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Published in: European Archives of Oto-Rhino-Laryngology 12/2023

16-08-2023 | Laryngology

Are smartphones and low-cost external microphones comparable for measuring time-domain acoustic parameters?

Authors: M. Enes Ceylan, M. Emrah Cangi, Göksu Yılmaz, Beyza Sena Peru, Özgür Yiğit

Published in: European Archives of Oto-Rhino-Laryngology | Issue 12/2023

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Abstract

Purpose

This study examined and compared the diagnostic accuracy and correlation levels of the acoustic parameters of the audio recordings obtained from smartphones on two operating systems and from dynamic and condenser types of external microphones.

Method

The study included 87 adults: 57 with voice disorder and 30 with a healthy voice. Each participant was asked to perform a sustained vowel phonation (/a/). The recordings were taken simultaneously using five microphones AKG-P220, Shure-SM58, Samson Go Mic, Apple iPhone 6, and Samsung Galaxy J7 Pro microphones in an acoustically insulated cabinet. Acoustic examinations were performed using Praat version 6.2.09. The data were examined using Pearson correlation and receiver-operating characteristic (ROC) analyses.

Results

The parameters with the highest area under curve (AUC) values among all microphone recordings in the time-domain analyses were the frequency perturbation parameters. Additionally, considering the correlation coefficients obtained by synchronizing the microphones with each other and the AUC values together, the parameter with the highest correlation coefficient and diagnostic accuracy values was the jitter-local parameter.

Conclusion

Period-to-period perturbation parameters obtained from audio recordings made with smartphones show similar levels of diagnostic accuracy to external microphones used in clinical conditions.
Appendix
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Literature
1.
go back to reference Svec JG, Granqvist S (2010) Guidelines for selecting microphones for human voice production research. Am J Speech Lang Pathol 19(4):356–368CrossRefPubMed Svec JG, Granqvist S (2010) Guidelines for selecting microphones for human voice production research. Am J Speech Lang Pathol 19(4):356–368CrossRefPubMed
2.
go back to reference Delıyıskı DD, Evans MK, Shaw HS (2005) Influence of data acquisition environment on accuracy of acoustic voice quality measurements. J Voice 19(2):176–186CrossRef Delıyıskı DD, Evans MK, Shaw HS (2005) Influence of data acquisition environment on accuracy of acoustic voice quality measurements. J Voice 19(2):176–186CrossRef
3.
go back to reference Patel RR, Awan SN, Barkmeier-Kraemer J, Courey M, Deliyski D, Eadie T, Hillman R (2018) Recommended protocols for instrumental assessment of voice: American Speech-Language-Hearing Association expert panel to develop a protocol for instrumental assessment of vocal function. Am J Speech-Lang Pathol 27(3):887–905CrossRefPubMed Patel RR, Awan SN, Barkmeier-Kraemer J, Courey M, Deliyski D, Eadie T, Hillman R (2018) Recommended protocols for instrumental assessment of voice: American Speech-Language-Hearing Association expert panel to develop a protocol for instrumental assessment of vocal function. Am J Speech-Lang Pathol 27(3):887–905CrossRefPubMed
4.
go back to reference Oliveira G, Fava G, Baglione M, Pimpinella M (2017) Mobile digital recording: adequacy of the irig and ios device for acoustic and perceptual analysis of normal voice. J Voice 31(2):236–242CrossRefPubMed Oliveira G, Fava G, Baglione M, Pimpinella M (2017) Mobile digital recording: adequacy of the irig and ios device for acoustic and perceptual analysis of normal voice. J Voice 31(2):236–242CrossRefPubMed
5.
go back to reference Payten CL, Nguyen DD, Novakovic D, O’Neill J, Chacon AM, Weir KA, Madill CJ (2022) Protocol: Telehealth voice assessment by speech-language pathologists during a global pandemic using principles of a primary contact model: an observational cohort study protocol. BMJ Open 12(1):e052518CrossRefPubMed Payten CL, Nguyen DD, Novakovic D, O’Neill J, Chacon AM, Weir KA, Madill CJ (2022) Protocol: Telehealth voice assessment by speech-language pathologists during a global pandemic using principles of a primary contact model: an observational cohort study protocol. BMJ Open 12(1):e052518CrossRefPubMed
6.
go back to reference Petrizzo D, Popolo PS (2021) Smartphone use in clinical voice recording and acoustic analysis: a literature review. J Voice 35(3):499-e23CrossRef Petrizzo D, Popolo PS (2021) Smartphone use in clinical voice recording and acoustic analysis: a literature review. J Voice 35(3):499-e23CrossRef
7.
go back to reference Castillo-Allendes A, Contreras-Ruston F, Cantor-Cutiva LC, Codino J, Guzman M, Malebran C, Behlau M (2021) Voice therapy in the context of the COVID-19 pandemic: guidelines for clinical practice. J Voice 35(5):717–727CrossRefPubMed Castillo-Allendes A, Contreras-Ruston F, Cantor-Cutiva LC, Codino J, Guzman M, Malebran C, Behlau M (2021) Voice therapy in the context of the COVID-19 pandemic: guidelines for clinical practice. J Voice 35(5):717–727CrossRefPubMed
10.
go back to reference Weidner K, Lowman J (2020) Telepractice for adult speech-language pathology services: a systematic review. Perspect ASHA Spec Interest Groups 5(1):326–338CrossRef Weidner K, Lowman J (2020) Telepractice for adult speech-language pathology services: a systematic review. Perspect ASHA Spec Interest Groups 5(1):326–338CrossRef
12.
go back to reference Lin FC, Chien HY, Chen SH, Kao YC, Cheng PW, Wang CT (2020) Voice therapy for benign voice disorders in the elderly: a randomized controlled trial comparing telepractice and conventional face-to-face therapy. J Speech Lang Hear Res 63(7):2132–2140CrossRefPubMed Lin FC, Chien HY, Chen SH, Kao YC, Cheng PW, Wang CT (2020) Voice therapy for benign voice disorders in the elderly: a randomized controlled trial comparing telepractice and conventional face-to-face therapy. J Speech Lang Hear Res 63(7):2132–2140CrossRefPubMed
14.
go back to reference Jannetts S, Schaeffler F, Beck J, Cowen S (2019) Assessing voice health using smartphones: bias and random error of acoustic voice parameters captured by different smartphone types. Int J Lang Commun Disord 54(2):292–305CrossRefPubMed Jannetts S, Schaeffler F, Beck J, Cowen S (2019) Assessing voice health using smartphones: bias and random error of acoustic voice parameters captured by different smartphone types. Int J Lang Commun Disord 54(2):292–305CrossRefPubMed
15.
go back to reference Lin E, Hornibrook J, Ormond T (2012) Evaluating iphone recordings for acoustic voice assessment. Folia Phoniatr Logop 64(3):122–130CrossRefPubMed Lin E, Hornibrook J, Ormond T (2012) Evaluating iphone recordings for acoustic voice assessment. Folia Phoniatr Logop 64(3):122–130CrossRefPubMed
16.
go back to reference Manfredi C, Lebacq J, Cantarella G, Schoentgen J, Orlandi S, Bandini A, Dejonckere PH (2017) Smartphones offer new opportunities in clinical voice research. J Voice 31(1):111.E1-111.E7CrossRefPubMed Manfredi C, Lebacq J, Cantarella G, Schoentgen J, Orlandi S, Bandini A, Dejonckere PH (2017) Smartphones offer new opportunities in clinical voice research. J Voice 31(1):111.E1-111.E7CrossRefPubMed
17.
go back to reference Maryn Y, Ysenbaert F, Zarowski A, Vanspauwen R (2017) Mobile communication devices, ambient noise, and acoustic voice measures. J Voice 31(2):248.E11-248.E23CrossRefPubMed Maryn Y, Ysenbaert F, Zarowski A, Vanspauwen R (2017) Mobile communication devices, ambient noise, and acoustic voice measures. J Voice 31(2):248.E11-248.E23CrossRefPubMed
18.
go back to reference Mat Baki M, Wood G, Alston M, Ratcliffe P, Sandhu G, Rubin JS, Birchall MA (2015) Reliability of opera VOX against multidimensional voice program (MDVP). Clin Otolaryngol 40(1):22–28CrossRefPubMed Mat Baki M, Wood G, Alston M, Ratcliffe P, Sandhu G, Rubin JS, Birchall MA (2015) Reliability of opera VOX against multidimensional voice program (MDVP). Clin Otolaryngol 40(1):22–28CrossRefPubMed
19.
go back to reference Uloza V, Padervinskis E, Vegiene A, Pribuisiene R, Saferis V, Vaiciukynas E, Gelzinis A, Verikas A (2015) Exploring the feasibility of smartphone microphone for measurement of acoustic voice parameters and voice pathology screening. Eur Arch Otorhinolaryngol 272(11):3391–3399CrossRefPubMed Uloza V, Padervinskis E, Vegiene A, Pribuisiene R, Saferis V, Vaiciukynas E, Gelzinis A, Verikas A (2015) Exploring the feasibility of smartphone microphone for measurement of acoustic voice parameters and voice pathology screening. Eur Arch Otorhinolaryngol 272(11):3391–3399CrossRefPubMed
21.
go back to reference Kardous CA, Shaw PB (2014) Evaluation of smartphone voice measurement applications. J Acoust Soc Am 135:186–192CrossRef Kardous CA, Shaw PB (2014) Evaluation of smartphone voice measurement applications. J Acoust Soc Am 135:186–192CrossRef
22.
go back to reference Murphy E, King EA (2016) Testing the accuracy of smartphones and voice level meter applications for measuring environmental noise. Appl Acoust 106:16–22CrossRef Murphy E, King EA (2016) Testing the accuracy of smartphones and voice level meter applications for measuring environmental noise. Appl Acoust 106:16–22CrossRef
23.
go back to reference Šrámková H, Granqvist S, Herbst CT, Švec JG (2015) The softest sound levels of human voice in normal subjects. J Acoust Soc Am 137:407–418CrossRefPubMed Šrámková H, Granqvist S, Herbst CT, Švec JG (2015) The softest sound levels of human voice in normal subjects. J Acoust Soc Am 137:407–418CrossRefPubMed
24.
go back to reference Švec JG, Granqvist S (2018) Tutorial and guidelines on measurement of sound pressure level in voice and speech. J Speech Lang Hear Res 61(3):441–461CrossRefPubMed Švec JG, Granqvist S (2018) Tutorial and guidelines on measurement of sound pressure level in voice and speech. J Speech Lang Hear Res 61(3):441–461CrossRefPubMed
26.
go back to reference Maryn Y, Zarowski A (2015) Calibration of clinical audio recording and analysis systems for sound intensity measurement. Am J Speech Lang Pathol 24(4):608–618CrossRefPubMed Maryn Y, Zarowski A (2015) Calibration of clinical audio recording and analysis systems for sound intensity measurement. Am J Speech Lang Pathol 24(4):608–618CrossRefPubMed
28.
go back to reference Lebacq J, Schoentgen J, Cantarella G, Bruss FT, Manfredı C, Dejonckere PH (2017) Maximal ambient noise level sand type of voice material required for valid use of smartphones in clinical voice research. J Voice 31:550–556CrossRefPubMed Lebacq J, Schoentgen J, Cantarella G, Bruss FT, Manfredı C, Dejonckere PH (2017) Maximal ambient noise level sand type of voice material required for valid use of smartphones in clinical voice research. J Voice 31:550–556CrossRefPubMed
29.
go back to reference Yılmaz G, Cangi ME, Yelken K (2021) Receiver operating characteristic analysis of acoustic and electroglottographic parameters with different sustained vowels. Logop Phoniatr Vocol 47:284–291CrossRef Yılmaz G, Cangi ME, Yelken K (2021) Receiver operating characteristic analysis of acoustic and electroglottographic parameters with different sustained vowels. Logop Phoniatr Vocol 47:284–291CrossRef
30.
go back to reference Awan SN, Roy N, Jetté ME, Meltzner GS, Hillman RE (2010) Quantifying dysphonia severity using a spectral/cepstral-based acoustic index: Comparisons with auditory-perceptual judgements from the CAPE-V. Clin Linguist Phon 24(9):742–758CrossRefPubMed Awan SN, Roy N, Jetté ME, Meltzner GS, Hillman RE (2010) Quantifying dysphonia severity using a spectral/cepstral-based acoustic index: Comparisons with auditory-perceptual judgements from the CAPE-V. Clin Linguist Phon 24(9):742–758CrossRefPubMed
31.
go back to reference Heman-Ackah Yd, Rt S, Laureyns G, Lurie D, Michael Dd, Heuer R, Lyons K (2014) Quantifying the cepstral peak prominence, a measure of dysphonia. J Voice 28(6):783–788CrossRefPubMed Heman-Ackah Yd, Rt S, Laureyns G, Lurie D, Michael Dd, Heuer R, Lyons K (2014) Quantifying the cepstral peak prominence, a measure of dysphonia. J Voice 28(6):783–788CrossRefPubMed
32.
go back to reference Awan SN, Giovinco A, Owens J (2012) Effects of vocal intensity and vowel type on cepstral analysis of voice. J Voice 26(5):670-e15CrossRef Awan SN, Giovinco A, Owens J (2012) Effects of vocal intensity and vowel type on cepstral analysis of voice. J Voice 26(5):670-e15CrossRef
33.
go back to reference Brockmann-Bauser M, Beyer D, Bohlender JE (2014) Clinical relevance of speaking voice intensity effects on acoustic jitter and shimmer in children between 5; 0 and 9; 11 years. Int J Pediatr Otorhinolaryngol 78(12):2121–2126CrossRefPubMed Brockmann-Bauser M, Beyer D, Bohlender JE (2014) Clinical relevance of speaking voice intensity effects on acoustic jitter and shimmer in children between 5; 0 and 9; 11 years. Int J Pediatr Otorhinolaryngol 78(12):2121–2126CrossRefPubMed
34.
go back to reference Brockmann-Bauser M, Bohlender JE, Mehta DD (2018) Acoustic perturbation measures improve with increasing vocal intensity in individuals with and without voice disorders. J Voice 32(2):162–168CrossRefPubMed Brockmann-Bauser M, Bohlender JE, Mehta DD (2018) Acoustic perturbation measures improve with increasing vocal intensity in individuals with and without voice disorders. J Voice 32(2):162–168CrossRefPubMed
35.
go back to reference Brockmann-Bauser M, Van Stan JH, Sampaio MC, Bohlender JE, Hillman RE, Mehta DD (2021) Effects of vocal intensity and fundamental frequency on cepstral peak prominence in patients with voice disorders and vocally healthy controls. J Voice 35(3):411–417CrossRefPubMed Brockmann-Bauser M, Van Stan JH, Sampaio MC, Bohlender JE, Hillman RE, Mehta DD (2021) Effects of vocal intensity and fundamental frequency on cepstral peak prominence in patients with voice disorders and vocally healthy controls. J Voice 35(3):411–417CrossRefPubMed
36.
go back to reference Maryn Y (2017) Practical acoustics in clinical voice assessment: a praat primer. Perspect ASHA Spec Interest Groups 2(3):14–32CrossRef Maryn Y (2017) Practical acoustics in clinical voice assessment: a praat primer. Perspect ASHA Spec Interest Groups 2(3):14–32CrossRef
37.
go back to reference Nathan V, Rahman MM, Vatanparvar K, Nemati E, Blackstock E, Kuang J (2019) Extraction of voice parameters from continuous running speech for pulmonary disease monitoring. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 859–864 Nathan V, Rahman MM, Vatanparvar K, Nemati E, Blackstock E, Kuang J (2019) Extraction of voice parameters from continuous running speech for pulmonary disease monitoring. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 859–864
38.
go back to reference Stasak B, Huang Z, Razavi S, Joachim D, Epps J (2021) Automatic detection of COVID-19 based on short-duration acoustic smartphone speech analysis. J Healthc Inform Res 5:201–217CrossRefPubMedPubMedCentral Stasak B, Huang Z, Razavi S, Joachim D, Epps J (2021) Automatic detection of COVID-19 based on short-duration acoustic smartphone speech analysis. J Healthc Inform Res 5:201–217CrossRefPubMedPubMedCentral
39.
go back to reference Ahmed S, Haigh AMF, de Jager CA, Garrard P (2013) Connected speech as a marker of disease progression in autopsy-proven Alzheimer’s disease. Brain 136(12):3727–3737CrossRefPubMedPubMedCentral Ahmed S, Haigh AMF, de Jager CA, Garrard P (2013) Connected speech as a marker of disease progression in autopsy-proven Alzheimer’s disease. Brain 136(12):3727–3737CrossRefPubMedPubMedCentral
40.
go back to reference Costantini G, Cesarini V, Di Leo P, Amato F, Suppa A, Asci F, Saggio G (2023) Artificial intelligence-based voice assessment of patients with Parkinson’s disease off and on treatment: machine vs deep-learning comparison. Sensors 23(4):2293CrossRefPubMedPubMedCentral Costantini G, Cesarini V, Di Leo P, Amato F, Suppa A, Asci F, Saggio G (2023) Artificial intelligence-based voice assessment of patients with Parkinson’s disease off and on treatment: machine vs deep-learning comparison. Sensors 23(4):2293CrossRefPubMedPubMedCentral
41.
go back to reference Arora S, Baghai-Ravary L, Tsanas A (2019) Developing a large scale population screening tool for the assessment of Parkinson’s disease using telephone-quality voice. J Acoust Soc Am 145(5):2871–2884CrossRefPubMedPubMedCentral Arora S, Baghai-Ravary L, Tsanas A (2019) Developing a large scale population screening tool for the assessment of Parkinson’s disease using telephone-quality voice. J Acoust Soc Am 145(5):2871–2884CrossRefPubMedPubMedCentral
42.
go back to reference Zhan A, Mohan S, Tarolli C, Schneider RB, Adams JL, Sharma S et al (2018) Using smartphones and machine learning to quantify Parkinson disease severity: the Mobile Parkinson Disease Score. JAMA Neurol 75(7):876–80CrossRefPubMedPubMedCentral Zhan A, Mohan S, Tarolli C, Schneider RB, Adams JL, Sharma S et al (2018) Using smartphones and machine learning to quantify Parkinson disease severity: the Mobile Parkinson Disease Score. JAMA Neurol 75(7):876–80CrossRefPubMedPubMedCentral
43.
go back to reference Zhang L, Duvvuri R, Chandra KK, Nguyen T, Ghomi RH (2020) Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative. Depress Anxiety 37(7):657–69CrossRefPubMed Zhang L, Duvvuri R, Chandra KK, Nguyen T, Ghomi RH (2020) Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative. Depress Anxiety 37(7):657–69CrossRefPubMed
44.
go back to reference Fagherazzi G, Fischer A, Ismael M, Despotovic V (2021) Voice for health: the use of vocal biomarkers from research to clinical practice. Digit Biomarkers 5(1):78–88CrossRef Fagherazzi G, Fischer A, Ismael M, Despotovic V (2021) Voice for health: the use of vocal biomarkers from research to clinical practice. Digit Biomarkers 5(1):78–88CrossRef
45.
go back to reference Asci F, Costantini G, Di Leo P, Zampogna A, Ruoppolo G, Berardelli A, Suppa A (2020) Machine-learning analysis of voice samples recorded through smartphones: the combined effect of ageing and gender. Sensors 20(18):5022CrossRefPubMedPubMedCentral Asci F, Costantini G, Di Leo P, Zampogna A, Ruoppolo G, Berardelli A, Suppa A (2020) Machine-learning analysis of voice samples recorded through smartphones: the combined effect of ageing and gender. Sensors 20(18):5022CrossRefPubMedPubMedCentral
Metadata
Title
Are smartphones and low-cost external microphones comparable for measuring time-domain acoustic parameters?
Authors
M. Enes Ceylan
M. Emrah Cangi
Göksu Yılmaz
Beyza Sena Peru
Özgür Yiğit
Publication date
16-08-2023
Publisher
Springer Berlin Heidelberg
Published in
European Archives of Oto-Rhino-Laryngology / Issue 12/2023
Print ISSN: 0937-4477
Electronic ISSN: 1434-4726
DOI
https://doi.org/10.1007/s00405-023-08179-3

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