scholarly journals On the Comparison of Line Spectral Frequencies and Mel-Frequency Cepstral Coefficients Using Feedforward Neural Network for Language Identification

2018 ◽  
Vol 10 (1) ◽  
pp. 168 ◽  
Author(s):  
Teddy Surya Gunawan ◽  
Mira Kartiwi
Keyword(s):  
Neural Network ◽  
Recognition Rate ◽  
Language Identification ◽  
Feedforward Neural Network ◽  
Majority Voting ◽  
Mel Frequency Cepstral Coefficients ◽  
Frame Size ◽  
Audio Features ◽  
Cepstral Coefficients ◽  
The Many

<p>Of the many audio features available, this paper focuses on the comparison of two most popular features, i.e. line spectral frequencies (LSF) and Mel-Frequency Cepstral Coefficients. We trained a feedforward neural network with various hidden layers and number of hidden nodes to identify five different languages, i.e. Arabic, Chinese, English, Korean, and Malay. LSF, MFCC, and combination of both features were extracted as the feature vectors. Systematic experiments have been conducted to find the optimum parameters, i.e. sampling frequency, frame size, model order, and structure of neural network. The recognition rate per frame was converted to recognition rate per audio file using majority voting. On average, the recognition rate for LSF, MFCC, and combination of both features are 96%, 92%, and 96%, respectively. Therefore, LSF is the most suitable features to be utilized for language identification using feedforward neural network classifier.</p>

scholarly journals Atrial Fibrillation Detection Using Feedforward Neural Network

2021 ◽  
Author(s):  
Yunfan Chen ◽  
Chong Zhang ◽  
Chengyu Liu ◽  
Yiming Wang ◽  
Xiangkui Wan
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Deep Learning ◽  
Detection System ◽  
Recognition Rate ◽  
Feedforward Neural Network ◽  
Classification Model ◽  
Detection Sensitivity ◽  
Ecg Signal ◽  
Atrial Fibrillation Detection

Abstract Atrial fibrillation is one of the most common arrhythmias in clinics, which has a great impact on people's physical and mental health. Electrocardiogram (ECG) based arrhythmia detection is widely used in early atrial fibrillation detection. However, ECG needs to be manually checked in clinical practice, which is time-consuming and labor-consuming. It is necessary to develop an automatic atrial fibrillation detection system. Recent research has demonstrated that deep learning technology can help to improve the performance of the automatic classification model of ECG signals. To this end, this work proposes effective deep learning based technology to automatically detect atrial fibrillation. First, novel preprocessing algorithms of wavelet transform and sliding window filtering (SWF) are introduced to reduce the noise of the ECG signal and to filter high-frequency components in the ECG signal, respectively. Then, a robust R-wave detection algorithm is developed, which achieves 99.22% detection sensitivity, 98.55% positive recognition rate, and 2.25% deviance on the MIT-BIH arrhythmia database. In addition, we propose a feedforward neural network (FNN) to detect atrial fibrillation based on ECG records. Experiments verified by a 10-fold cross-validation strategy show that the proposed model achieves competitive detection performance and can be applied to wearable detection devices. The proposed atrial fibrillation detection model achieves an accuracy of 84.00%, the detection sensitivity of 84.26%, the specificity of 93.23%, and the area under the receiver working curve of 89.40% on the mixed dataset composed of Challenge2017 database and MIT-BIH arrhythmia database.

scholarly journals Vowel recognition of patients after total laryngectomy using Mel Frequency Cepstral Coefficients and mouth contour

2010 ◽  
Vol 20 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Rafal Pietruch ◽  
Antoni Grzanka
Keyword(s):  
Support Vector Machines ◽  
Total Laryngectomy ◽  
Vocal Tract ◽  
Recognition Rate ◽  
Video Signal ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Vector Machines ◽  
Cepstral Coefficients

The paper addresses a problem of isolated vowels recognition in patients following total laryngectomy. The visual and acoustic speech modalities were separately incorporated in the machine learning algorithms. The authors used the Mel Frequency Cepstral Coefficients as acoustic descriptors of a speech signal. A lip contour was extracted from a video signal of the speaking faces using OpenCV software library. In a vowels recognition procedure the three types of classifiers were used for comparison purposes: Artificial Neural Networks, Support Vector Machines and Naive Bayes. The highest recognition rate was evaluated using Support Vector Machines. For a group of the laryngectomees having a different quality of speech the authors achieved 75% for acoustic and 40% for visual recognition performances. The authors obtained higher recognition rate than in a previous research where 10 cross-sectional areas of a vocal tract were estimated. Using presented image processing algorithm the visual features can be extracted automatically from a video signal.

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