H.6.5.13. Signal processing
Zeynab Mohammadpoory; Mahda Nasrollahzadeh; Sakineh Asadi
Abstract
Nowadays, the recognition of emotions using speech signals has gained popularity because of its vast number of applications in different fields such as medicine, online marketing, online search engines, education systems, criminal investigations, traffic collisions, and more. Many researchers have adopted ...
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Nowadays, the recognition of emotions using speech signals has gained popularity because of its vast number of applications in different fields such as medicine, online marketing, online search engines, education systems, criminal investigations, traffic collisions, and more. Many researchers have adopted different methodologies to improve emotion classification accuracy using speech signals. This study presents a novel time-series-to-graph transformation framework for speech emotion recognition. Speech signals were segmented into overlapping windows, each converted into graphs, from which 16 structural features were extracted. Significant features were then selected via Minimum Redundancy Maximum Relevance (mRMR) and used to train four classifiers: random forest (RF), linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbors (KNN). Finally, a soft-voting ensemble strategy was employed to integrate their predictions, yielding improved classification performance. The proposed method achieved the highest sensitivity, specificity, and accuracy for the SAVEE database: 83.57%, 98.93%, and 98.16%, respectively. Similarly, for the EmoDB database, the highest values were 94.47%, 99.09%, and 98.40%, respectively. We also compared our results with other methods and found that our method outperformed state-of-the-art techniques in emotion classification.
H.6.5.13. Signal processing
Samira Moghani; Hossein Marvi; Zeynab Mohammadpoory
Abstract
This study introduces a novel classification framework based on Deep Orthogonal Non-Negative Matrix Factorization (Deep ONMF), which leverages scalogram representations of phonocardiogram (PCG) signals to hierarchically extract structural features crucial for detecting valvular heart diseases (VHDs). ...
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This study introduces a novel classification framework based on Deep Orthogonal Non-Negative Matrix Factorization (Deep ONMF), which leverages scalogram representations of phonocardiogram (PCG) signals to hierarchically extract structural features crucial for detecting valvular heart diseases (VHDs). Scalograms, generated via the Continuous Wavelet Transform (CWT), serve as the foundational input to the proposed feature extraction pipeline, which integrates them with Deep ONMF in a unified and segmentation-free architecture. The resulting scalogram–Deep ONMF framework is designed to hierarchically extract features through two complementary perspectives: Scale-Domain Analysis (SDA) and Temporal-Domain Analysis (TDA). These extracted features are then classified using shallow classifiers, with Random Forest (RF) achieving the best results, particularly when paired with SDA features based on the Bump wavelet. Experimental evaluations on two public PCG datasets—one with five heart sound classes and another with binary classification—demonstrate the effectiveness of the proposed method, achieving high classification accuracies of up to 98.40% and 97.23%, respectively, thereby confirming its competitiveness with state-of-the-art techniques. The results suggest that the proposed approach offers a practical and powerful solution for automated heart sound analysis, with potential applications beyond VHD detection.
H.6.5.13. Signal processing
Khadijeh Aghajani
Abstract
Voice Activity Detection (VAD) plays a vital role in various audio processing applications, such as speech recognition, speech enhancement, telecommunications, satellite phone, and noise reduction. The performance of these systems can be enhanced by utilizing an accurate VAD method. In this paper, multiresolution ...
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Voice Activity Detection (VAD) plays a vital role in various audio processing applications, such as speech recognition, speech enhancement, telecommunications, satellite phone, and noise reduction. The performance of these systems can be enhanced by utilizing an accurate VAD method. In this paper, multiresolution Mel- Frequency Cepstral Coefficients (MRMFCCs), their first and secondorder derivatives (delta and delta2), are extracted from speech signal and fed into a deep model. The proposed model begins with convolutional layers, which are effective in capturing local features and patterns in the data. The captured features are fed into two consecutive multi-head self-attention layers. With the help of these two layers, the model can selectively focus on the most relevant features across the entire input sequence, thus reducing the influence of irrelevant noise. The combination of convolutional layers and self-attention enables the model to capture both local and global context within the speech signal. The model concludes with a dense layer for classification. To evaluate the proposed model, 15 different noise types from the NoiseX-92 corpus have been used to validate the proposed method in noisy condition. The experimental results show that the proposed framework achieves superior performance compared to traditional VAD techniques, even in noisy environments.
H.6.5.13. Signal processing
F. Sabahi
Abstract
Frequency control is one of the key parts for the arrangement of the performance of a microgrid (MG) system. Theoretically, model-based controllers may be the ideal control mechanisms; however, they are highly sensitive to model uncertainties and have difficulty with preserving robustness. The presence ...
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Frequency control is one of the key parts for the arrangement of the performance of a microgrid (MG) system. Theoretically, model-based controllers may be the ideal control mechanisms; however, they are highly sensitive to model uncertainties and have difficulty with preserving robustness. The presence of serious disturbances, the increasing number of MG, varying voltage supplies of MGs, and both independent operations of MGs and their interaction with the main grid makes the design of model-based frequency controllers for MGs become inherently challenging and problematic. This paper proposes an approach that takes advantage of interval Type II fuzzy logic for modeling an MG system in the process of its robust H∞ frequency control. Specifically, the main contribution of this paper is that the parameters of the MG system are modeled by interval Type-II fuzzy system (IT2FS), and simultaneously MG deals with perturbation using H∞ index to control its frequency. The performance of the microgrid equipped with the proposed modeling and controller is then compared with the other controllers such as H2 and μ-synthesis during changes in the microgrid parameters and occurring perturbations. The comparison shows the superiority and effectiveness of the proposed approach in terms of robustness against uncertainties in the modeling parameters and perturbations.
H.6.5.13. Signal processing
M. Asadolahzade Kermanshahi; M. M. Homayounpour
Abstract
Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There ...
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Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in DNN-based phoneme recognition systems including training and testing. Most previous research attempted to improve training phase such as training algorithms, different types of network, network architecture, feature type, etc. But in this study, we focus on test phase which is related to generate phoneme sequence that is also essential to achieve good phoneme recognition accuracy. Past research used Viterbi algorithm on hidden Markov model (HMM) to generate phoneme sequences. We address an important problem associated with this method. To deal with the problem of considering geometric distribution of state duration in HMM, we use real duration probability distribution for each phoneme with the aid of hidden semi-Markov model (HSMM). We also represent each phoneme with only one state to simply use phonemes duration information in HSMM. Furthermore, we investigate the performance of a post-processing method, which corrects the phoneme sequence obtained from the neural network, based on our knowledge about phonemes. The experimental results using the Persian FarsDat corpus show that using extended Viterbi algorithm on HSMM achieves phoneme recognition accuracy improvements of 2.68% and 0.56% over conventional methods using Gaussian mixture model-hidden Markov models (GMM-HMMs) and Viterbi on HMM, respectively. The post-processing method also increases the accuracy compared to before its application.
