ABSTRACT
Singing is a very common activity among people and especially children. It is of particular importance in young ages, as it has been suggested to promote the children’s social integration. While singing is a key component of elementary music classes, proper vocal training of young individuals has not been implemented into the current Greek elementary school curriculum. In addition, oftentimes music teachers are not trained to provide proper vocal instruction to young individuals. This work is part of the ASMA research project, which focuses on the social and aesthetic importance of student vocal training and the development of interactive applications supporting its instruction in elementary schools. The main hypothesis is that new technologies informing modern teaching methodologies can support proper vocal practice, by offering teachers individualized feedback on each student’s needs. Within this framework, this paper discusses the development of a set of tools assisting music teachers build individualized vocal profiles for each student based on simple vocal acoustic measurements.
- 2021. Teaching and Learning in Primary Education. https://eacea.ec.europa.eu/national-policies/eurydice/content/teaching-and-learning-primary-education-20_enGoogle Scholar
- Paavo Alku, Jouni Pohjalainen, Martti Vainio, Anne-Maria Laukkanen, and Brad H. Story. 2013. Formant frequency estimation of high-pitched vowels using weighted linear prediction. The Journal of the Acoustical Society of America 134, 2 (2013), 1295–1313. Publisher: Acoustical Society of America.Google ScholarCross Ref
- Evangelos Angelakis, Anastasia Georgaki, and Panagiotis Velianitis. 2017. «Match Your Own Voice!» A software tool to assist singing practice on the somatosensory motivation. In Proceedings of the 12th Pan European Voice Conference (PEVOC12).Google Scholar
- Evangelos Angelakis, George Kosteletos, Areti Andreopoulou, and Anastasia Georgaki. 2018. Development and Evaluation of an Audio Signal Processing Educational Tool to Support Somatosensory Singing Control. In Audio Engineering Society Convention 145. Audio Engineering Society.Google Scholar
- Amalia Arvaniti. 2007. Greek PhoneticsThe State of the Art. J Greek Linguist 8, 1 (2007), 97–208. https://doi.org/10.1075/jgl.8.08arvGoogle ScholarCross Ref
- Viviane M. Oliveira Barrichelo, Reinhardt J Heuer, Carole M Dean, and Robert T Sataloff. 2001. Comparison of Singer’s Formant, Speaker’s Ring, and LTA Spectrum Among Classical Singers and Untrained Normal Speakers. Journal of Voice 15, 3 (Sept. 2001), 344–350. https://doi.org/10.1016/S0892-1997(01)00036-4Google Scholar
- Irene Velsvik Bele. 2006. The Speaker’s Formant. Journal of Voice 20, 4 (Dec. 2006), 555–578. https://doi.org/10.1016/j.jvoice.2005.07.001Google ScholarCross Ref
- Paul Boersma. 2001. Praat, a system for doing phonetics by computer. Glot. Int. 5, 9 (2001), 341–345.Google Scholar
- Antonis Botinis, Marios Fourakis, and John W. Hawks. 1997. A perceptual study of the Greek vowel space using synthetic stimuli. In Fifth European Conference on Speech Communication and Technology.Google ScholarCross Ref
- Johanna Böhm, Florian Eyben, Maximilian Schmitt, Harald Kosch, and Björn Schuller. 2017. Seeking the SuperStar: Automatic assessment of perceived singing quality. In 2017 International Joint Conference on Neural Networks (IJCNN). 1560–1569. https://doi.org/10.1109/IJCNN.2017.7966037 ISSN: 2161-4407.Google ScholarCross Ref
- Gerhard Böhme and Gisela Stuchlik. 1995. Voice profiles and standard voice profile of untrained children. Journal of Voice 9, 3 (Sept. 1995), 304–307. https://doi.org/10.1016/S0892-1997(05)80238-3Google ScholarCross Ref
- Jean Callaghan, William Thorpe, and Jan van Doorn. 2004. The science of singing and seeing. In Conference on Interdisciplinary Musicology, R Parncutt, A Kessler, and F Zimmer (Eds.). Graz/Austira, 10.Google Scholar
- Annabel J. Cohen, Vickie L. Armstrong, Marsha S. Lannan, and Jenna D. Coady. 2009. A Protocol for Cross-Cultural Research on the Acquisition of Singing. Annals of the New York Academy of Sciences 1169, 1 (July 2009), 112–115. https://doi.org/10.1111/j.1749-6632.2009.04771.xGoogle ScholarCross Ref
- Ekaterini Derdemezis, Houri K. Vorperian, Ray D. Kent, Marios Fourakis, Emily L. Reinicke, and Daniel M. Bolt. 2016. Optimizing Vowel Formant Measurements in Four Acoustic Analysis Systems for Diverse Speaker Groups. Am J Speech Lang Pathol 25, 3 (Aug. 2016), 335–354. https://doi.org/10.1044/2015_AJSLP-15-0020Google ScholarCross Ref
- Michael Fuchs, Sylvia Meuret, Susanne Thiel, Roland Täschner, Andreas Dietz, and Götz Gelbrich. 2009. Influence of Singing Activity, Age, and Sex on Voice Performance Parameters, on Subjects’ Perception and Use of Their Voice in Childhood and Adolescence. Journal of Voice 23, 2 (March 2009), 182–189. https://doi.org/10.1016/j.jvoice.2007.09.007Google ScholarCross Ref
- Dr Nathalie Henrich, Mara Kiek, John Smith, and Joe Wolfe. 2007. Resonance strategies used in Bulgarian women’s singing style: A pilot study. Logopedics Phoniatrics Vocology 32, 4 (Jan. 2007), 171–177. https://doi.org/10.1080/14015430600891504 Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/14015430600891504.Google Scholar
- David M Howard. 2009. Acoustics of the trained versus untrained singing voice:. Current Opinion in Otolaryngology & Head and Neck Surgery 17, 3 (June 2009), 155–159. https://doi.org/10.1097/MOO.0b013e32832af11bGoogle ScholarCross Ref
- David M. Howard and James A. S. Angus. 1997. A comparison between singing pitching strategies of 8 to 11 year olds and trained adult singers. Logopedics Phoniatrics Vocology 22, 4 (Jan. 1997), 169–176. https://doi.org/10.3109/14015439709075331Google ScholarCross Ref
- David M. Howard, Jude Brereton, Graham F. Welch, Evangelos Himonides, Michael DeCosta, Jenevora Williams, and Andrew W. Howard. 2007. Are Real-Time Displays of Benefit in the Singing Studio? An Exploratory Study. Journal of Voice 21, 1 (Jan. 2007), 20–34. https://doi.org/10.1016/j.jvoice.2005.10.003Google ScholarCross Ref
- David M. Howard and Graham F. Welch. 1993. Visual displays for the assessment of vocal pitch matching development. Applied Acoustics 39, 4 (Jan. 1993), 235–252. https://doi.org/10.1016/0003-682X(93)90008-TGoogle ScholarCross Ref
- David M. Howard, Graham F. Welch, Jude Brereton, Evangelos Himonides, Michael DeCosta, Jenevora Williams, and Andrew W. Howard. 2004. WinSingad: A real-time display for the singing studio. Logopedics Phoniatrics Vocology 29, 3 (Oct. 2004), 135–144. https://doi.org/10.1080/14015430410000728Google ScholarCross Ref
- Yannick Jadoul, Bill Thompson, and Bart de Boer. 2018. Introducing Parselmouth: A Python interface to Praat. Journal of Phonetics 71(2018), 1–15. https://doi.org/10.1016/j.wocn.2018.07.001Google ScholarCross Ref
- Mayank Vibhuti Jha and Preeti Rao. 2012. Assessing vowel quality for singing evaluation. In 2012 National Conference on Communications (NCC). 1–5. https://doi.org/10.1109/NCC.2012.6176860Google ScholarCross Ref
- Raymond D. Kent and Houri K. Vorperian. 2018. Static measurements of vowel formant frequencies and bandwidths: A review. Journal of Communication Disorders 74 (July 2018), 74–97. https://doi.org/10.1016/j.jcomdis.2018.05.004Google ScholarCross Ref
- Tomi Kinnunen, Ville Hautamaki, and Pasi Franti. 2006. On the Use of Long-Term Average Spectrum in Automatic Speaker Recognition. In International Symposium on Chinese Spoken Language Processing, Vol. Vol. II. ISCA Archive, Kent Ridge, Singapore, 559–567.Google Scholar
- May Kokkidou, Zoe Dionyssiou, and Polyvios Androutsos. 2014. Problems, visions and concerns of pre-service music and general education teachers in Greece resulting from their teaching practice in music. Music Education Research 16, 4 (Oct. 2014), 485–504. https://doi.org/10.1080/14613808.2014.881795 Publisher: Routledge _eprint: https://doi.org/10.1080/14613808.2014.881795.Google Scholar
- Varvara Kyritsi, Anastasia Georgaki, and Georgios Kouroupetroglou. 2007. A Score-to-singing voice synthesis System for the Greek Language. In Proceedings of the 2007 International Computer Music Conference, ICMC 2007, Copenhagen, Denmark, August 27-31, 2007. Michigan Publishing. http://hdl.handle.net/2027/spo.bbp2372.2007.156Google Scholar
- Timo Leino, Anne-Maria Laukkanen, and Vojtěch Radolf. 2011. Formation of the Actor’s/Speaker’s Formant: A Study Applying Spectrum Analysis and Computer Modeling. Journal of Voice 25, 2 (March 2011), 150–158. https://doi.org/10.1016/j.jvoice.2009.10.002Google ScholarCross Ref
- Donna S. Lundy, Soham Roy, Roy R. Casiano, Jun W. Xue, and Joseph Evans. 2000. Acoustic analysis of the singing and speaking voice in singing students. Journal of Voice 14, 4 (Dec. 2000), 490–493. https://doi.org/10.1016/S0892-1997(00)80006-5Google ScholarCross Ref
- Anders Löfqvist. 1986. The long-time-average spectrum as a tool in voice research. Journal of Phonetics 14, 3-4 (Oct. 1986), 471–475. https://doi.org/10.1016/S0095-4470(19)30692-8Google ScholarCross Ref
- Suely Master, Noemi de Biase, Vanessa Pedrosa, and Brasília Maria Chiari. 2006. The long-term average spectrum in research and in the clinical practice of speech therapists. Pró-Fono Revista de Atualização Científica 18, 1 (Jan. 2006), 111–120. https://doi.org/10.1590/S0104-56872006000100013 Publisher: Pró-Fono Departamento Editorial.Google Scholar
- Dieter Maurer. 2016. Acoustics of the Vowel(peter lang ed.). sub-Texte, Vol. 12. Peter Lang CH. https://doi.org/10.3726/978-3-0343-2391-8Google Scholar
- Anita McAllister, Elisabeth Sederholm, Johan Sundberg, and Patricia Gramming. 1994. Relations between voice range profiles and physiological and perceptual voice characteristics in ten-year-old children. Journal of Voice 8, 3 (Sept. 1994), 230–239. https://doi.org/10.1016/S0892-1997(05)80294-2Google ScholarCross Ref
- Randall S. Moore. 1991. Comparison of Children’s and Adults’ Vocal Ranges and Preferred Tessituras in Singing Familiar Songs. Bulletin of the Council for Research in Music Education107 (1991), 13–22. http://www.jstor.org/stable/40318417Google Scholar
- Fotios Moschos, Anastasia Georgaki, and Georgios Kouroupetroglou. 2016. FONASKEIN: An Interactive Software Application for the Practice of the Singing Voice. In Proceedings of the Sound and Music Computing (SMC) Conference, September. 326–331.Google Scholar
- Tomoyasu Nakano, Masataka Goto, and Yuzuru Hiraga. 2006. An Automatic Singing Skill Evaluation Method for Unknown Melodies Using Pitch Interval Accuracy and Vibrato Features. In Ninth International Conference on Spoken Language Processing. Pittsburgh, PA, USA, 4. https://www.isca-speech.org/archive/interspeech_2006/i06_1854.htmlGoogle Scholar
- Katerina Nicolaidis. 1991. Coarticulatory Strategies in Greek and English VCV Sequences. Selected papers on theoretical and applied linguistics 5, 0 (May 1991), 113–140. https://doi.org/10.26262/istal.v5i0.7134 Number: 0.Google Scholar
- Elina Nirgianaki. 2014. Acoustic characteristics of Greek fricatives. The Journal of the Acoustical Society of America 135, 5 (2014), 2964–2976.Google ScholarCross Ref
- Kay Norton. 2015. Singing and wellbeing: Ancient wisdom, modern proof. Routledge.Google Scholar
- George Onwudiwe. 2021. The acoustic analysis of speech: A precursor to better speech performance and perception. Journal Of Linguistics, Language and Culture (JOLLC) 7, 1 (Jan. 2021). https://www.nigerianjournalsonline.com/index.php/jollc/article/view/1362 Number: 1.Google Scholar
- Emmi Pentikäinen, Anni Pitkäniemi, Sini-Tuuli Siponkoski, Maarit Jansson, Jukka Louhivuori, Julene K Johnson, Teemu Paajanen, and Teppo Särkämö. 2021. Beneficial effects of choir singing on cognition and well-being of older adults: Evidence from a cross-sectional study. PloS one 16, 2 (2021), e0245666.Google ScholarCross Ref
- Manuel Pérez-Gil, Jesús Tejada, Remigi Morant, and A Pérez-González-de. 2015. DESIGN AND IMPLEMENTATION OF A SOFTWARE FOR THE TRAINING AND REAL-TIME ASSESSMENT OF MUSICAL INTONATION AT MUSIC EDUCATION INSTITUTIONS. In INTED2015. Madrid, Spain, 2485–2493.Google Scholar
- Gareth Roberts and Robin Clark. 2020. Dispersion, communication, and alignment: an experimental study of the emergence of structure in combinatorial phonology. Journal of Language Evolution 5, 2 (Aug. 2020), 121–139. https://doi.org/10.1093/jole/lzaa004Google ScholarCross Ref
- David Rossiter and David M. Howard. 1996. ALBERT: a real-time visual feedback computer tool for professional vocal development. Journal of Voice : Official Journal of the Voice Foundation 10, 4 (Dec. 1996), 321–336. https://europepmc.org/article/med/8943135Google Scholar
- Joanne Rutkowski. 2015. The Relationship Between Children’s Use of Singing Voice and Singing Accuracy. Music Perception 32, 3 (Feb. 2015), 283–292. https://doi.org/10.1525/mp.2015.32.3.283Google ScholarCross Ref
- Joanne Rutkowski and Martha Snell Miller. 2002. A Longitudinal Study of Elementary Children’s Acquisition of Their Singing Voices. Sage journals (2002), 10.Google Scholar
- Anna Sfakianaki. 2002. Acoustic characteristics of Greek vowels produced by adults and children. Selected papers on theoretical and applied linguistics Vol 14 (April 2002), 383–394 Pages. https://doi.org/10.26262/ISTAL.V14I0.6223 Artwork Size: 383-394 Pages Publisher: Selected papers on theoretical and applied linguistics.Google Scholar
- Nora Siupsinskiene and Hugo Lycke. 2011. Effects of Vocal Training on Singing and Speaking Voice Characteristics in Vocally Healthy Adults and Children Based on Choral and Nonchoral Data. Journal of Voice 25, 4 (July 2011), e177–e189. https://doi.org/10.1016/j.jvoice.2010.03.010Google ScholarCross Ref
- Stavropoulou Sofia, Georgaki Anastasia, and Moschos Fotis. 2014. The Effectiveness of visual feedback singing vocal technology in greek elementary school. ICMC-SMC 2014, 1786–1792.Google Scholar
- Marina Sotiropoulou Zormpala, Kalliopi Trouli, and Michail Linardakis. 2015. Arts education offered by Greek Universities to future pre-school and primary school teachers. Preschool and Primary Education 3, 1 (March 2015), 34–52. https://www.learntechlib.org/p/187354/ Publisher: Laboratory of Pedagogical Research & Applications.Google Scholar
- Brad H. Story and Kate Bunton. 2016. Formant measurement in children’s speech based on spectral filtering. Speech Communication 76 (Feb. 2016), 93–111. https://doi.org/10.1016/j.specom.2015.11.001Google ScholarDigital Library
- Charalambos Themistocleous and Angeliki Logotheti. 2016. Standard Modern Greek and Cypriot Greek vowels: a sociophonetic study. Modern Greek Dialects and Linguistic Theory Vol 6, 1 (2016), 178–184. https://doi.org/10.26220/mgdlt.v6i1.2684Google Scholar
- Ingo R. Titze, Ronald J. Baken, Kenneth W. Bozeman, Svante Granqvist, Nathalie Henrich, Christian T. Herbst, David M. Howard, Eric J. Hunter, Dean Kaelin, Raymond D. Kent, Jody Kreiman, Malte Kob, Anders Löfqvist, Scott McCoy, Donald G. Miller, Hubert Noé, Ronald C. Scherer, John R. Smith, Brad H. Story, Jan G. Švec, Sten Ternström, and Joe Wolfe. 2015. Toward a consensus on symbolic notation of harmonics, resonances, and formants in vocalization. The Journal of the Acoustical Society of America 137, 5 (May 2015), 3005–3007. https://doi.org/10.1121/1.4919349 Publisher: Acoustical Society of America.Google ScholarCross Ref
- Ingo R. Titze, Lynn M. Maxfield, and Megan C. Walker. 2017. A Formant Range Profile for Singers. Journal of Voice 31, 3 (May 2017), 382.e9–382.e13. https://doi.org/10.1016/j.jvoice.2016.08.014Google ScholarCross Ref
- Valerie L. Trollinger. 2003. Relationships between Pitch-Matching Accuracy, Speech Fundamental Frequency, Speech Range, Age, and Gender in American English-Speaking Preschool Children. Journal of Research in Music Education 51, 1 (April 2003), 78–94. https://doi.org/10.2307/3345650Google ScholarCross Ref
- Cathering M. Tu. 2020. Correlations among Music Aptitude, Singing Voice Development, and Singing Accuracy Achievement in Young Children. In The Routledge Companion to Interdisciplinary Studies in Singing, Volume I: Development(1st ed.), Frank A. Russo, Beatriz Ilari, and Annabel J. Cohen (Eds.). Vol. 1. Routledge, 520. https://doi.org/10.4324/9781315163734Google Scholar
- Irene Vogel, Angeliki Athanasopoulou, and Nadya Pincus. 2016. Prominence, Contrast, and the Functional Load Hypothesis: An Acoustic Investigation. In Dimensions of Phonological Stress, Jeffrey Heinz, Rob Goedemans, and Harry van der Hulst (Eds.). Cambridge University Press, Cambridge, 123–167. https://doi.org/10.1017/9781316212745.006Google Scholar
- Graham F Welch, Evangelos Himonides, Jo Saunders, Ioulia Papageorgi, and Marc Sarazin. 2014. Singing and social inclusion. Frontiers in psychology 5 (2014), 803.Google Scholar
- Graham F. Welch, David M. Howard, Evangelos Himonides, and Jude Brereton. 2005. Real-time feedback in the singing studio: an innovatory action-research project using new voice technology. Music Education Research 7, 2 (July 2005), 225–249. https://doi.org/10.1080/14613800500169779Google ScholarCross Ref
- Pat H Wilson, Kerrie Lee, Jean Callaghan, and C William Thorpe. 2008. Learning to Sing in Tune: Does Real-Time Visual Feedback Help?Jourmal of Interdisciplinary Music Studies Vol. 2, Issue 1/2 (2008), 157–172.Google Scholar
- Jan G. Švec and Svante Granqvist. 2010. Guidelines for Selecting Microphones for Human Voice Production Research. Am J Speech Lang Pathol 19, 4 (Nov. 2010), 356–368. https://doi.org/10.1044/1058-0360(2010/09-0091)Google ScholarCross Ref
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