Assessing and Quantifying Air Added to the Voice by Means of Laryngostroboscopic Imaging, EGG, and Acoustics in Vocally Trained Subjects

Towards Improved Auditory-Perceptual Assessment of Timbres: Comparing Accuracy and Reliability of Four Deconstructed Timbre Assessment Models

Timbre is a central quality of singing, yet remains a complex notion poorly understood in psychoacoustic studies. Previous studies note how no single acoustic variable or combinations of variables consistently predict timbre dimensions. Timbre varies on a continuum from darkest to lightest. These extremes are associated with laryngeal and vocal tract adjustments related to smaller and larger vocal tract area and variations in vocal fold vibratory characteristics. Perceptually, timbre assessment is influenced by spectral characteristics and formant frequency adjustments, though these dimensions are not independently perceived. Perceptual studies repeatedly demonstrate difficulties in correlating variations in timbre stimuli to specific measures. A recent study demonstrated how acoustic predictive salience of voice category and voice weight across pitches contribute to timbre assessments and concludes that timbre may be related to as-of-yet unknown factor(s). The purpose of this study was to test four different models for assessing timbre; one model focused on specific anatomy, one on listener intuition, one utilizing auditory anchors, and one using expert raters in a deconstructed timbre model with five specific dimensions.

METHODS

Four independent panels were conducted with separate cohorts of professional singing teachers. Forty-one assessors took part in the anatomically focused panel, 54 in the intuition-based panel, 30 in the anchored panel, and 12 in the expert listener panel. Stimuli taken from live performances of well-known singers were used for all panels, representing all genders, genres, and styles across a large pitch range. All stimuli are available as Supplementary Materials. Fleiss' kappa values, descriptive statistics, and significance tests are reported for all panel assessments.

RESULTS

Panels 1 through 4 varied in overall accuracy and agreement. The intuition-based model showed overall 45% average accuracy (SD ± 4%), k = 0.289 (<0.001) compared to overall 71% average accuracy (SD ± 3%), k = 0.368 (<0.001) of the anatomical focused panel. The auditory-anchored model showed overall 75% average accuracy (SD ± 8%), k = 0.54 (<0.001) compared with overall 83% average accuracy and agreement of k = 0.63 (<0.001) for panel 4. Results revealed that the highest accuracy and reliability were achieved in a deconstructed timbre model and that providing anchoring improved reliability but with no further increase in accuracy.

CONCLUSION

Deconstructing timbre into specific parameters improved auditory perceptual accuracy and overall agreement. Assessing timbre along with other perceptual dimensions improves accuracy and reliability. Panel assessors' expert level of listening skills remain an important factor in obtaining reliable and accurate assessments of auditory stimuli for timbre dimensions. Anchoring improved reliability but with no further increase in accuracy. The study suggests that timbre assessment can be improved by approaching the percept through a prism of five specific dimensions each related to specific physiology and auditory-perceptual subcategories. Further tests are needed with framework-naïve listeners, nonmusically educated listeners, artificial intelligence comparisons, and synthetic stimuli to further test the reliability.

Towards Automated Vocal Mode Classification in Healthy Singing Voice-An XGBoost Decision Tree-Based Machine Learning Classifier

Auditory-perceptual assessment is widely used in clinical and pedagogical practice for speech and singing voice, yet several studies have shown poor intra- and inter-rater reliability in both clinical and singing voice contexts. Recent advances in artificial intelligence and machine learning offer models for automated classification and have demonstrated discriminatory power in both pathological and healthy voice. This study develops and tests an XGBoost decision tree based machine learning classifier to develop automated vocal mode classification in healthy singing voice. Classification models trained on mel-frequency cepstrum coefficients, MFCC-Zero-Time Windowing, glottal features, voice quality features, and α-ratios demonstrated 92% average F1-score accuracy in distinguishing metallic and non-metallic singing for male singers and 87% average F1-score for female singers. The model distinguished vocal modes with 70% and 69% average F1-score for male and female samples, respectively. Model performance was compared to human auditory-perceptual assessments of 64 corresponding samples performed by 41 professional singers. The model performed with approximating or subpar performance to human assessors on task-matched problems. The XGBoost gains observed across tested features reveal that the most important attributes for the tested classification problems were MFCCs and α-ratios between high and low frequency energy, with models trained on only these features achieving performance not statistically significantly different from the best tested models. The best automated models in this study do not yet match human auditory-perceptual discrimination but improve on previously reported F1-average accuracies in automated classification in singing voice.

Spectral and cepstral measurements in women with behavioral dysphonia

PURPOSE

To investigate whether there are differences in cepstral and spectral acoustic measures between women with behavioral dysphonia with and without laryngeal lesions and verify whether there is a correlation between such measures and the auditory-perceptual evaluation of voice quality.

METHODS

The sample comprised 78 women with behavioral dysphonia without laryngeal lesions (BDWOL) and 68 with behavioral dysphonia with laryngeal lesions (vocal nodules) (BDWL). Cepstral peak prominence (CPP), cepstral peak prominence-smoothed (CPPS), spectral decrease, and H1-H2 (difference between the amplitude of the first and second harmonics) were extracted. They were submitted to the auditory-perceptual evaluation (APE) of the grade of hoarseness (GH), roughness (RO), breathiness (BR), and strain (ST).

RESULTS

BDWL women had higher H1-H2 values and lower CPP and CPPS values than BDWOL women. More deviant voices had lower CPP and CPPS values. Breathy voices had lower CPP and CPPS values and higher H1-H2 values than rough ones. There was a weak negative correlation between CPP and RO, a moderate negative correlation with GH, and a strong negative correlation with BR. CPPS had a moderate negative correlation with GH, RO, and BR. H1-H2 had a weak positive correlation with BR. There was a weak positive correlation between spectral decrease and ST.

CONCLUSION

H1-H2, CPP, and CPPS were different between BDWOL and BDWL women. Furthermore, cepstral and spectral measures were correlated with the different APE parameters.

Extreme Vocals-A Retrospective Longitudinal study of Vocal Health in 20 Professional Singers Performing and Teaching Rough Vocal Effects

BACKGROUND

Rough vocal effects, extreme, or extended vocal techniques to sound intentionally hoarse or rough are an integral part of many genres and styles, and research has recently demonstrated the involvement of supraglottic narrowing and vibrations to produce such sounds. The vocal health of singing with rough vocal effects is poorly documented, especially in a longitudinal manner, while much vocal pedagogy continuously treats the sounds as harming to or dangerous for the vocal mechanism.

OBJECTIVE

To longitudinally investigate the vocal health of professional singers who perform the five rough-sounding vocal effects Distortion, Growl, Grunt, Rattle, and Creaking as part of their singing and teaching.

METHODS

Twenty singers underwent nasoendoscopic examination, filled in SVHI questionnaires, and were assessed by GRBAS with a 14-year interval in a retrospective longitudinal study (from 2007 to 2021). Endoscopic materials were assessed by Reflux Finding Score and a hybrid version of the Stroboscopy Rating Scale.

RESULTS

Singers presented at initiation of study with an average SVHI of 9.2 (±9), which decreased at time of follow up 14 years later to an average of 5.12 (±6). Laryngeal assessments (RFS and SRS) revealed low averages at initiation of study as well as at conclusion of the study with only small fluctuations in averages, with findings mainly relating to arytenoid asymmetry.

CONCLUSION

The participating singers perform and teach rough vocal effects continually and present with healthy laryngeal mechanisms and within-normal SVHI and GRBAS scores. The findings suggest that controlled supraglottic narrowing and techniques to allow for supraglottic structures to engage in vibration as an additional noise source can be performed sustainable and in a healthy manner if performed with correct vocal technique.

Towards a Singing Voice Multi-Sensor Analysis Tool: System Design, and Assessment Based on Vocal Breathiness

Singing voice is a human quality that requires the precise coordination of numerous kinetic functions and results in a perceptually variable auditory outcome. The use of multi-sensor systems can facilitate the study of correlations between the vocal mechanism kinetic functions and the voice output. This is directly relevant to vocal education, rehabilitation, and prevention of vocal health issues in educators; professionals; and students of singing, music, and acting. In this work, we present the initial design of a modular multi-sensor system for singing voice analysis, and describe its first assessment experiment on the ‘vocal breathiness’ qualitative characteristic. A system case study with two professional singers was conducted, utilizing signals from four sensors. Participants sung a protocol of vocal trials in various degrees of intended vocal breathiness. Their (i) vocal output, (ii) phonatory function, and (iii) respiratory behavior-per-condition were recorded through a condenser microphone (CM), an Electroglottograph (EGG), and thoracic and abdominal respiratory effort transducers (RET), respectively. Participants’ individual respiratory management strategies were studied through qualitative analysis of RET data. Microphone audio samples breathiness degree was rated perceptually, and correlation analysis was performed between sample ratings and parameters extracted from CM and EGG data. Smoothed Cepstral Peak Prominence (CPPS) and vocal folds’ Open Quotient (OQ), as computed with the Howard method (HOQ), demonstrated the higher correlation coefficients, when analyzed individually. DECOM method-computed OQ (DOQ) was also examined. Interestingly, the correlation coefficient of pitch difference between estimates from CM and EGG signals appeared to be (based on the Pearson correlation coefficient) statistically insignificant (a result that warrants investigation in larger populations). The study of multi-variate models revealed even higher correlation coefficients. Models studied were the Acoustic Breathiness Index (ABI) and the proposed multiple regression model CDH (CPPS, DOQ, and HOQ), which was attempted in order to combine analysis results from microphone and EGG signals. The model combination of ABI and the proposed CDH appeared to yield the highest correlation with perceptual breathiness ratings. Study results suggest potential for the use of a completed system version in vocal pedagogy and research, as the case study indicated system practicality, a number of pertinent correlations, and introduced topics with further research possibilities.