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Mapping Individual Voice Quality over the Voice Range: The Measurement Paradigm of the Voice Range Profile
KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH. Institute of Sonology, Royal Conservatory.ORCID iD: 0000-0003-2497-3109
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The acoustic signal of voiced sounds has two primary attributes: fundamental frequency and sound level. It has also very many secondary attributes, or ‘voice qualities’, that can be derived from the acoustic signal, in particular from its periodicity and its spectrum. Acoustic voice analysis as a discipline is largely concerned with identifying and quantifying those qualities or parameters that are relevant for assessing the health or training status of a voice or that characterize the individual quality. The thesis presented here is that all such voice qualities covary essentially and individually with the fundamental frequency and the sound level, and that methods for assessing the voice must account for this covariation and individuality. The central interest in the "voice field" measurement paradigm becomes to map the proportional dependencies that exist between voice parameters. The five studies contribute to ways of doing this in practice, while the framework text presents the theoretical basis for the analysis model in relation to the practical principles.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. , p. 90
Series
TRITA-EECS-AVL ; 2018:70
Keywords [en]
Voice Range Profile (VRP), Phonetogram, Voice Field, Voice quality, Singing voice, Voice synthesis, Clinical voice assessment, Sound level measurement, Maximum Entropy
National Category
Other Natural Sciences
Research subject
Speech and Music Communication
Identifiers
URN: urn:nbn:se:kth:diva-235824ISBN: 978-91-7729-958-5 (print)OAI: oai:DiVA.org:kth-235824DiVA, id: diva2:1253755
Public defence
2018-10-26, Kollegiesalen, Brinellvägen 8, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

QC 20181008

Available from: 2018-10-08 Created: 2018-10-05 Last updated: 2018-10-08Bibliographically approved
List of papers
1. Automatic phonetogram recording supplemented with acoustic voice quality parameters
Open this publication in new window or tab >>Automatic phonetogram recording supplemented with acoustic voice quality parameters
1989 (English)In: Journal of Speech, Language and Hearing Research, ISSN 1092-4388, E-ISSN 1558-9102, Vol. 31, p. 710-722Article in journal (Refereed) Published
National Category
Other Natural Sciences
Identifiers
urn:nbn:se:kth:diva-235817 (URN)10.1044/jshr.3104.710 (DOI)
Note

QC 20181008

Available from: 2018-10-05 Created: 2018-10-05 Last updated: 2018-10-08Bibliographically approved
2. Objective acoustic voice-quality parameters in the computer phonetogram
Open this publication in new window or tab >>Objective acoustic voice-quality parameters in the computer phonetogram
1991 (English)In: Journal of Voice, ISSN 0892-1997, E-ISSN 1873-4588, Vol. 5, no 3, p. 203-216Article in journal (Refereed) Published
National Category
Other Natural Sciences
Identifiers
urn:nbn:se:kth:diva-235821 (URN)10.1016/S0892-1997(05)80188-2 (DOI)
Note

QC 2018108

Available from: 2018-10-05 Created: 2018-10-05 Last updated: 2018-10-08Bibliographically approved
3. Fourier Descriptor Analysis and Unification of Voice Range Profile Contours: Method and Applications
Open this publication in new window or tab >>Fourier Descriptor Analysis and Unification of Voice Range Profile Contours: Method and Applications
2011 (English)In: Journal of Speech, Language and Hearing Research, ISSN 1092-4388, E-ISSN 1558-9102, Vol. 54, no 3, p. 755-776Article in journal (Refereed) Published
Abstract [en]

Purpose: To describe a method for unified description, statistical modeling, and comparison of voice range profile (VRP) contours, even from diverse sources. Method: A morphologic modeling technique, which is based on Fourier descriptors (FDs), is applied to the VRP contour. The technique, which essentially involves resampling of the curve of the contour, is assessed and also is compared to density-based VRP averaging methods that use the overlap count. Results: VRP contours can be usefully described and compared using FDs. The method also permits the visualization of the local covariation along the contour average. For example, the FD-based analysis shows that the population variance for ensembles of VRP contours is usually smallest at the upper left part of the VRP. To illustrate the method's advantages and possible further application, graphs are given that compare the averaged contours from different authors and recording devices-for normal, trained, and untrained male and female voices as well as for child voices. Conclusions: The proposed technique allows any VRP shape to be brought to the same uniform base. On this uniform base, VRP contours or contour elements coming from a variety of sources may be placed within the same graph for comparison and for statistical analysis.

Keywords
voice range profile, phonetogram, norms, contour averaging, Fourier descriptors
National Category
General Language Studies and Linguistics
Identifiers
urn:nbn:se:kth:diva-35125 (URN)10.1044/1092-4388(2010/08-0222) (DOI)000291166100003 ()2-s2.0-79958737078 (Scopus ID)
Note

QC 20110623

Available from: 2011-06-23 Created: 2011-06-20 Last updated: 2018-10-22Bibliographically approved
4. Effects on Vocal Range and Voice Quality of Singing Voice Training: The Classically Trained Female Voice
Open this publication in new window or tab >>Effects on Vocal Range and Voice Quality of Singing Voice Training: The Classically Trained Female Voice
2014 (English)In: Journal of Voice, ISSN 0892-1997, E-ISSN 1873-4588, Vol. 28, no 1, p. 36-51Article in journal (Refereed) Published
Abstract [en]

ObjectivesA longitudinal study was performed on the acoustical effects of singing voice training under a given study programme, using the Voice Range Profile (VRP). Study DesignPre- and post-training recordings were made of students that participated in a 3-year bachelor singing study programme. A questionnaire that included questions on optimal range, register use, classification, vocal health and hygiene, mixing technique, and training goals, was used to rate and categorize self-assessed voice changes. Based on the responses, a sub-group of 10 classically trained female voices was selected, that was homogeneous enough for effects of training to be identified. MethodsThe VRP perimeter contour was analyzed for effects of voice training. Also, a mapping within the VRP of voice quality, as expressed by the crest factor, was used to indicate the register boundaries and to monitor the acoustical consequences of the newly learned vocal technique of ‘mixed voice.’ VRP’s were averaged across subjects. Findings were compared to the self-assessed vocal changes. ResultsPre-post comparison of the average VRPs showed, in the midrange, (1) a decrease in the VRP area that was associated with the loud chest voice, (2) a reduction of the crest factor values, and (3) a reduction of maximum SPL values. The students’ self-evaluations of the voice changes appeared in some cases to contradict the VRP findings. ConclusionsVRP’s of individual voices were seen to change over the course of a singing education. These changes were manifest also in the group average. High resolution computerized recording, complemented with an acoustic register marker, allows a meaningful assessment of some effects of training, on an individual basis as well as for groups comprised of singers of a specific genre. It is argued that this kind of investigation is possible only within a focussed training programme, given by a faculty that has agreed on the goals.

Place, publisher, year, edition, pages
Elsevier, 2014
Keywords
Mixed voice, Phonetogram, Voice range profile, Voice training
National Category
Computer Sciences
Research subject
Speech and Music Communication
Identifiers
urn:nbn:se:kth:diva-137397 (URN)10.1016/j.jvoice.2013.06.005 (DOI)000329326100006 ()2-s2.0-84891835734 (Scopus ID)
Funder
Swedish Research Council
Note

QC 20140130 tmh_import_13_12_13, tmh_id_3868

Available from: 2013-12-13 Created: 2013-12-13 Last updated: 2018-10-05Bibliographically approved
5. Feature maps of the acoustic spectrum of the voice
Open this publication in new window or tab >>Feature maps of the acoustic spectrum of the voice
2018 (English)In: Journal of Voice, ISSN 0892-1997, E-ISSN 1873-4588Article in journal (Refereed) Epub ahead of print
Abstract [en]

The change in the spectrum of sustained /a/ vowels was mapped over the voice range from low to high fundamentalfrequency and low to high sound pressure level (SPL), in the form of the so-called voice range profile (VRP). In eachinterval of one semitone and one decibel, narrowband spectra were averaged both within and across subjects. Thesubjects were groups of 7 male and 12 female singing students, as well as a group of 16 untrained female voices. Foreach individual and also for each group, pairs of VRP recordings were made, with stringent separation of themodal/chest and falsetto/head registers. Maps are presented of eight scalar metrics, each of which was chosen toquantify a particular feature of the voice spectrum, over fundamental frequency and SPL. Metrics 1 and 2 chart the roleof the fundamental in relation to the rest of the spectrum. Metrics 3 and 4 are used to explore the role of resonances inrelation to SPL. Metrics 5 and 6 address the distribution of high frequency energy, while metrics 7 and 8 seek todescribe the distribution of energy at the low end of the voice spectrum. Several examples are observed ofphenomena that are difficult to predict from linear source-filter theory, and of the voice source being less uniform overthe voice range than is conventionally assumed. These include a high-frequency band-limiting at high SPL and anunexpected persistence of the second harmonic at low SPL. The two voice registers give rise to clearly different maps.Only a few effects of training were observed, in the low frequency end below 2 kHz. The results are of potentialinterest in voice analysis, voice synthesis and for new insights into the voice production mechanism.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Voice Range Profile (VRP), Voice Register, level of the fundamental, harmonicformant interaction, spectrum balance (SB), non-linear source-filter interaction
National Category
Computer and Information Sciences
Research subject
Speech and Music Communication
Identifiers
urn:nbn:se:kth:diva-234309 (URN)10.1016/j.jvoice.2018.08.014 (DOI)
Projects
Fonadyn
Funder
Swedish Research Council, 2010-4565
Note

QC 201801001

Available from: 2018-09-06 Created: 2018-09-06 Last updated: 2018-10-05Bibliographically approved
6. The Voice Range Profile: its function, applications, pitfalls and potential
Open this publication in new window or tab >>The Voice Range Profile: its function, applications, pitfalls and potential
2016 (English)In: Acta Acoustica united with Acustica, ISSN 1610-1928, E-ISSN 1861-9959, Vol. 102, no 2, p. 268-283Article in journal (Refereed) Published
Abstract [en]

An overview is given of the current status of the computerised voice range profile (VRP) as a voice measurement paradigm. Its operating principles are described, and sources of errors and variability are discussed. The features of the VRP contour and its characterisaï¿œtion are described. Methods for performing statistics on VRP contour and interior data are considered. Examples are given of clinical, pedagogical and research applications. Finally, issues with the models used to interpret VRP data are discussed. It is concluded that, while the VRP offers a convenient frame of reference for a multitude of voice assessment metrics, it also exposes the many degrees of freedom in the voice to an extent that challenges us to improve our models of how the voice functions over a large range and in a dynamic setting.

Place, publisher, year, edition, pages
S. Hirzel Verlag, 2016
Keywords
voice, voice analysis, voice range profile
National Category
Signal Processing
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-183406 (URN)10.3813/AAA.918943 (DOI)000372478500008 ()2-s2.0-84961590849 (Scopus ID)
Funder
Swedish Research Council, 2010-4565Forte, Swedish Research Council for Health, Working Life and Welfare, 2002-0416
Note

QC 20160316

Available from: 2016-03-10 Created: 2016-03-10 Last updated: 2018-10-05Bibliographically approved

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