Snorer Diarisation Based On Deep Neural Network Embeddings

Romero, Hector E.; Ma, Ning; Brown, Guy J.

doi:10.1109/icassp40776.2020.9053683

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…In the future, robustness to noise from a bed partner will be investigated, building upon our previous work on snore analysis [47] and snorer diarisation [48]. A possible approach would be to extend this work to detect other breathing events in addition to snores, for example breaths, and analyse the temporal pattern of each subject's breathing.…”

Section: Discussionmentioning

confidence: 99%

Acoustic Screening for Obstructive Sleep Apnea in Home Environments Based on Deep Neural Networks

Romero

Brown

et al. 2022

IEEE J. Biomed. Health Inform.

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Obstructive sleep apnea (OSA) is a chronic and prevalent condition with well-established comorbidities. However, many severe cases remain undiagnosed due to poor access to polysomnography (PSG), the gold standard for OSA diagnosis. Accurate home-based methods to screen for OSA are needed, which can be applied inexpensively to high-risk subjects to identify those that require PSG to fully assess their condition. A number of methods that analyse speech or breathing sounds to screen for OSA have been previously investigated. However, these methods have constraints that limit their use in home environments (e.g., they require specialised equipment, are not robust to background noise, are obtrusive or depend on tightly controlled conditions). This paper proposes a novel method to screen for OSA, which analyses sleep breathing sounds recorded with a smartphone at home. Audio recordings made over a whole night are divided into segments, each of which is classified for the presence or absence of OSA by a deep neural network. The apneahypopnea index estimated from the segments predicted as containing evidence of OSA is then used to screen for the condition. Audio recordings made during home sleep apnea testing from 103 participants for 1 or 2 nights were used to develop and evaluate the proposed system. When screening for moderate OSA the acoustics based system achieved a sensitivity of 0.79 and a specificity of 0.80. The sensitivity and specificity when screening for severe OSA were 0.78 and 0.93, respectively. The system is suitable for implementation on consumer smartphones.

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Section: Discussionmentioning

confidence: 99%

Acoustic Screening for Obstructive Sleep Apnea in Home Environments Based on Deep Neural Networks

Romero

Brown

et al. 2022

IEEE J. Biomed. Health Inform.

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“…There are however two major challenges for realistic homemonitoring: 1) audio may be corrupted by background noise or interfered by snoring from the bed partner [14]; 2) sensors may be unreliable or missing (e.g., incorrectly fitted or falling off during sleep). To overcome these, this study proposes a multimodal approach through a combination of audio recordings and abdominal respiratory effort.…”

Section: Introductionmentioning

confidence: 99%

Obstructive sleep apnea screening with breathing sounds and respiratory effort: a multimodal deep learning approach

Romero¹,

Brown²,

Johnson³

2023

Interspeech 2023

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Obstructive sleep apnea (OSA) is a chronic and prevalent condition with well-established comorbidities. Due to limited diagnostic resources and high cost, a significant OSA population lives undiagnosed, and accurate and low-cost methods to screen for OSA are needed. We propose a novel screening method based on breathing sounds recorded with a smartphone and respiratory effort. Whole night recordings are divided into 30-s segments, each of which is classified for the presence or absence of OSA events by a multimodal deep neural network. Data fusion techniques were investigated and evaluated based on the apnea-hypopnea index estimated from whole night recordings. Real-world recordings made during home sleep apnea testing from 103 participants were used to develop and evaluate the proposed system. The late fusion system achieved the best sensitivity and specificity when screening for severe OSA, at 0.93 and 0.92, respectively. This offers the prospect of inexpensive OSA screening at home.

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“…The authors suggested a blind delayed source separation (BDSS) algorithm to estimate the snore over time. Another study by Romero et al (2020) used a snorer diarization method to obtain two concatenated audio signals of snoring events. In the current study, we modified the BDSS approach along with other separation approaches such as the T-F representation of signals.…”

Section: Introductionmentioning

confidence: 99%

Audio source separation to reduce sleeping partner sounds: a simulation study

Mordoh

Zigel

2021

Physiol. Meas.

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Objective. When recording a subject in an at-home environment for sleep evaluation or for other breathing disorder diagnoses using non-contact microphones, the breathing recordings (audio signals) can be distorted by sounds such as TV, outside noise, or air-conditioners. If two people are sleeping together, both may produce breathing/snoring sounds that need to be separated. In this study, we present signal processing and source separation algorithms for the enhancement of individual breathing/snoring audio signals in a simulated environment. Approach. We developed a computer simulation of mixed signals derived from genuine nocturnal recordings of 110 subjects. Two main source separation approaches were tested: (1) changing the basis vectors for the mixtures in the time domain (principal and independent component analysis, PCA/ICA) and (2) converting the mixtures to their time-frequency representations (degenerate un-mixing estimation technique, DUET). In addition to these source separation techniques, a beamforming approach was tested. Main results. The separation results with a reverberation time of 0.15 s and zero SNR between signals showed good performance (mean source to interference ratio (SIR): DUET = 12.831 dB, ICA = 3.388 dB, PCA = 4.452 dB), and for beamforming (SIR = −0.304 dB). To evaluate our source separation results, we propose two new measures: an evaluation measure based on a spectral similarity score (mel-SID) between the target source and its estimation (after separation) and a breathing energy ratio measure (BER). The results with the new proposed measures yielded comparable conclusions (mel-SID: DUET = 1.320, ICA = 2.732, PCA = 1.927, and beamforming = 2.590, BER: DUET = 10.241 dB, ICA = 0.270 dB, PCA = −2.847 dB, and beamforming = −1.151 dB), but better differentiated the differences between the performance of the algorithms. The DUET is superior on all measures. Its main advantage is that it only uses two microphones for separation. Significance. The separated audio signal can thus contribute to a more informed diagnosis of sleep-related and non-sleep-related diseases. The Institutional Review Committee of Soroka University Medical Center approved this study protocol (protocol number 10141) and all methods were performed in accordance with the relevant guidelines and regulations.

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Snorer Diarisation Based On Deep Neural Network Embeddings

Abstract: This is a repository copy of Snorer diarisation based on deep neural network embeddings.

Cited by 6 publications

References 22 publications

Acoustic Screening for Obstructive Sleep Apnea in Home Environments Based on Deep Neural Networks

Acoustic Screening for Obstructive Sleep Apnea in Home Environments Based on Deep Neural Networks

Obstructive sleep apnea screening with breathing sounds and respiratory effort: a multimodal deep learning approach

Audio source separation to reduce sleeping partner sounds: a simulation study

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