Change search
Refine search result
1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Pabon, Peter
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH. Institute of Sonology, Royal Conservatory.
    Mapping Individual Voice Quality over the Voice Range: The Measurement Paradigm of the Voice Range Profile2018Doctoral 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.

  • 2.
    Pabon, Peter
    Institute of Sonology, Royal Conservatory.
    Objective acoustic voice-quality parameters in the computer phonetogram1991In: Journal of Voice, ISSN 0892-1997, E-ISSN 1873-4588, Vol. 5, no 3, p. 203-216Article in journal (Refereed)
  • 3.
    Pabon, Peter
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Music Acoustics.
    Howard, David M.
    Ternström, Sten
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Music Acoustics.
    Kob, Malte
    Eckel, Gerhard
    KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID.
    Future Perspectives2017In: Oxford Handbook of Singing / [ed] Welch, Graham; Howard, David M.; Nix, John, Oxford University Press, 2017, Vol. 1Chapter in book (Other academic)
    Abstract [en]

    This chapter, through examining several emerging or continuing areas of research, serves to look ahead at possible ways in which humans, with the help of technology, may interact with each other vocally as well as musically. Some of the topic areas, such the use of the Voice Range Profile, hearing modeling spectrography, voice synthesis, distance masterclasses, and virtual acoustics, have obvious pedagogical uses in the training of singers. Others, such as the use of 3D printed vocal tracts and computer music composition involving the voice, may lead to unique new ways in which singing may be used in musical performance. Each section of the chapter is written by an expert in the field who explains the technology in question and how it is used, often drawing upon recent research led by the chapter authors.

  • 4.
    Pabon, Peter
    et al.
    Institute of Sonology, Royal Conservatory, The Hague.
    Plomp, Reinier
    Free University, Amsterdam.
    Automatic phonetogram recording supplemented with acoustic voice quality parameters1989In: Journal of Speech, Language and Hearing Research, ISSN 1092-4388, E-ISSN 1558-9102, Vol. 31, p. 710-722Article in journal (Refereed)
  • 5.
    Pabon, Peter
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Music Acoustics.
    Stallinga, R.
    Södersten, M.
    Ternström, Sten
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Music Acoustics.
    Effects on Vocal Range and Voice Quality of Singing Voice Training: The Classically Trained Female Voice2014In: Journal of Voice, ISSN 0892-1997, E-ISSN 1873-4588, Vol. 28, no 1, p. 36-51Article in journal (Refereed)
    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.

  • 6.
    Pabon, Peter
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH, Music Acoustics. Royal Conservatoire, The Hague, Netherlands.
    Ternström, Sten
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH, Music Acoustics.
    Feature maps of the acoustic spectrum of the voice2018In: Journal of Voice, ISSN 0892-1997, E-ISSN 1873-4588Article in journal (Refereed)
    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.

  • 7.
    Pabon, Peter
    et al.
    Royal Conservatoire, The Hague, NL.
    Ternström, Sten
    KTH, School of Computer Science and Communication (CSC).
    Lamarche, Anick
    KTH, School of Computer Science and Communication (CSC).
    Fourier Descriptor Analysis and Unification of Voice Range Profile Contours: Method and Applications2011In: Journal of Speech, Language and Hearing Research, ISSN 1092-4388, E-ISSN 1558-9102, Vol. 54, no 3, p. 755-776Article in journal (Refereed)
    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.

  • 8.
    Ternström, Sten
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Pabon, Peter
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH. Royal Conservatoire, The Hague, NL.
    Accounting for variability over the voice range2019In: Proceedings of the ICA 2019 and EAA Euroregio / [ed] Martin Ochmann, Michael Vorländer, Janina Fels, Aachen, DE: Deutsche Gesellschaft für Akustik (DEGA e.V.) , 2019, p. 7775-7780Conference paper (Refereed)
    Abstract [en]

    Researchers from the natural sciences interested in the performing arts often seek quantitative findings with explanatory power and practical relevance to performers and educators. However, the complexity of singing voice production continues to challenge us. On their own, entities that are readily measurable in the domain of physics are rarely of direct relevance to excellence in the domain of performance; because information on one level of representation (e.g., acoustic) is artistically meaningful mostly when interpreted in a context at a higher level of representation (e.g., emotional or semantic). Also, practically any acoustic or physiologic metric derived from the sound of a voice, or from other signals or images, will exhibit considerable variation both across individuals and across the voice range, from soft to loud or from low to high pitch. Here, we review some recent research based on the sampling paradigm of the voice field, also known as the voice range profile. Despite large inter-subject variation, the localizing by fo and SPL in the voice field will make the recorded values very reproducible within subjects. We demonstrate some technical possibilities, and argue the importance of making physical measurements that provide a more encompassing and individual-centric view of singing voice production.

  • 9.
    Ternström, Sten
    et al.
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Music Acoustics.
    Pabon, Peter
    KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Music Acoustics. Royal Conservatoire, The Hague, Netherlands.
    Södersten, M.
    The Voice Range Profile: its function, applications, pitfalls and potential2016In: Acta Acoustica united with Acustica, ISSN 1610-1928, E-ISSN 1861-9959, Vol. 102, no 2, p. 268-283Article in journal (Refereed)
    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.

1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf