Recurrent Neural Network for Classification of Snoring and Non-Snoring Sound Events

Arsenali, Bruno; Dijk, Hans van; Ouweltjes, Okke; Brinker, Bert den; Pevernagie, Dirk; Krijn, Roy; Gilst, Merel M van; Overeem, Sebastiaan

doi:10.1109/embc.2018.8512251

Cited by 26 publications

(21 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From results in Table IX, CRNN architecture is more capable to learn features from audios than traditional methods. ④Results: For snore and cough, our experiment performs better than [43]- [44]. However, it performs less than [45] when it comes to bowel sound.…”

Section: ) Comparison With Related Workmentioning

confidence: 77%

“…The algorithm achieves a high F1 of 88.07. [44] proposes such a method and achieves a very high F1 of 94.93. The centerpiece of the proposed method is a Compared with [43]- [45], there are some differences: ①Task: [43]- [45] focus on one specific type of sound event while our intention is to do multi-classification.…”

Section: ) Comparison With Related Workmentioning

confidence: 99%

“…The centerpiece of the proposed method is a Compared with [43]- [45], there are some differences: ①Task: [43]- [45] focus on one specific type of sound event while our intention is to do multi-classification. For example, [44] only outputs cough and non-cough events, our experiment outputs the results of all those 6 sound events. ②Dataset: The dataset is labeled on the event-level in [43]- [45] while our GI Sound Set is labeled on the recording-level.…”

Section: ) Comparison With Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A CRNN System for Sound Event Detection Based on Gastrointestinal Sound Dataset Collected by Wearable Auscultation Devices

Zheng

Zhang

Chen³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In this paper, we set up a novel audio dataset named Gastrointestinal (GI) Sound Set which includes 6 kinds of body sounds Bowel sound, Speech, Snore, Cough, Groan, and Rub. We do sound event detection (SED) based on it, and can accurately detect 6 types of sound events. First, the GI Sound Set is collected by wearable auscultation devices. To ensure generalization, patients from five different hospital departments are recruited for data collection, along with a group of healthy subjects. GI Sound Set refers to Google AudioSet in data format but varies in audio length and sampling rate. Second, we extract Mel-filter features from the recordings and investigate the performance of different activation functions and neural network architectures for detecting sound events. We use data augmentation, class balance to deal with the problem of quantitative imbalance between classes on the dataset. We apply multiple instances learning(MIL) to give out not only bag-level results but also frame-level results. In this work, GI Sound Set is the largest body sound dataset to date, and our approach shows state-of-the-art performance with an average score of F1=81.06% evaluated on the test set. Due to its simple network and conventional processing method, our CRNN system has high universality, which can be used in other audio datasets, such as respiratory sound and heart sound. INDEX TERMS Gastrointestinal (GI) Sound Set, sound event detection(SED), convolutional recurrent neural network (CRNN), multiple instance learning(MIL).

show abstract

Section: ) Comparison With Related Workmentioning

confidence: 77%

Section: ) Comparison With Related Workmentioning

confidence: 99%

Section: ) Comparison With Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A CRNN System for Sound Event Detection Based on Gastrointestinal Sound Dataset Collected by Wearable Auscultation Devices

Zheng

Zhang

Chen³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Aside polysomnography which has high financial cost implication [12], other several approaches [3,7], [13][14][15][16][17][18][19][20][21][22] has been proposed in literatures to detect, diagnose and remedy early snoring stage before getting to an unhealthy phase. Lately, methods in the field of signal processing and AI seems to be the promising area to detect snoring and apnea event.…”

Section: Introductionmentioning

confidence: 99%

A Brief on Snoring Data and Classification Methods

Adesuyi¹

2020

IJATCSE

View full text Add to dashboard Cite

Recently, research on snoring sound had gained interest especially in the area of classification in Obstructive Sleep Apnea (OSA) and distinction from non-snoring sounds. Classifiers such as Support Vector Machine (SVM), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) had been used to meet this interest. These approaches relies on several underlying techniques such as Mel Frequency Cepstral Coefficients (MFCC), Short-Time Fourier transform (STFT) and several others to extract features from snore/apnea Spectrogram images before classification process. However, achieving desirable classification accuracy depends on choice of classifier, feature extraction techniques and, available dataset. In the paper, we presented a brief survey on existing methods and snore data acquisition processes to quickly expose and ease new researches in this domain to make appropriate choice from available methods.

show abstract

Recurrent Neural Network for Classification of Snoring and Non-Snoring Sound Events

Cited by 26 publications

References 15 publications

A CRNN System for Sound Event Detection Based on Gastrointestinal Sound Dataset Collected by Wearable Auscultation Devices

A CRNN System for Sound Event Detection Based on Gastrointestinal Sound Dataset Collected by Wearable Auscultation Devices

Detection of Snore from OSAHS Patients Based on Deep Learning

A Brief on Snoring Data and Classification Methods

Contact Info

Product

Resources

About