Yang Tan

• Master of Engineering
• Beijing Institute of Technology

Heart sounds play a crucial role in assessing Coronary Artery Disease (CAD). The advancement of Artificial Intelligence (AI) technologies has given rise to Computer Audition (CA)-based methods for CAD detection. However, previous research has focused primarily on analyzing and modeling heart sound data, overlooking practical application scenarios....

There are already many analyses of heart sounds used to develop diagnostic systems for the heart condition. However, the existing heart sound database state is not sufficient to train a large number of deep learning models and is not balanced-the normal heart sounds are always more present than abnormal heart sounds. Hence, we are interested in alg...

The development of affective computing and medical electronic technologies has led to the emergence of Artificial Intelligence (AI)-based methods for the early detection of depression. However, previous studies have often overlooked the necessity for the AI-assisted diagnosis system to be wearable and accessible in practical scenarios for depressio...

Heart sound auscultation has been applied in clinical usage for early screening of cardiovascular diseases. Due to the high demand for auscultation expertise, automatic auscultation can help with auxiliary diagnosis and reduce the burden of training professional clinicians. Nevertheless, there is a limit to classic machine learning’s performance im...

Objective: Speech recognition technology is widely used as a mature technical approach in many fields. In the study of depression recognition, speech signals are commonly used due to their convenience and ease of acquisition. Though speech recognition is popular in the research field of depression recognition, it has been little studied in somatisa...

The cardiovascular diseases (CVDs) cause tremendous deaths yearly. The Mel-spectrogram is widely used as a tool to analyse the heart sound, which facilitate a cheap and efficient diagnosis of CVDs. Nevertheless, the amplitude and frequency responses of the Mel filter banks remain constant, limiting its function to frequency selection. We propose an...

Mental disorders cannot only bring tremendous burdens to patients themselves, but also to the society. Effective early prediction and symptom monitoring can significantly improve mental health care across different populations. In this aspect, research on detecting mental disorders based on spontaneous physical activity (SPA) data has yielded promi...

Recent evidence have demonstrated that facial expressions could be a valid and important aspect for depression recognition. Although various works have been achieved in automatic depression recognition, it is a challenge to explore the inherent nuances of facial expressions that might reveal the underlying differences between depressed patients and...

Cardiovascular diseases (CVDs) are the leading cause of death globally. Heart sound signal analysis plays an important role in clinical detection and physical examination of CVDs. In recent years, auxiliary diagnosis technology of CVDs based on the detection of heart sound signals has become a research hotspot. The detection of abnormal heart sound...

In this paper, we describe our submissions for DCASE 2023 Challenge Task 2. For solving anomalous sound detection problem, an ensemble system with gan and auto-encoder model are proposed. Spectrograms and log-mel energies are used to train models. As a result, the proposed systems achieved a better performance than the baseline models.

Obstructive sleep apnoe (OSA) is a common clinical sleep-related breathing disorder. Classifying the excitation location of snore sound can help doctors provide more accurate diagnosis and complete treatment plans. In this study, we propose a strategy to classify snore sound leveraging ‘classic’ features sets. At training stage, we eliminate select...

Heart sound auscultation has been demonstrated to be beneficial in clinical usage for early screening of cardiovascular diseases. Due to the high requirement of well-trained professionals for auscultation, automatic auscultation benefiting from signal processing and machine learning can help auxiliary diagnosis and reduce the burdens of training pr...

Obstructive sleep apnoe (OSA) is a common clinical sleep-related breathing disorder. Classifying the excitation location of snore sound can help doctors provide more accurate diagnosis and complete treatment plans. In this study, we propose a strategy to classify snore sound leveraging 'classic' features sets. At training stage, we eliminate select...

Filters have a wide range of applications in many fields. Engineers generally use signal sources to manually test the characteristics of filters under test. In this research, we propose a system that automatically recognizes the characteristics and parameters of the analog filter throughout the entire process. This paper presents the theoretical co...

Automatic classification of heart sounds has been studied for many years, because computer-aided auscultation of heart sounds can help doctors make a preliminary diagnosis. We propose a classification method for heart sounds that uses fractional Fourier transformation entropy (FRFE) as the features and a support vector machine (SVM) as the classifi...