Automatic snore sound extraction from sleep sound recordings via auditory image modeling

One of humans’ auditory abilities is differentiation between sounds with slightly different frequencies. Recently, the auditory image model (AIM) was developed to numerically explain this auditory phenomenon. Acoustic analyses of snore sounds have been performed recently by using non-contact microphones. Snore/non-snore classification techniques have been required at the front-end of snore analyses. The performances of sound classification methods can be evaluated based on human hearing, which is considered to be the gold standard. In this paper, we propose a novel method of automatically extracting snore sounds from sleep sounds by using an AIM-based snore/non-snore classification system. We report that the proposed automatic classification method could achieve a sensitivity of 97.2% and specificity of 96.3% when analyzing snore and non-snore sounds from 40 subjects. It is anticipated that our findings will contribute to the development of an automated snore analysis system to be used in sleep studies.

[1]  N. Fisher,et al.  Statistical Analysis of Circular Data , 1993 .

[2]  H. Helmholtz,et al.  On the Sensations of Tone as a Physiological Basis for the Theory of Music , 2005 .

[3]  W D Duckitt,et al.  Automatic detection, segmentation and assessment of snoring from ambient acoustic data , 2006, Physiological measurement.

[4]  K. Hajian‐Tilaki,et al.  Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. , 2013, Caspian journal of internal medicine.

[5]  Perfecto Herrera,et al.  Comparing audio descriptors for singing voice detection in music audio files , 2007 .

[6]  Arthur Pewsey,et al.  The large-sample joint distribution of key circular statistics , 2004 .

[7]  D. Bloch,et al.  Comparison of the NovaSom QSG, a new sleep apnea home-diagnostic system, and polysomnography. , 2003, Sleep Medicine.

[8]  T. Bourne,et al.  Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[9]  Roy D Patterson,et al.  Functional imaging of the auditory processing applied to speech sounds , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[10]  R. Biddulph,et al.  Differential Pitch Sensitivity of the Ear , 1931 .

[11]  Kun-Ming Yu,et al.  Automatic Music Genre Classification using Modulation Spectral Contrast Feature , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[12]  Minoru Tsuzaki.  Feature extraction by auditory modeling for unit selection in concatenative speech synthesis , 2001, INTERSPEECH.

[13]  W. Youden,et al.  Index for rating diagnostic tests , 1950, Cancer.

[14]  M. Greiner,et al.  Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. , 2000, Preventive veterinary medicine.

[15]  Roy D. Patterson,et al.  The sound of a sinusoid: Spectral models , 1994 .

[16]  Roy D Patterson,et al.  Perception of acoustic scale and size in musical instrument sounds. , 2006, The Journal of the Acoustical Society of America.

[17]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[18]  R. Patterson,et al.  Complex Sounds and Auditory Images , 1992 .

[19]  Asela S Karunajeewa,et al.  Silence–breathing–snore classification from snore-related sounds , 2008, Physiological measurement.

[20]  Ray Meddis,et al.  Virtual pitch and phase sensitivity of a computer model of the auditory periphery , 1991 .

[21]  Roy D. Patterson,et al.  The sound of a sinusoid: Time‐interval models , 1994 .

[22]  Changsheng Xu,et al.  Automatic music classification and summarization , 2005, IEEE Transactions on Speech and Audio Processing.

[23]  Ying Sun,et al.  Rapid screening test for sleep apnea using a nonlinear and nonstationary signal processing technique. , 2007, Medical engineering & physics.

[24]  Brian R Glasberg,et al.  Derivation of auditory filter shapes from notched-noise data , 1990, Hearing Research.

[25]  Zahra Moussavi,et al.  Automatic and Unsupervised Snore Sound Extraction From Respiratory Sound Signals , 2011, IEEE Transactions on Biomedical Engineering.

[26]  R. Patterson.  Auditory filter shape. , 1974, The Journal of the Acoustical Society of America.

[27]  Steffen Pauws,et al.  CubyHum: a fully operational "query by humming" system , 2002, ISMIR.

[28]  Masataka Goto,et al.  Discrimination between singing and speaking voices , 2005, INTERSPEECH.

[29]  Roy D. Patterson,et al.  Auditory images:How complex sounds are represented in the auditory system , 2000 .

[30]  José Antonio Fiz,et al.  Multiclass classification of subjects with sleep apnoea-hypopnoea syndrome through snoring analysis. , 2012, Medical engineering & physics.

[31]  B. Moore,et al.  Frequency selectivity as a function of level and frequency measured with uniformly exciting notched noise. , 2000, The Journal of the Acoustical Society of America.

[32]  U. Abeyratne,et al.  Multi-feature snore sound analysis in obstructive sleep apnea-hypopnea syndrome. , 2011, Physiological measurement.

[33]  Takahide Matsuoka,et al.  Experiments on perceiving the missing fundamental by using two harmonic components tone , 2007 .

[34]  Y. Zigel,et al.  Automatic Detection of Whole Night Snoring Events Using Non-Contact Microphone , 2013, PloS one.

[35]  Tarmo Lipping,et al.  Comparison of entropy and complexity measures for the assessment of depth of sedation , 2006, IEEE Transactions on Biomedical Engineering.

[36]  Roy D. Patterson,et al.  Aim-mat: the auditory image model in MATLAB , 2004 .

[37]  Danoush Hosseinzadeh,et al.  Combining Vocal Source and MFCC Features for Enhanced Speaker Recognition Performance Using GMMs , 2007, 2007 IEEE 9th Workshop on Multimedia Signal Processing.

[38]  R. Patterson,et al.  Time-domain modeling of peripheral auditory processing: a modular architecture and a software platform. , 1995, The Journal of the Acoustical Society of America.

[39]  M Cavusoglu,et al.  An efficient method for snore/nonsnore classification of sleep sounds , 2007, Physiological measurement.

[40]  Roy D. Patterson,et al.  A Dynamic Compressive Gammachirp Auditory Filterbank , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[41]  U R Abeyratne,et al.  Obstructive sleep apnea screening by integrating snore feature classes , 2013, Physiological measurement.

[42]  Timothy R. Anderson,et al.  Binaural phoneme recognition using the auditory image model and cross-correlation , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.