Recognition of Audio Depression Based on Convolutional Neural Network and Generative Antagonism Network Model

Wang, Zhiyong; Chen, Longxi; Wang, Lifeng; Diao, Guangqiang

doi:10.1109/access.2020.2998532

Cited by 29 publications

(8 citation statements)

References 30 publications

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“…We found that the KRR model with 40-dimensional principal components had the best performance, which was inputted with both facial and gaze features. Compared with some previous studies on auto recognition of depression, the proposed model has a smaller Mae value [ 39 , 40 ] and a larger p -value [ 41 ]. This shows that the model performed well.…”

Section: Discussionmentioning

confidence: 83%

Automatic Identification of a Depressive State in Primary Care

Liu

Wang

2022

Healthcare

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The Center for Epidemiologic Studies Depression Scale (CES-D) performs well in screening depression in primary care. However, people are looking for alternatives because it screens for too many items. With the popularity of social media platforms, facial movement can be recorded ecologically. Considering that there are nonverbal behaviors, including facial movement, associated with a depressive state, this study aims to establish an automatic depression recognition model to be easily used in primary healthcare. We integrated facial activities and gaze behaviors to establish a machine learning algorithm (Kernal Ridge Regression, KRR). We compared different algorithms and different features to achieve the best model. The results showed that the prediction effect of facial and gaze features was higher than that of only facial features. In all of the models we tried, the ridge model with a periodic kernel showed the best performance. The model showed a mutual fund R-squared (R2) value of 0.43 and a Pearson correlation coefficient (r) value of 0.69 (p < 0.001). Then, the most relevant variables (e.g., gaze directions and facial action units) were revealed in the present study.

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Section: Discussionmentioning

confidence: 83%

Automatic Identification of a Depressive State in Primary Care

Liu

Wang

2022

Healthcare

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“…The values of S k and W k are different scales and sliding Wireless Communications and Mobile Computing windows, and the calculation formula is [23], CNN [24], and DCNN [25]. The parameter settings of the network used in this research are shown in Table 1.…”

Section: Madn Methodmentioning

confidence: 99%

A Novel Smart Depression Recognition Method Using Human‐Computer Interaction System

Hou

Gao

2021

Wireless Communications and Mobile Computing

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In recent years, depression not only makes patients suffer from psychological pain such as self-blame but also has a high disability mortality rate. Early detection and diagnosis of depression and timely treatment of patients with different levels can improve the cure rate. Because there are quite a few potential depression patients who are not aware of their illness, some even suspect that they are sick but are unwilling to go to the hospital. In response to this situation, this research designed an intelligent depression recognition human-computer interaction system. The main contributions of this research are (1) the use of an audio depression regression model (DR AudioNet) based on a convolutional neural network (CNN) and a long-short-term memory network (LSTM) to identify the prevalence of depression patients. And it uses a multiscale audio differential normalization (MADN) feature extraction algorithm. The MADN feature describes the characteristics of nonpersonalized speech, and two network models are designed based on the MADN features of two adjacent segments of audio. Comparative experiments show that the method is effective in identifying depression. (2) Based on the research conclusion of the previous step, a human-computer interaction system is designed. After the user inputs his own voice, the final recognition result is output through the recognition of the network model used in this research. Visual operation is more convenient for users and has a practical application value.

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“…With a purpose of achieving higher accuracy in detection and classification process, some of the significant machine-learning based approaches are viz. identifying depression from audio using convolution neural network in Wang et al [27], Jazaery and Guo [28] applying deep learning on spatio-temporal attributes, Ding et al [29], and Tadesse et al [30] support vector machine over textual data, Cao et al [31] deep neural network for investigate the possibility of bipolar disorder on mobile usage, integrated usage of K-nearest neighborhood, support vector machine, Mahendran et al [32] random forest and multi-layer perceptron for constructing generalized model for stacking. The above-mentioned studies offer a productive guideline for modeling bipolar disorder while the pitfalls associated with it is highlighted in next section.…”

Section: Related Workmentioning

confidence: 99%

Stochastic modelling of transition dynamic of mixed mood episodes in bipolar disorder

Kumar¹,

Saxena²

2022

IJECE

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In the present state of health and wellness, mental illness is always deemed less importance compared to other forms of physical illness. In reality, mental illness causes serious multi-dimensional adverse effect to the subject with respect to personal life, social life, as well as financial stability. In the area of mental illness, bipolar disorder is one of the most prominent type which can be triggered by any external stimulation to the subject suffering from this illness. There diagnosis as well as treatment process of bipolar disorder is very much different from other form of illness where the first step of impediment is the correct diagnosis itself. According to the standard body, there are classification of discrete forms of bipolar disorder viz. type-I, type-II, and cyclothymic. Which is characterized by specific mood associated with depression and mania. However, there is no study associated with mixed-mood episode detection which is characterized by combination of various symptoms of bipolar disorder in random, unpredictable, and uncertain manner. Hence, the model contributes to obtain granular information with dynamics of mood transition. The simulated outcome of the proposed system in MATLAB shows that resulting model is capable enough for detection of mixed mood episode precisely

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Recognition of Audio Depression Based on Convolutional Neural Network and Generative Antagonism Network Model

Cited by 29 publications

References 30 publications

Automatic Identification of a Depressive State in Primary Care

Automatic Identification of a Depressive State in Primary Care

A Novel Smart Depression Recognition Method Using Human‐Computer Interaction System

Stochastic modelling of transition dynamic of mixed mood episodes in bipolar disorder

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