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1

Snoring Sound Recognition Using Multi-Channel Spectrograms

Ye Ziqiang, Peng Jianxin, Zhang Xiaowen, Song Lijuan

Archives of Acoustics

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2024

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Vol. 49, nr 2

169--178

EN

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common and high-risk sleep-related breathing disorder. Snoring detection is a simple and non-invasive method. In many studies, the feature maps are obtained by applying a short-time Fourier transform (STFT) and feeding the model with single-channel input tensors. However, this approach may limit the potential of convolutional networks to learn diverse representations of snore signals. This paper proposes a snoring sound detection algorithm using a multi-channel spectrogram and convolutional neural network (CNN). The sleep recordings from 30 subjects at the hospital were collected, and four different feature maps were extracted from them as model input, including spectrogram, Mel-spectrogram, continuous wavelet transform (CWT), and multi-channel spectrogram composed of the three single-channel maps. Three methods of data set partitioning are used to evaluate the performance of feature maps. The proposed feature maps were compared through the training set and test set of independent subjects by using a CNN model. The results show that the accuracy of the multi-channel spectrogram reaches 94.18%, surpassing that of the Mel-spectrogram that exhibits the best performance among the single-channel spectrograms. This study optimizes the system in the feature extraction stage to adapt to the superior feature learning capability of the deep learning model, providing a more effective feature map for snoring detection.

2

Study on Chinese Speech Intelligibility Under Different Low-Frequency Characteristics of Reverberation Time Using a Hybrid Method

Huang Wuqiong, Peng Jianxin, Xie Tinghui

Archives of Acoustics

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2023

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Vol. 48, No. 2

151--157

EN

Reverberation time (RT) is an important indicator of room acoustics, however, most studies focus on the mid-high frequency RT, and less on the low-frequency RT. In this paper, a hybrid approach based on geometric and wave methods was proposed to build a more accurate and wide frequency-band room acoustic impulse response. This hybrid method utilized the finite-difference time-domain (FDTD) method modeling at low frequencies and the Odeon simulation at mid-high frequencies, which was investigated in a university classroom. The influence of the low-frequency RT on speech intelligibility was explored. For the low-frequency part, different impedance boundary conditions were employed and the effectiveness of the hybrid method has also been verified. From the results of objective acoustical parameters and subjective listening experiments, the smaller the low-frequency RT was, the higher the Chinese speech intelligibility score was. The syllables, consonants, vowels, and the syllable order also had significant effects on the intelligibility score.

3

Sleep Snoring Sound Recognition Based on Wavelet Packet Transform

Ding Li, Peng Jianxin, Zhang Xiaowen, Song Lijuan

Archives of Acoustics

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2023

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Vol. 48, No. 1

3--12

EN

Snoring is a typical and intuitive symptom of the obstructive sleep apnea hypopnea syndrome (OSAHS), which is a kind of sleep-related respiratory disorder having adverse effects on people’s lives. Detecting snoring sounds from the whole night recorded sounds is the first but the most important step for the snoring analysis of OSAHS. An automatic snoring detection system based on the wavelet packet transform (WPT) with an eXtreme Gradient Boosting (XGBoost) classifier is proposed in the paper, which recognizes snoring sounds from the enhanced episodes by the generalization subspace noise reduction algorithm. The feature selection technology based on correlation analysis is applied to select the most discriminative WPT features. The selected features yield a high sensitivity of 97.27% and a precision of 96.48% on the test set. The recognition performance demonstrates that WPT is effective in the analysis of snoring and non-snoring sounds, and the difference is exhibited much more comprehensively by sub-bands with smaller frequency ranges. The distribution of snoring sound is mainly on the middle and low frequency parts, there is also evident difference between snoring and non-snoring sounds on the high frequency part.

4

Relationship Between Chinese Speech Intelligibility of Elderly and Speech Transmission Index

Peng Jianxin, Zeng Jiazhong, Zhao Yuezhe

Archives of Acoustics

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2021

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Vol. 46, No. 2

229--235

EN

In this paper, the relationship between Chinese speech intelligibility (CSI) scores of the elderly aged 60-69 and over 70 years old, and speech transmission index (STI) were investigated through the auralization method under different reverberation time and background noise levels (BNL, 40 dBA and 55 dBA). The results show that the CSI scores of the elderly are significantly worse than those of young adults. For the elderly over 70, the CSI scores become much lower than those of young adults. To be able to achieve the same CSI, the elderly, especially those over 70, need much higher STI and greater SNR than the young. The elderly aged 60-69 and over 70 need to improve their STI by 0.419 and 0.058 respectively under BNL 40 dBA, as well as 0.282 and 0.072 respectively under BNL 55 dBA, so as to obtain the same CSI scores as the young adults.

5

A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring

Wang Can, Peng Jianxin, Zhang Xiaowen

Archives of Acoustics

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2020

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Vol. 45, No. 1

141--151

EN

Acoustical analysis of snoring provides a new approach for the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS). A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. The acoustical features of snoring were extracted from a full night’s recording of 6 OSAHS patients, and regular snoring sounds and snoring sounds related to respiratory disorder events were classified using a support vector machine (SVM) method. The mean recognition rate for simple snoring sounds and snoring sounds related to respiratory disorder events is more than 91.14% by using the grid search, a genetic algorithm and particle swarm optimization methods. The predicted AHI from the present study has a high correlation with the AHI from polysomnography and the correlation coefficient is 0.976. These results demonstrate that the proposed method can classify the snoring sounds of OSAHS patients and can be used to provide guidance for diagnosis of OSAHS.