Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice

Tuncer, Türker; Doğan, Şengül; Özyurt, Fatih; Belhaouari, Samir Brahim; Bensmail, Halima

doi:10.1109/access.2020.2992641

Cited by 89 publications

(42 citation statements)

References 44 publications

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“…These features are concatenated to generate a 7680-feature set. 5) Min-max normalization [44], [47] is applied before feature reduction and selection. 6) Weights of the 7680 features are calculated using NCA.…”

Section: Methodsmentioning

confidence: 99%

“…It calculates features using distance parameters like KNN [31], [48]. NCA generates nonnegative weights for all features [44], it uses a gradient-based optimizer for the best weights. NCA is a nonconvex optimization, such as a kmeans algorithm, so it is difficult to train [31].…”

Section: Methodsmentioning

confidence: 99%

“…One dimensional LTP (1D-LTP) has been improved from LTP for speech signal frames; it compares each value with its neighbors according to a threshold value. To compute the ternary pattern, a three-valued function is given by 5[42]- [44].…”

Section: B One Dimensional Local Ternary Patternmentioning

confidence: 99%

“…The magnitude differences between ( ± ) and are calculated. ( , , ) is divided into upper features in the nine blocks and lower features in the nine blocks by (6) and 7[42]- [44]. In Fig.…”

Section: B One Dimensional Local Ternary Patternmentioning

confidence: 99%

“…1D-LTP extracts 512 features from a signal, 256 features from the upper blocks and 256 features from the lower blocks. These features are computed and encoded through their decimal values and by (8) and 9[42]- [44].…”

Section: Inmentioning

confidence: 99%

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A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

Sönmez

Varol

2020

IEEE Access

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Interpreting a speech signal is quite challenging because it consists of different frequencies and features that vary according to emotions. Although different algorithms are being developed in the speech emotion recognition (SER) domain, the success rates vary according to the spoken languages, emotions, and databases. In this study, a new lightweight effective SER method has been developed that has low computational complexity. This method, called 1BTPDN, is applied on RAVDESS, EMO-DB, SAVEE, and EMOVO databases. First, low-pass filter coefficients are obtained by applying a one-dimensional discrete wavelet transform on the raw audio data. The features are extracted by applying textural analysis methods, a one-dimensional local binary pattern, and a one-dimensional local ternary pattern to each filter. Using neighborhood component analysis, the most dominant 1024 features are selected from 7680 features while the other features are discarded. These 1024 features are selected as the input of the classifier which is a thirddegree polynomial kernel-based support vector machine. The success rates of the 1BTPDN reached 95.16%, 89.16%, 76.67%, and 74.31% in the RAVDESS, EMO-DB, SAVEE, and EMOVO databases, respectively. The recognition rates are higher compared to many textural, acoustic, and deep learning state-of-the-art SER methods. INDEX TERMS Discrete wavelet transform, local binary pattern, local ternary pattern, neighborhood component analysis, speech emotion recognition.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: B One Dimensional Local Ternary Patternmentioning

confidence: 99%

Section: B One Dimensional Local Ternary Patternmentioning

confidence: 99%

Section: Inmentioning

confidence: 99%

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A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

Sönmez

Varol

2020

IEEE Access

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Hybrid‐Patch‐Alex: A new patch division and deep feature extraction‐based image classification model to detect COVID‐19, heart failure, and other lung conditions using medical images

Erdem,

Kobat,

Bilen

et al. 2023

Int J Imaging Syst Tech

Self Cite

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COVID-19, chronic obstructive pulmonary disease (COPD), heart failure (HF), and pneumonia can lead to acute respiratory deterioration. Prompt and accurate diagnosis is crucial for effective clinical management. Chest X-ray (CXR) and chest computed tomography (CT) are commonly used for confirming the diagnosis, but they can be time-consuming and biased. To address this, we developed a computationally efficient deep feature engineering model called Hybrid-Patch-Alex for automated COVID-19, COPD, and HF diagnosis. We utilized one CXR dataset and two CT image datasets, including a newly collected dataset with four classes: COVID-19, COPD, HF, and normal. Our model employed a hybrid patch division method, transfer learning with pre-trained AlexNet, iterative neighborhood component analysis for feature selection, and three standard classifiers (k-nearest neighbor, support vector machine, and artificial neural network) for automated classification. The model achieved high accuracy rates of 99.82%, 92.90%, and 97.02% on the respective datasets, using kNN and SVM classifiers.

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Optimal feature selection based speech emotion recognition using two‐stream deep convolutional neural network

Mustaqeem

Kwon

2021

Int J of Intelligent Sys

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Speech signal processing is an active area of research, the most dominant source of exchanging information among human beings, and the best way for human–computer interaction (HCI). Human behavior assessments and emotion recognition from a speech signal, such as speech emotion recognition (SER) is an emerging HCI area of exploration with various real time claims. The performance of an efficient SER system depends on feature learning, which include salient and discriminative information such as high‐level deep features. In this paper, we proposed a two‐stream deep convolutional neural network with an iterative neighborhood component analysis (INCA) to learn mutually spatial‐spectral features and select the most discriminative optimal features for the final prediction. Our model is composed of two channels, and each channel is associated with the convolutional neural network structure to extract cues from the oral signals. The first channel extracts feature from the spectral domain, and the second channel extracts features from the spatial domain, which are then fused and fed to the INCA to remove the severance and select the optimal features for the final model training. The joint refine features are passed from the fully connected network with a softmax classifier to yield the predictions of the different emotions. We trained our proposed system using three benchmarks, which included the EMO‐DB, SAVEE, and RAVDESS emotional speech corpora, and we tested the prediction performance to secure 95%, 82%, and 85% recognition rates. The performance of the system shows the effectiveness and significance of the proposed system.

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Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice

Cited by 89 publications

References 44 publications

A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

Hybrid‐Patch‐Alex: A new patch division and deep feature extraction‐based image classification model to detect COVID‐19, heart failure, and other lung conditions using medical images

Optimal feature selection based speech emotion recognition using two‐stream deep convolutional neural network

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