Voice Pathology Detection and Multi-classification Using Machine Learning Classifiers

Wu, Yuanyuan; Zhou, Changwei; Fan, Ziqi; Zhang, Yihua; Zhang, Xiaojun; Tao, Zhi

doi:10.1109/icsmd50554.2020.9261710

Cited by 8 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fast Fourier Transform (FFT) is employed to convert each frame of samples from the time domain into the frequency domain. To perform FFT, the input length is the next power of 2 from Nw, that is Nw < 2 p ⟶ 1103 < 2 11 . Hence, the length of FFT is nfft = 2048.…”

Section: B Mel-frequency Cepstral Coefficient (Mfcc)mentioning

confidence: 99%

“…In the sequential learning step, the output matrix of the hidden layer +1 will be updated for the new sample as shown in equation (11). Furthermore, the output weight matrix β k+1 will be updated according to the following equations:…”

Section: Online Sequential Extreme Learning Machine (Oselm)mentioning

confidence: 99%

“…Consequently, the study of speech signal processing of pathological voices by objective evaluation becomes an important topic for researchers as it aims to reduce medical laboratory work in diagnosing pathological speeches. Furthermore, it provides a non-invasive method of diagnosis which is more comfortable for patients, faster, as well as cost-effective [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

et al. 2021

View full text Add to dashboard Cite

In the last decade, the implementation of machine learning algorithms in the analysis of voice disorder is paramount in order to provide a non-invasive voice pathology detection by only using audio signal. In spite of that, most recent systems of voice pathology work on a limited acoustic database. In other words, the systems use one vowel, such as /a/, and ignore sentences and other vowels when analyzing the audio signal. Other key issues that should be considered in the systems are accuracy and time consumption of an algorithm. Online Sequential Extreme Learning Machine (OSELM) is one of the machine learning algorithms that can be regarded as a rapid and accurate algorithm in the classification process. Therefore, this paper presents a voice pathology detection and classification system by using OSELM algorithm as a classifier, and Mel-frequency cepstral coefficient (MFCC) as a featured extraction. In this work, the voice samples were taken from the Saarbrücken voice database (SVD). This system involves two parts of the database; the first part includes all voices in SVD with sentences and vowels /a/, /i/, and /u/, which are uttered in high, low, and normal pitches; and the second part utilizes voice samples of the common three types of pathologies (cyst, polyp, and paralysis) based on the vowel /a/ that is produced in normal pitch. The experimental results have shown that OSELM was able to achieve the highest accuracy up to 91.17%, 94% of precision, and 91% of recall. Furthermore, OSELM obtained 87%, 87.55%, and 97.67% for f-measure, G-mean, and specificity, respectively. The proposed system also presents a high ability to achieve detection and classification results in real-time clinical applications.

show abstract

Section: B Mel-frequency Cepstral Coefficient (Mfcc)mentioning

confidence: 99%

Section: Online Sequential Extreme Learning Machine (Oselm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In this work, we used the Radial Basis Function (RBF) kernel. Finally, for the optimization of the kernel parameters, c and gamma, we chose the grid search as an optimization method [7], [16], [32]- [34].…”

Section: Proposed Systemmentioning

confidence: 99%

“…The forward pass consists of passing the input layer by layer to the output neuron that will produce the true output of the network. The backward pass consists of detecting the error signal by deducting the desired output from the actual output [18], [34], [35].…”

Section: • Multilayer Perceptronmentioning

confidence: 99%

Voice Pathology Detection Using the Adaptive Orthogonal Transform Method, SVM and MLP.

Abakarim

Abenaou

2021

Int. J. Onl. Eng.

View full text Add to dashboard Cite

In this paper, an automatic voice pathology recognition system is realized. The special features are extracted by the Adaptive Orthogonal Transform method, and to provide their statistical properties we calculated the average, variance, skewness and kurtosis values. The classification process uses two models that are widely used as a classification method in the field of signal processing: Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The proposed system is tested by using a German voice database: the Saarbruecken Voice Database (SVD). The experimental results show that the Adaptive Orthogonal Transform method works perfectly with the Multilayer Perceptron Neural Network, which achieved 98.87% accuracy. On the other hand, the combination of the Adaptive Orthogonal Transform method and Support Vector Machine reached 85.79% accuracy.

show abstract

Fast Learning Network Algorithm for Voice Pathology Detection and Classification

Albadr,

Ayob,

Tiun

et al. 2024

Multimed Tools Appl

View full text Add to dashboard Cite

Voice Pathology Detection and Multi-classification Using Machine Learning Classifiers

Cited by 8 publications

References 17 publications

Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

Voice Pathology Detection Using the Adaptive Orthogonal Transform Method, SVM and MLP.

Fast Learning Network Algorithm for Voice Pathology Detection and Classification

Contact Info

Product

Resources

About