suggests that the expression of emotions in the singing and speaking voice are related, 28 and that the same methods of emotion recognition apply to both. 29 As with the speaking voice, the voice quality in anger is easiest to recognize. This phenomenon might have an evolutionary underpinning, as it continues to be a useful skill to recognize potentially hazardous situations.…”

Section: Discussionmentioning

confidence: 99%

Emotion Recognition From Singing Voices Using Contemporary Commercial Music and Classical Styles

2019

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Section: Discussionmentioning

confidence: 99%

Emotion Recognition From Singing Voices Using Contemporary Commercial Music and Classical Styles

2019

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“…In the ML experiments, we employ the ComParE feature set (Schuller et al 2013), comprising 6373 acoustic features (Eyben et al 2015) computed by applying statistical functions to 65 Low-Level Descriptors (LLDs), extracted by the OPENSMILE feature extractor (Eyben et al 2010), and a Support Vector Machine (SVM) classifier with a linear kernel from the open-source toolkit LIBLINEAR (Fan et al 2008). Even though Deep Neural Networks (DNNs) are prevalent nowadays for ML tasks, in affective computing research their performance is not yet superior to rather classic ML procedures such as SVMs.…”

Section: Methodsmentioning

confidence: 99%

DEMoS: an Italian emotional speech corpus

Parada-Cabaleiro

Costantini

Batliner

et al. 2019

Lang Resources & Evaluation

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We present DEMoS (Database of Elicited Mood in Speech), a new, large database with Italian emotional speech: 68 speakers, some 9 k speech samples. As Italian is under-represented in speech emotion research, for a comparison with the state-of-theart, we model the 'big 6 emotions' and guilt. Besides making available this database for research, our contribution is three-fold: First, we employ a variety of mood induction procedures, whose combinations are especially tailored for specific emotions. Second, we use combinations of selection procedures such as an alexithymia test and self-and external assessment, obtaining 1,5 k (proto-) typical samples; these were used in a perception test (86 native Italian subjects, categorical identification and dimensional rating). Third, machine learning techniques-based on standardised brute-forced openSMILE ComParE features and support vector machine classifiers-were applied to assess how emotional typicality and sample size might impact machine learning efficiency. Our results are three-fold as well: First, we show that appropriate induction techniques ensure the collection of valid samples, whereas the type of self-assessment employed turned out not to be a meaningful measurement. Second, emotional typicality-which shows up in an acoustic analysis of prosodic main features-in contrast to sample size is not an essential feature for successfully training machine learning models. Third, the perceptual findings demonstrate that the confusion patterns mostly relate to cultural rules and to ambiguous emotions.

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“…Acoustic Features. Acoustic measures of music have been extensively studied (Berenzweig et al 2004;Mckay and Fujinaga 2008;Eyben et al 2015). In this work, we employ openSMILE (Eyben et al 2013), an open-source multimedia feature extractor to extract the "emobase" set of 988-dimensional acoustic features.…”

Section: Music Feature Extractionmentioning

confidence: 99%

PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms

Jia

et al. 2020

AAAI

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With the rapid expansion of digital music formats, it's indispensable to recommend users with their favorite music. For music recommendation, users' personality and emotion greatly affect their music preference, respectively in a long-term and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users' long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotion-oriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users' personality and emotion. Extensive experiments on a large real-world dataset of 171,254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0.5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.

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Emotion in the singing voice—a deeperlook at acoustic features in the light ofautomatic classification

Cited by 26 publications

References 19 publications

Emotion Recognition From Singing Voices Using Contemporary Commercial Music and Classical Styles

Emotion Recognition From Singing Voices Using Contemporary Commercial Music and Classical Styles

DEMoS: an Italian emotional speech corpus

PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms

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