Speech emotion classification using attention based network and regularized feature selection

IndoWaveSentiment: Indonesian audio dataset for emotion classification

Voice is a one of media for human communication and interaction. Emotions conveyed through voice, such as laughter or tears, can communicate messages more quickly than spoken or written language. In sentiment analysis, the emotional component is crucial for reflecting human perceptions and opinions. This paper introduces IndoWaveSentiment, a dataset of emotional voice recordings categorized into five classes: neutral, happy, surprised, disgusted, and disappointed. The data collection took place in a recording studio with ten actors, evenly split between men and women. Each actor repeated the same sentence in Bahasa Indonesia three times for each emotion class, and the recordings were saved in .wav format. The annotation process was manually conducted using Audacity and validated through a questionnaire-based sampling technique that supports audio data. This dataset is valuable for researchers in Signal Processing and Artificial Intelligence, aiding the development of classification models within Machine Learning.

Advanced differential evolution for gender-aware English speech emotion recognition

Speech emotion recognition (SER) technology involves feature extraction and prediction models. However, recognition efficiency tends to decrease because of gender differences and the large number of extracted features. Consequently, this paper introduces a SER system based on gender. First, gender and emotion features are extracted from speech signals to develop gender recognition and emotion classification models. Second, according to gender differences, distinct emotion recognition models are established for male and female speakers. The gender of speakers is determined before executing the corresponding emotion model. Third, the accuracy of these emotion models is enhanced by utilizing an advanced differential evolution algorithm (ADE) to select optimal features. ADE incorporates new difference vectors, mutation operators, and position learning, which effectively balance global and local searches. A new position repairing method is proposed to address gender differences. Finally, experiments on four English datasets demonstrate that ADE is superior to comparison algorithms in recognition accuracy, recall, precision, F1-score, the number of used features and execution time. The findings highlight the significance of gender in refining emotion models, while mel-frequency cepstral coefficients are important factors in gender differences.

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

Accurate identification of porcine cough plays a vital role in comprehensive respiratory health monitoring and diagnosis of pigs. It serves as a fundamental prerequisite for stress-free animal health management, reducing pig mortality rates, and improving the economic efficiency of the farming industry. Creating a representative multi-source signal signature for porcine cough is a crucial step toward automating its identification. To this end, a feature fusion method that combines the biological features extracted from the acoustic source segment with the deep physiological features derived from thermal source images is proposed in the paper. First, acoustic features from various domains are extracted from the sound source signals. To determine the most effective combination of sound source features, an SVM-based recursive feature elimination cross-validation algorithm (SVM-RFECV) is employed. Second, a shallow convolutional neural network (named ThermographicNet) is constructed to extract deep physiological features from the thermal source images. Finally, the two heterogeneous features are integrated at an early stage and input into a support vector machine (SVM) for porcine cough recognition. Through rigorous experimentation, the performance of the proposed fusion approach is evaluated, achieving an impressive accuracy of 98.79% in recognizing porcine cough. These results further underscore the effectiveness of combining acoustic source features with heterogeneous deep thermal source features, thereby establishing a robust feature representation for porcine cough recognition.