Binary Emotion Classification of Music Using Deep Neural Networks

Revathy, V. R.; Pillai, Anitha S.

doi:10.1007/978-3-030-96302-6_45

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…For emotion classification in [36], classification was performed on the Music4All dataset [37], using valence, danceability and energy as features. The classification is binary, with happy/sad classes.…”

Section: A Fully Connected Deep Neural Network (Fcdnn)mentioning

confidence: 99%

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

et al. 2023

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The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music Signal Processing has received an increasing amount of attention over the last decade. This work reviews the most recent developments in Deep Learning in Music signal processing. Two main applications that are discussed are Music Information Retrieval, which spans a plethora of applications, and Music Generation, which can fit a range of musical styles. After a review of both topics, several emerging directions are identified for future research.

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Section: A Fully Connected Deep Neural Network (Fcdnn)mentioning

confidence: 99%

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

et al. 2023

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“…This study is based on textual emotion classification, where the goal of emotion classification, an extended field of sentiment analysis, is to assign possible emotions to a piece of text that most accurately reflect the mental state of the author. There are three ways to solve the emotion classification problem based on the approach: (1) binary emotion classification detects whether an emotion is present or not [7], (2) multi-class emotion classification classifies an instance into one of the predefined set of n labels [8], [9], (3) multi-label emotion classification classifies a given instance as ''neutral or no emotion'' or one or more from a set of predefined n labels that best represent the mental state of the author [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic

Ameer

Bölücü

Sidorov³

et al. 2023

IEEE Access

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Social media is a widely used platform that provides a huge amount of user-generated content that can be processed to extract information about users' emotions. This has numerous benefits, such as understanding how individuals feel about certain news or events. It can be challenging to categorize emotions from text created on social media, especially when trying to identify several different emotions from a short text length, as in a multi-label classification problem. Most previous work on emotion classification has focused on deep neural networks such as Convolutional Neural Networks and Recurrent Neural Networks. However, none of these networks have used semantic and syntactic knowledge to classify multiple emotions from a text. In this study, semantic and syntactic aware graph attention networks were proposed to classify emotions from a text with multiple labels. We integrated semantic information in the graph attention network in the form of Universal Conceptual Cognitive Annotation and syntactic information in the form of dependency trees. Our extensive experimental results showed that our two models, UCCA-GAT (accuracy = 71.2) and Dep-GAT (accuracy = 68.7), were able to outperform the state-of-the-art performance on both the challenging SemEval-2018 E-c: Detecting Emotions (multi-label classification) English dataset (accuracy = 58.8) and GoEmotions dataset (accuracy = 65.9).

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Analyzing factors influencing the severity of occupational accidents in textile industry using decision tree algorithms

Mutlu

Altuntaş

2023

Cluster Comput

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Binary Emotion Classification of Music Using Deep Neural Networks

Cited by 4 publications

References 21 publications

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic

Analyzing factors influencing the severity of occupational accidents in textile industry using decision tree algorithms

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