Pathological Voice Detection Using Transfer Learning Methods

2021 ◽  
Author(s):  
Zhang Yihua ◽  
Zhu Xincheng ◽  
Wu Yuanbo ◽  
Zhang Xiaojun ◽  
Xu Yishen ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Learning Methods ◽  
Voice Detection ◽  
Pathological Voice

scholarly journals Experimental Evaluation of Deep Learning Methods for an Intelligent Pathological Voice Detection System Using the Saarbruecken Voice Database

2021 ◽  
Vol 11 (15) ◽  
pp. 7149
Author(s):  
Ji-Yeoun Lee
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Linear Prediction ◽  
Detection System ◽  
Mel Frequency Cepstral Coefficients ◽  
Learning Methods ◽  
Acoustic Measures ◽  
Voice Detection ◽  
Voice Data ◽  
Pathological Voice

This work is focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for pathological voice detection using mel-frequency cepstral coefficients (MFCCs), linear prediction cepstrum coefficients (LPCCs), and higher-order statistics (HOSs) parameters. In total, 518 voice data samples were obtained from the publicly available Saarbruecken voice database (SVD), comprising recordings of 259 healthy and 259 pathological women and men, respectively, and using /a/, /i/, and /u/ vowels at normal pitch. Significant differences were observed between the normal and the pathological voice signals for normalized skewness (p = 0.000) and kurtosis (p = 0.000), except for normalized kurtosis (p = 0.051) that was estimated in the /u/ samples in women. These parameters are useful and meaningful for classifying pathological voice signals. The highest accuracy, 82.69%, was achieved by the CNN classifier with the LPCCs parameter in the /u/ vowel in men. The second-best performance, 80.77%, was obtained with a combination of the FNN classifier, MFCCs, and HOSs for the /i/ vowel samples in women. There was merit in combining the acoustic measures with HOS parameters for better characterization in terms of accuracy. The combination of various parameters and deep learning methods was also useful for distinguishing normal from pathological voices.

Deep Learning Based Classification of Time Series of Chaotic Systems over Graphic Images

2021 ◽  
Author(s):  
Süleyman UZUN ◽  
Sezgin KAÇAR ◽  
Burak ARICIOĞLU
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Transfer Learning ◽  
Chaotic Systems ◽  
Initial Conditions ◽  
Step Size ◽  
Learning Methods ◽  
Data Set ◽  
Graphic Images ◽  
Different Chaotic Systems

Abstract In this study, for the first time in the literature, identification of different chaotic systems by classifying graphic images of their time series with deep learning methods is aimed. For this purpose, a data set is generated that consists of the graphic images of time series of the most known three chaotic systems: Lorenz, Chen, and Rossler systems. The time series are obtained for different parameter values, initial conditions, step size and time lengths. After generating the data set, a high-accuracy classification is performed by using transfer learning method. In the study, the most accepted deep learning models of the transfer learning methods are employed. These models are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet and GoogLeNet. As a result of the study, classification accuracy is found between 96% and 97% depending on the problem. Thus, this study makes association of real time random signals with a mathematical system possible.

scholarly journals Methods and Algorithms for Knowledge Reuse in Multiagent Reinforcement Learning

2020 ◽  
Author(s):  
Felipe Leno Da Silva ◽  
Anna Helena Reali Costa
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Learning Process ◽  
Trial And Error ◽  
Knowledge Reuse ◽  
Previous Knowledge ◽  
Learning Methods ◽  
Types Of Knowledge ◽  
Learning Agent ◽  
Multiagent Reinforcement Learning

Reinforcement Learning (RL) is a powerful tool that has been used to solve increasingly complex tasks. RL operates through repeated interactions of the learning agent with the environment, via trial and error. However, this learning process is extremely slow, requiring many interactions. In this thesis, we leverage previous knowledge so as to accelerate learning in multiagent RL problems. We propose knowledge reuse both from previous tasks and from other agents. Several flexible methods are introduced so that each of these two types of knowledge reuse is possible. This thesis adds important steps towards more flexible and broadly applicable multiagent transfer learning methods.

scholarly journals Does BERT need domain adaptation for clinical negation detection?

2020 ◽  
Vol 27 (4) ◽  
pp. 584-591 ◽  
Author(s):  
Chen Lin ◽  
Steven Bethard ◽  
Dmitriy Dligach ◽  
Farig Sadeque ◽  
Guergana Savova ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Domain Adaptation ◽  
Fine Tuning ◽  
Adaptation Algorithm ◽  
Learning Methods ◽  
Clinical Text ◽  
Unsupervised Domain Adaptation ◽  
Adversarial Training ◽  
Negation Detection ◽  
Adaptation Method

Abstract Introduction Classifying whether concepts in an unstructured clinical text are negated is an important unsolved task. New domain adaptation and transfer learning methods can potentially address this issue. Objective We examine neural unsupervised domain adaptation methods, introducing a novel combination of domain adaptation with transformer-based transfer learning methods to improve negation detection. We also want to better understand the interaction between the widely used bidirectional encoder representations from transformers (BERT) system and domain adaptation methods. Materials and Methods We use 4 clinical text datasets that are annotated with negation status. We evaluate a neural unsupervised domain adaptation algorithm and BERT, a transformer-based model that is pretrained on massive general text datasets. We develop an extension to BERT that uses domain adversarial training, a neural domain adaptation method that adds an objective to the negation task, that the classifier should not be able to distinguish between instances from 2 different domains. Results The domain adaptation methods we describe show positive results, but, on average, the best performance is obtained by plain BERT (without the extension). We provide evidence that the gains from BERT are likely not additive with the gains from domain adaptation. Discussion Our results suggest that, at least for the task of clinical negation detection, BERT subsumes domain adaptation, implying that BERT is already learning very general representations of negation phenomena such that fine-tuning even on a specific corpus does not lead to much overfitting. Conclusion Despite being trained on nonclinical text, the large training sets of models like BERT lead to large gains in performance for the clinical negation detection task.

scholarly journals Combined Reinforcement Learning via Abstract Representations

2019 ◽  
Vol 33 ◽  
pp. 3582-3589 ◽  
Author(s):  
Vincent Francois-Lavet ◽  
Yoshua Bengio ◽  
Doina Precup ◽  
Joelle Pineau
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Transfer Learning ◽  
Computationally Efficient ◽  
Dimensional Representation ◽  
Learning Methods ◽  
Model Free ◽  
Abstract Representations ◽  
Low Dimensional ◽  
New Strategies

In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which opens up new strategies for interpretable AI, exploration and transfer learning.

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