An Enhanced Emotion Recognition Algorithm Using Pitch Correlogram, Deep Sparse Matrix Representation and Random Forest Classifier

Hamsa, Shibani; Iraqi, Youssef; Shahin, Ismail; Werghi, Naoufel

doi:10.1109/access.2021.3086062

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…English and Arabic datasets were used and processed to extract features after noise reduction. The four different datasets used were the ESD private Arabic dataset and the SUSAS, RAVDESS, and SAVEE public English datasets, and an average accuracy of more than 80% was achieved [ 13 ]. S. Hamsa et al proposed an emotionally intelligent system to identify the emotion of an unknown speaker using energy, time, and spectral features for three distinct speech datasets of two different languages.…”

Section: Related Workmentioning

confidence: 99%

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

2023

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Emotion recognition becomes an important aspect in the development of human-machine interaction (HMI) systems. Positive emotions impact our lives positively, whereas negative emotions may cause a reduction in productivity. Emotionally intelligent systems such as chatbots and artificially intelligent assistant modules help make our daily life routines effortless. Moreover, a system which is capable of assessing the human emotional state would be very helpful to assess the mental state of a person. Hence, preventive care could be offered before it becomes a mental illness or slides into a state of depression. Researchers have always been curious to find out if a machine could assess human emotions precisely. In this work, a unimodal emotion classifier system in which one of the physiological signals, an electrocardiogram (ECG) signal, has been used is proposed to classify human emotions. The ECG signal was acquired using a capacitive sensor-based non-contact ECG belt system. The machine-learning-based classifiers developed in this work are SVM and random forest with 10-fold cross-validation on three different sets of ECG data acquired for 45 subjects (15 subjects in each age group). The minimum classification accuracies achieved with SVM and RF emotion classifier models are 86.6% and 98.2%, respectively.

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Section: Related Workmentioning

confidence: 99%

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

2023

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“…The SARSA (state action reward state action) learning method is a complete state-action transition. When updating the current state action [ 18 ], this method does not use the state value function at the next moment but randomly selects actions to update the current state-action space according to a certain probability value and determines the execution action at the next moment when updating, which is the core of the SARSA method [ 19 ].The mathematical expression is described as follows:

…”

Section: Methodsmentioning

confidence: 99%

Research on Chorus Emotion Recognition and Intelligent Medical Application Based on Health Big Data

Chen

2022

Journal of Healthcare Engineering

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In chorus activities, the conductor leads chorus members to recreate music works. If you want to interpret music works perfectly with sound, emotion and emotional expression are particularly important. In this paper, a cloud HBD (health big data) integration system based on ensemble learning is designed to realize the high-efficiency and high-precision integration of HBD. An emotional speech database containing three emotions such as pleasure, calmness, and boredom is established, and the corpus problems such as emotional feature analysis and extraction needed for chorus emotion recognition research are solved. It also studies the classification and decision-making in emotional changes, and a DBN (deep belief network) chorus emotion recognition algorithm based on multiple emotional features is proposed. Feature DBN (Deep Belief Network) Chorus Emotion Recognition Algorithm This paper extracts various robust low-level features according to different features' ability to describe emotions and then feeds them into the DBN network to extract high-level feature descriptors. Then, the classification results of ELM (extreme learning machine) are voted and fused with the idea of ensemble learning, and the effectiveness of the algorithm is proved on three public datasets.

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“…Support vector machines are employed as a classifier in the final stage. Hamsa et al [18] proposed a speaker-independent & textindependent SER system for real applications where the speech is noisy and talking conditions like stressful. They modeled the work with the combination of the pre-processing stage as pitch-correlogram-based noise reduction, the feature representation method as sparse-dense decomposition, and Random Forest as a classifier.…”

Section: Literature Surveymentioning

confidence: 99%

Multi-Attribute Feature Extraction and Selection for Emotion Recognition from Speech through Machine Learning

Ramyasree¹,

Kumar²

2023

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Speech-based emotion recognition is still challenging due to its complexity despite being widely used in applications relating to emotions. In this paper, we developed a framework by considering three features: Prosodic features, Wavelet, and Spectral features. Under Prosodic, pitch and energy are considered, while under wavelet features, the approximation and detailed sub-bands ate fur scales are considered. Mel-Frequency Cepstral Coefficients (MFCC), Formants, and Long-Term Average Spectrum (LTAS) are all measured from speech signals as part of spectral features. Further, the significant features are selected based on nonlinear statistics, and dimensionality reduction is accomplished through Fisher Criterion. Spearman Rank Correlation is employed to find the nonlinear statistics under correlation analysis. For categorization, a Support Vector Machine and Decision Tree are used. The proposed method is simulated over RAVDESS, SAVEE, EMOVO, and URDU databases, and the observed recognition rates are approximately 79.66%, 88.99%, 87.68%, and 95.78%, respectively.

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An Enhanced Emotion Recognition Algorithm Using Pitch Correlogram, Deep Sparse Matrix Representation and Random Forest Classifier

Cited by 10 publications

References 37 publications

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

Research on Chorus Emotion Recognition and Intelligent Medical Application Based on Health Big Data

Multi-Attribute Feature Extraction and Selection for Emotion Recognition from Speech through Machine Learning

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