Classification of snoring sound based on a recurrent neural network

Lim, Seung Ju; Jang, Seong Jin; Lim, Jee Young; Ko, Jae Hoon

doi:10.1016/j.eswa.2019.01.020

Cited by 29 publications

(8 citation statements)

References 31 publications

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“…Audio pattern recognition is an important research topic in machine learning and plays an important role in our life. Audio pattern recognition contains several tasks such as audio tagging [1], acoustic scene classification [2], sound event detection [3], classifying the emotion of patients from their speech [4], detecting the abnormal snoring of a patient [5] and classifying heart sounds.…”

Section: Introductionmentioning

confidence: 99%

PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

Kong

Cao

Iqbal

et al. 2019

Preprint

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Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification and sound event detection. Recently neural networks have been applied to solve audio pattern recognition problems. However, previous systems focus on small datasets, which limits the performance of audio pattern recognition systems. Recently in computer vision and natural language processing, systems pretrained on large datasets have generalized well to several tasks. However, there is limited research on pretraining neural networks on large datasets for audio pattern recognition. In this paper, we propose large-scale pretrained audio neural networks (PANNs) trained on AudioSet. We propose to use Wavegram, a feature learned from waveform, and the mel spectrogram as input. We investigate the performance and complexity of a variety of convolutional neural networks. Our proposed AudioSet tagging system achieves a state-of-the-art mean average precision (mAP) of 0.439, outperforming the best previous system of 0.392. We transferred a PANN to six audio pattern recognition tasks and achieve state-of-the-art performance in many tasks. Source code and pretrained models have been released.

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Section: Introductionmentioning

confidence: 99%

PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

Kong

Cao

Iqbal

et al. 2019

Preprint

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“…A Recurrent Neural Network (RNN) is one of the most widely used deep learning methods and it can be used to simulate human memory cells [18]. It has shown great promise in tackling sequential data, including image segmentation [19], sound recognition [20], genes engineering [21] and stock prediction [22]. RNN models have been applied to typhoon and hurricane predictions as well.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the unit root test should be conducted for the stationarity of each time series variables. In this study, the Augmented Dickey-Fuller unit root test (ADF) [20] is used for the stationary test. The null hypothesis of the ADF means that if the unit root exists, then the time series variable is not stable.…”

Section: ) the Multi-dimensional Feature Selection Layermentioning

confidence: 99%

A Novel Data-Driven Tropical Cyclone Track Prediction Model Based on CNN and GRU With Multi-Dimensional Feature Selection

et al. 2020

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Strong tropical cyclones have made a drastic effect on human life and natural environment. As large amounts of meteorological data and monitoring data continue to accumulate, traditional methods for predicting tropical cyclone tracks face numerous challenges regarding their prediction efficiency and accuracy. Deep learning methods recently have been proven to be able to learn both spatial and temporal features from a large amount of dataset and be extremely efficient and accurate for forecasting data in complex structures. In this paper, we propose a novel data-driven deep learning model to predict tropical cyclone tracks using the spatial locations and multiple meteorological factors. This model comprises a multidimensional feature selection layer, a CNN layer and a GRU layer. The proposed model was trained using a dataset of real-world tropical cyclones from the years 1945 to 2017. Through the comparison experiments, the results verify that the proposed model outperforms the traditional forecasting methods, including a climatologically aware forecasting technique, the Sanders Barotropic technique and a numerical weather prediction (NWP) model. In addition, the proposed model has better accuracy than some deep learning methods, including RNN, GRU, CNN, AE-RNN, CNN-RNN, and CNN-GRU without the proposed feature selection layer. INDEX TERMS Tropical cyclones tracks prediction, deep learning, feature selection, typhoon.

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“…In the study of snoring, the researchers first studied the technique of extracting snoring sounds from breathing sounds. For example, the nonlinear classification algorithm to identify snoring sounds was studied by Ankishan [ 13 ]; Lim proposed a snoring recognition method based on RNN [ 14 , 15 ]. The study of OSAHS recognition based on snoring has also been proposed after the effective extraction of snoring signals: After extracting the time-domain features of snoring after apnea events, Temrat et al judged the severity degree of OSAHS through distinguishing different types of snoring by the leave-one-out cross-validation technique [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Snore from OSAHS Patients Based on Deep Learning

Shen

Cheng

Zhu

et al. 2020

Journal of Healthcare Engineering

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Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunction, resulting in damage to multiple organs and multiple systems throughout the body and negatively affecting the cardiovascular, kidney, and mental systems. Clinically, doctors usually use standard PSG (Polysomnography) to assist diagnosis. PSG determines whether a person has apnea syndrome with multidimensional data such as brain waves, heart rate, and blood oxygen saturation. In this paper, we have presented a method of recognizing OSAHS, which is convenient for patients to monitor themselves in daily life to avoid delayed treatment. Firstly, we theoretically analyzed the difference between the snoring sounds of normal people and OSAHS patients in the time and frequency domains. Secondly, the snoring sounds related to apnea events and the nonapnea related snoring sounds were classified by deep learning, and then, the severity of OSAHS symptoms had been recognized. In the algorithm proposed in this paper, the snoring data features are extracted through the three feature extraction methods, which are MFCC, LPCC, and LPMFCC. Moreover, we adopted CNN and LSTM for classification. The experimental results show that the MFCC feature extraction method and the LSTM model have the highest accuracy rate which was 87% when it is adopted for binary-classification of snoring data. Moreover, the AHI value of the patient can be obtained by the algorithm system which can determine the severity degree of OSAHS.

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Classification of snoring sound based on a recurrent neural network

Cited by 29 publications

References 31 publications

PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

A Novel Data-Driven Tropical Cyclone Track Prediction Model Based on CNN and GRU With Multi-Dimensional Feature Selection

Detection of Snore from OSAHS Patients Based on Deep Learning

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