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.

Background: The flow ball is a device that creates a static backpressure in the vocal tract while providing real-time visual feedback of airflow. A ball height of 0 to 10 cm corresponds to airflows of 0.2 to 0.4. L/s. These high airflows with low transglottal pressure correspond to low flow resistances, similar to the ones obtained when phonating into straws with 3.7 mm diameter and of 2.8 cm length. Objectives: To investigate whether there are immediate effects of flow ball-assisted training on vocal fold contact. Methods: Ten singers (five males and five females) performed a messa di voce at different pitches over one octave in three different conditions: before, during and after phonating with a flow ball. For all conditions, both audio and electrolaryngographic (ELG) signals were simultaneously recorded using a Laryngograph microprocessor. The vocal fold contact quotient Qci (the area under the normalized EGG cycle) and dEGGmaxN (the normalized maximum rate of change of vocal fold contact area) were obtained for all EGG cycles, using the FonaDyn system. We introduce also a compound metric Ic ,the ‘index of contact’ [Qci × log10(dEGGmaxN)], with the properties that it goes to zero at no contact. It combines information from both Qci and dEGGmaxN and thus it is comparable across subjects. The intra-subject means of all three metrics were computed and visualized by colour-coding over the fo-SPL plane, in cells of 1 semitone × 1 dB. Results: Overall, the use of flow ball-assisted phonation had a small yet significant effect on overall vocal fold contact across the whole messa di voce exercise. Larger effects were evident locally, i.e., in parts of the voice range. Comparing the pre-post flow-ball conditions, there were differences in Qci and/or dEGGmaxN. These differences were generally larger in male than in female voices. Ic typically decreased after flow ball use, for males but not for females. Conclusion: Flow ball-assisted training seems to modify vocal fold contacting gestures, especially in male singers.

The electroglottographic waveform is of interest for characterizing phonation non-invasively. Existing parameterizations tend to give disparate results because they rely on somewhat arbitrary thresholds and/or contacting events. It is shown that neither are needed for formulating a normalized contact quotient and a normalized peak derivative. A heuristic combination of the two resolves also the ambiguity of a moderate contact quotient, with regard to vocal fold contacting being firm versus weak or absent. As preliminaries, schemes for electroglottography signal preconditioning and time-domain period detection are described that improve somewhat on similar methods. The algorithms are simple and compute quickly.

Vocal sound imitations provide a new challenge for understanding the coupling between articulatory mechanisms and the resulting audio. In this study, we have modeled the classification of three articulatory categories, phonation, supraglottal myoelastic vibrations, and turbulence from audio recordings. Two data sets were assembled, consisting of different vocal imitations by four professional imitators and four non-professional speakers in two different experiments. The audio data were manually annotated by two experienced phoneticians using a detailed articulatory description scheme. A separate set of audio features was developed specifically for each category using both time-domain and spectral methods. For all time-frequency transformations, and for some secondary processing, the recently developed Auditory Receptive Fields Toolbox was used. Three different machine learning methods were applied for predicting the final articulatory categories. The result with the best generalization was found using an ensemble of multilayer perceptrons. The cross-validated classification accuracy was 96.8 % for phonation, 90.8 % for supraglottal myoelastic vibrations, and 89.0 % for turbulence using all the 84 developed features. A final feature reduction to 22 features yielded similar results.

A unified approach for the numerical simulation of vowels is presented, which accounts for the self-oscillations of the vocal folds including contact, the generation of acoustic waves and their propagation through the vocal tract, and the sound emission outwards the mouth. A monolithic incompressible fluid-structure interaction model is used to simulate the interaction between the glottal jet and the vocal folds, whereas the contact model is addressed by means of a level set application of the Eikonal equation. The coupling with acoustics is done through an acoustic analogy stemming from a simplification of the acoustic perturbation equations. This coupling is one-way in the sense that there is no feedback from the acoustics to the flow and mechanical fields. All the involved equations are solved together at each time step and in a single computational run, using the finite element method (FEM). As an application, the production of vowel [i] has been addressed. Despite the complexity of all physical phenomena to be simulated simultaneously, which requires resorting to massively parallel computing, the formant locations of vowel [i] have been well recovered.

Although it has been shown in previous research (Orlikoff, 1991; Henrich et al, 2005; Kuang et al, 2014; Awan, 2015) that there exists a relationship between the electroglottogram (EGG) waveform and the acoustic signal, this relationship is still not fully understood. To investigate this relationship, the EGG and acoustic signals were measured for four male amateur choir singers who each produced eight consecutive tones of increasing and decreasing vocal intensity. The EGG signals were processed cycle-synchronously to obtain the discrete Fourier transform, and the data were used as an input to a clustering algorithm. The acoustic signal was analyzed in terms of sound pressure level (dB SPL) and fundamental frequency (f(o)) of vibration, and the results of both EGG and acoustic analysis were depicted on a two-dimensional plane with f(o) on the x-axis and SPL on the y-axis. All the subjects were seen to have a weak, near-sinusoidal EGG waveform in their lowest SPL range, whereas increase in SPL coincided with progressive enrichment in harmonic content of the EGG waveforms. The results of the clustering were additionally used to classify waveforms across subjects to enable inter-subject comparisons and assessment of individual strategies of exploring the f(o)-SPL dimensions. In these male subjects, the EGG waveform shape appeared to vary with SPL and to remain essentially constant with f(o) over one octave.

Objectives

Good voice quality is an asset to professional voice users, including radio performers. We examined whether (1) voices could be reliably categorized as good for the radio and (2) these categories could be predicted using acoustic measures.

Participants and Methods

Male radio performers (n = 24) and age-matched male controls performed “The Rainbow Passage” as if presenting on the radio. Voice samples were rated using a three-stage paired-comparison paradigm by 51 naive listeners and perceptual categories were identified (Study 1), and then analyzed for fundamental frequency, long-term average spectrum, cepstral peak prominence, and pause or spoken-phrase duration (Study 2).

Results

Study 1: Good inter-judge reliability was found for perceptual judgments of the best 15 voices (good for radio category, 14/15 = radio performers), but agreement on the remaining 33 voices (unranked category) was poor. Study 2: Discriminant function analyses showed that the SD standard deviation of sounded portion duration, equivalent sound level, and smoothed cepstral peak prominence predicted membership of categories with moderate accuracy (R² = 0.328).

Conclusions

Radio performers are heterogeneous for voice quality; good voice quality was judged reliably in only 14 out of 24 radio performers. Current acoustic analyses detected some of the relevant signal properties that were salient in these judgments. More refined perceptual analysis and the use of other perceptual methods might provide more information on the complex nature of judging good voices.

Objectives: To investigate how the direct biofeedback on vocal loudness administered with a portable voice accumulator (VoxLog) should be configured, to facilitate an optimal learning outcome for individuals with Parkinson's disease (PD), on the basis of principles of motor learning. Study Design: Methodologic development in an experimental study. Methods: The portable voice accumulator VoxLog was worn by 20 participants with PD during habitual speech during semistructured conversations. Six different biofeedback configurations were used, in random order, to study which configuration resulted in a feedback frequency closest to 20% as recommended on the basis of previous studies. Results: Activation of feedback when the wearer speaks below a threshold level of 3dB below the speaker's mean voice sound level in habitual speech combined with an activation time of 500ms resulted in a mean feedback frequency of 21.2%. Conclusions: Settings regarding threshold and activation time based on the results from this study are recommended to achieve an optimal learning outcome when administering biofeedback on vocal loudness for individuals with PD using portable voice accumulators.

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.