This article is intended as an introductory tutorial for technically inclined clinicians, vocologists and voice pedagogues who want to understand the principles and potentials of voice mapping. Voice mapping has its origins in the Voice Range Profile, or phonetogram, but it is less concerned with the extremes of the voice range, and more with what happens within a relevant range of the voice. It is a voice instrumentation paradigm that is intended to improve the evidential value of voice measurements. It exposes and automatically accounts for the strong co-variation that most voice metrics exhibit with fundamental frequency and sound level. Very many data points are automatically collected in a short time, and their means are mapped by colour onto maps. This results in a robust representation of voice status and function. While individual voices are very different, a voice map’s appearance is reproducible within individuals. Comparing maps across interventions gives rich information, even on subtle changes in a voice. Further, by automatically clustering multiple metrics, phonation types can be identified and mapped automatically, which can increase the clinical relevance, and facilitate a better understanding of voice data in general.

Let “voice range profile” (a.k.a, “phonetogram”) be the term for a graph of the maximum phonatory range of a voice on the fo×SPL plane, i.e., a closed contour. Let “voice map” be the term for a map of a scalar metric over some relevant range,not necessarily to the extremes, on that same plane, i.e., a 2D scalar field. For imaging several metrics, one voice map can have several “layers”, all derived from the same recording. This paradigm for collection and collation of voice data is useful, because it accounts for how the chosen metrics vary systematically with fo and SPL. Both fo and SPL are influential and typically nonlinear covariates of other voice metrics. Not accounting for them can obscure the effects of an intervention. Here we summarize some central concepts, rationales and techniques related to voice mapping.

Individual acoustic and other physical metrics of vocal status have long struggled to prove their worth as clinical evidence. While combinations of metrics or “features” are now being intensely explored using data analytics methods, there is a risk that explainability and insight will suffer. The voice mapping paradigm discards the temporal dimension of vocal productions and uses fundamental frequency (f_o) and sound pressure level (SPL) as independent control variables to implement a dense grid of measurement points over a relevant voice range. Such mapping visualizes how most physical voice metrics are greatly affected by fo and SPL, and more so individually than has been generally recognized. It is demonstrated that if f_o and SPL are not controlled for during task elicitation, repeated measurements will generate “elicitation noise”, which can easily be large enough to obscure the effect of an intervention. It is observed that, although a given metric’s dependencies on f_o and SPL often are complex and/or non-linear, they tend to be systematic and reproducible in any given individual. Once such personal trends are accounted for, ordinary voice metrics can be used to assess vocal status. The momentary value of any given metric needs to be interpreted in the context of the individual’s voice range, and voice mapping makes this possible. Examples are given of how voice mapping can be used to quantify voice variability, to eliminate elicitation noise, to improve the reproducibility and representativeness of already established metrics of the voice, and to assess reliably even subtle effects of interventions. Understanding variability at this level of detail will shed more light on the interdependent mechanisms of voice production, and facilitate progress toward more reliable objective assessments of voices across therapy or training.

The change in the spectrum of sustained /a/ vowels was mapped over the voice range from low to high fundamental frequency and low to high sound pressure level (SPL), in the form of the so-called voice range profile (VRP). In each interval of one semitone and one decibel, narrowband spectra were averaged both within and across subjects. The subjects were groups of 7 male and 12 female singing students, as well as a group of 16 untrained female voices. For each individual and also for each group, pairs of VRP recordings were made, with stringent separation of the modal/chest and falsetto/head registers. Maps are presented of eight scalar metrics, each of which was chosen to quantify a particular feature of the voice spectrum, over fundamental frequency and SPL. Metrics 1 and 2 chart the role of the fundamental in relation to the rest of the spectrum. Metrics 3 and 4 are used to explore the role of resonances in relation to SPL. Metrics 5 and 6 address the distribution of high frequency energy, while metrics 7 and 8 seek to describe the distribution of energy at the low end of the voice spectrum.

Several examples are observed of phenomena that are difficult to predict from linear source-filter theory, and of the voice source being less uniform over the voice range than is conventionally assumed. These include a high-frequency band-limiting at high SPL and an unexpected 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 potential interest in voice analysis, voice synthesis and for new insights into the voice production mechanism.

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.

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.

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.

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.

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.