Classifying Emotions in Film Music—A Deep Learning Approach

Ciborowski, Tomasz; Reginis, Szymon; Weber, Dawid; Kurowski, Adam; Kostek, Bożena

doi:10.3390/electronics10232955

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…The experiments make use of the CAL500 (Turnbull 2007 forward) and CAL500exp ( [100]) datasets. In [101], classification is performed specifically for film music, with 9 emotional classes. Each class is also associated with specific colors.…”

Section: Convolutional Neural Network (Cnn)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: Convolutional Neural Network (Cnn)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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“…Deep convolutional neural networks (CNNs) achieve high performance in various computer vision tasks [1][2][3][4][5][6][7][8]. A general anatomy of CNN splits the architecture into two parts: a feature extractor and a classifier [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Exploiting Features with Split-and-Share Module

et al. 2022

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Deep convolutional neural networks (CNNs) have shown state-of-the-art performances in various computer vision tasks. Advances on CNN architectures have focused mainly on designing convolutional blocks of the feature extractors, but less on the classifiers that exploit extracted features. In this work, we propose Split-and-Share Module (SSM), a classifier that splits a given feature into parts, which are partially shared by multiple sub-classifiers. Our intuition is that the more the features are shared, the more common they will become, and SSM can encourage such structural characteristics in the split features. SSM can be easily integrated into any architecture without bells and whistles. We have extensively validated the efficacy of SSM on ImageNet-1K classification task, and SSM has shown consistent and significant improvements over baseline architectures. In addition, we analyze the effect of SSM using the Grad-CAM visualization.

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Intelligent Audio Signal Processing – Do We Still Need Annotated Datasets?

Kostek

2022

Intelligent Information and Database Systems

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Classifying Emotions in Film Music—A Deep Learning Approach

Cited by 7 publications

References 46 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

Exploiting Features with Split-and-Share Module

Intelligent Audio Signal Processing – Do We Still Need Annotated Datasets?

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