Effect on speech emotion classification of a feature selection approach using a convolutional neural network

Amjad, Ammar; Khan, Lal; Chang, Hsien-Tsung

doi:10.7717/peerj-cs.766

Cited by 24 publications

(10 citation statements)

References 60 publications

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“…is, however, has restricted the diversified development of music education [13][14][15]. Due to the multiculturalism need and the pluralistic society, teachers should use sufficient excellent traditional music knowledge and OES on the Internet to supplement students outside the classroom.…”

Section: Development and Utilization Of Music Oermentioning

confidence: 99%

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Xue

2022

Computational Intelligence and Neuroscience

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This work intends to classify and integrate music genres and emotions to improve the quality of music education. This work proposes a web image education resource retrieval method based on semantic network and interactive image filtering for a music education environment. It makes a judgment on these music source data and then uses these extracted feature sequences as the emotions expressed in the model of the combination of Long Short-Term Memory (LSTM) and Attention Mechanism (AM), thus judging the emotion category of music. The emotion recognition accuracy has increased after improving LSTM-AM into the BiGR-AM model. The greater the difference between emotion genres is, the easier it is to analyze the feature sequence containing emotion features, and the higher the recognition accuracy is. The classification accuracy of the excited, relieved, relaxed, and sad emotions can reach 76.5%, 71.3%, 80.8%, and 73.4%, respectively. The proposed interactive filtering method based on a Convolutional Recurrent Neural Network can effectively classify and integrate music resources to improve the quality of music education.

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Section: Development and Utilization Of Music Oermentioning

confidence: 99%

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Xue

2022

Computational Intelligence and Neuroscience

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“…Accordingly, researchers use random forest (RF) and ANN classifiers' performance scores as metrics of the collected emotional frequencies' efficacy. Identifying the most essential and discriminative characteristics for each SER was done using the feature selection (FS) method [20].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Role of Emotional Intelligence on the Performance of Small and Medium Enterprises (SMEs) Using AI-Based Convolutional Neural Networks (CNNs)

Serbaya

2022

Security and Communication Networks

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Human emotion detection is necessary for social interaction and plays an important role in our daily lives. Artificial intelligence research is rising, focusing on automated emotion detection. The capability to identify the emotion, which is considered one of the traits of emotional intelligence, is a component of human intelligence. Although the study is limited dependent on facial expressions or voice is flourishing, it is identifying emotions via body movements, a less researched issue. To attain emotional intelligence, this study suggests a deep learning approach. Here initially the video can be converted into image frames after the converted image frames can be preprocessed using the Glitter bandpass butter worth filter and contrast stretch histogram equalization. Then from the enhanced image, the features can be clustered using the hybrid Gaussian BIRCH algorithm. Then the specialized features are retrieved from the body of human gestures using the AdaDelta bacteria foraging optimization algorithm, and the selected features are fed to a supervised Kernel Boosting LENET deep-learning algorithm. The experiment is conducted using Geneva multimodal emotion portrayals (GEMEPs) corpus data set. This data set includes, human body gestures portraying the archetypes of five emotions, such as anger, fear, joy, pride, and sad. In these emotion detection techniques, the suggested Kernel Boosting LENET classifier achieves 98.5% accuracy, 94% precision, 95% sensitivity, and F-Score 93% outperformed better than the other existing classifiers. As a result, emotional acknowledgment may help small and medium enterprises (SMEs) to improve their performance and entrepreneurial orientation. The correlation coefficient of 188 and the significance coefficient of 0.00 show that emotional intelligence and SMEs performance have a significant and positive association.

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“…Furthermore, as recommended in [39], the experiments were performed using speaker-independent Leave-One-Speaker-Out (LOSO) or Leave-One-Speaker-Group-Out (LOSGO) techniques too. As we know, emotions are deeply linked to the identity of the speaker.…”

Section: Resultsmentioning

confidence: 99%

Speech Emotion Recognition using Sub-Band Spectrogram fusion and Deep Convolutional Neural Network transfer learning

Mansouri

Ghaffary

Harimi

2022

Preprint

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Speech emotion recognition (SER) is a challenging field of research that has attracted research during the last two decades. Successful performance of Deep Convolutional Neural Networks (DNNs) in various difficult pattern recognition problems motivates researchers to develop SER systems based on deep learning algorithms. The most essential requirement in training a deep model is the presence of a large-scale dataset. However, in many cases, such an amount of data is not available. Transfer learning approaches provide a practical solution to this problem. In this paper, we proposed an SER system based on AlexNet, the well-known deep model trained on the large-scale ImageNet dataset. In this way, the novel enriched spectrogram calculated based on the fusion of wide-band and narrow-band spectrograms is developed as a proper input for such a model. The proposed fused spectrogram benefited from both high temporal and spectral resolution. These images have been applied to the pre-trained AlexNet. All the experiments were performed on the popular Emo-DB, IEMOCAP, and eNTERFACE05 datasets based on 10-fold cross-validation and Leave-One-Speaker-Group-Out known as speaker-dependent and speaker-independent techniques, respectively. The proposed approach gains competent performance in contrast to other state-of-the-art methods.

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Effect on speech emotion classification of a feature selection approach using a convolutional neural network

Cited by 24 publications

References 60 publications

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Analyzing the Role of Emotional Intelligence on the Performance of Small and Medium Enterprises (SMEs) Using AI-Based Convolutional Neural Networks (CNNs)

Speech Emotion Recognition using Sub-Band Spectrogram fusion and Deep Convolutional Neural Network transfer learning

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