Convolution neural network based automatic speech emotion recognition using Mel-frequency Cepstrum coefficients

Pawar, Manju Dhanraj; Kokate, Rajendra D.

doi:10.1007/s11042-020-10329-2

“…Secondly, a self-organization mechanism of the neurons (fuzzy rules) of the TS-type fuzzy neural network is proposed to optimize the structure of the model by three mechanisms: adding, merging, and deleting to improve the robustness and generalization ability of the model. Finally, to solve the problem that the stochastic gradient descent algorithm is easy to fall into the local optimal point, a set of adaptive learning mechanisms is proposed to enhance the fitting ability of the model [ 17 ]. Normalize the data.…”

Section: Analysis Of Music Intelligence Marketing Strategies For Variational Fuzzy Neural Network Algorithmsmentioning

confidence: 99%

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Sun

¹

2022

Computational Intelligence and Neuroscience

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In this paper, we use a variational fuzzy neural network algorithm to conduct an in-depth analysis and research on the optimization of music intelligent marketing strategy. The music recommendation system proposed in this paper includes a user modelling module, audio feature extraction module, and recommendation algorithm module. The basic idea of the recommendation algorithm is as follows: firstly, the historical behavioural information of music users is collected, and the user preference model is constructed by using the method of matrix decomposition of the hidden semantic model; then, the audio resources in the system are preprocessed and the spectrum map that can represent the music features is extracted; the similarity between the user’s preferred features and the music potential features are calculated to generate recommendations for the target user. The user-music dataset for model training and testing is constructed in-house, and the network model structure used for system experiments is designed based on a typical convolutional neural network model, while the model training tuning parameters are compared and selected. Finally, the model is trained and tested in this paper, and the system is evaluated in terms of both prediction rating accuracy and recommendation list generation accuracy using root mean square error, accuracy, recall, and F1 value as recommendation quality evaluation metrics. The experimental results show that the recommendation algorithm in this paper has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, this paper makes full use of the powerful advantage of deep neural networks to automatically extract features and obtain higher-level music feature representations from the audio content, while incorporating the historical behavioural information of user interactions with music, which can effectively alleviate the problems such as cold start in recommendation systems.

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“…The convolutional neural network(CNN) is a feedforward neural network that includes convolutional calculations and has a deep structure. It is one of the representative algorithms of deep learning.It and its improved models ([ 14 ]) have been proven to perform well in computer vision ([ 1 ]), natural language processing ([ 78 ]) and other fields. Table 2 shows some representative examples of using CNN for rumor detection tasks in the latest research and details the latest method.…”

Section: Model Structurementioning

confidence: 99%

Research status of deep learning methods for rumor detection

Tan

¹

,

Wang

²

,

Jia

³

et al. 2022

Multimed Tools Appl

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To manage the rumors in social media to reduce the harm of rumors in society. Many studies used methods of deep learning to detect rumors in open networks. To comprehensively sort out the research status of rumor detection from multiple perspectives, this paper analyzes the highly focused work from three perspectives: Feature Selection, Model Structure, and Research Methods. From the perspective of feature selection, we divide methods into content feature, social feature, and propagation structure feature of the rumors. Then, this work divides deep learning models of rumor detection into CNN, RNN, GNN, Transformer based on the model structure, which is convenient for comparison. Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods, based knowledge graph, and other methods for the first time. And compare the advantages of different methods to detect rumors. In addition, this review enumerate datasets available and discusses the potential issues and future work to help researchers advance the development of field.

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“…The convolutional neural network(CNN) is a feedforward neural network that includes convolutional calculations and has a deep structure. It is one of the representative algorithms of deep learning.It and its improved models ( K. Chen, Franko, and Sang (2021)) have been proven to perform well in computer vision ( Alam, Wang, Guangpei, Yunrong, and Chen (2021)), natural language processing ( Pawar and Kokate (2021)) and other fields. Table 2 shows some representative examples of using CNN for rumor detection tasks in the latest research and details the latest method.…”

Section: Rumor Detection Based On Cnnmentioning

confidence: 99%

Research Status of Deep Learning Methods for Rumor Detection

Tan,

Wang,

Jia

et al. 2022

Preprint

1

0

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To manage the rumors in social media to reduce the harm of rumors in society. Many studies used methods of deep learning to detect rumors in open networks. To comprehensively sort out the research status of rumor detection from multiple perspectives, this paper analyzes the highly focused work from three perspectives: Feature Selection, Model Structure, and Research Methods. From the perspective of feature selection, we divide methods into content feature, social feature, and propagation structure feature of the rumors. Then, this work divides deep learning models of rumor detection into CNN, RNN, GNN, Transformer based on the model structure, which is convenient for comparison. Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods, based knowledge graph, and other methods for the first time. And compare the advantages of different methods to detect rumors. In addition, this review enumerate datasets available and discusses the potential issues and future work to help researchers advance the development of field.

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Convolution neural network based automatic speech emotion recognition using Mel-frequency Cepstrum coefficients

Cited by 42 publications

References 25 publications

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Research status of deep learning methods for rumor detection

Research Status of Deep Learning Methods for Rumor Detection

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