Differences Among Mixed, Chest, and Falsetto Registers: A Multiparametric Study.

INTRODUCTION: Typical singing registers are the chest and falsetto; however, trained singers have an additional register, namely, the mixed register. The mixed register, which is also called "mixed voice" or "mix," is an important technique for singers, as it can help bridge from the chest voice to falsetto without noticeable voice breaks. OBJECTIVE: The present study aims to reveal the nature of the voice-production mechanism of the different registers (chest, mix, and falsetto) using high-speed digital imaging (HSDI), electroglottography (EGG), and acoustic and aerodynamic measurements. STUDY DESIGN: Cross-sectional study. METHODS: Aerodynamic measurements were acquired for twelve healthy singers (six men and women) during the phonation of a variety of pitches using three registers. HSDI and EGG devices were simultaneously used on three healthy singers (two men and one woman) from which an open quotient (OQ) and speed quotient (SQ) were detected. Audio signals were recorded for five sustained vowels, and a spectral analysis was conducted to determine the amplitude of each harmonic component. Furthermore, the absolute (not relative) value of the glottal volume flow was estimated by integrating data obtained from the HSDI and aerodynamic studies. RESULTS: For all singers, the subglottal pressure (PSub) was the highest for the chest in the three registers, and the mean flow rate (MFR) was the highest for the falsetto. Conversely, the PSub of the mix was as low as the falsetto, and the MFR of the mix was as low as the chest. The HSDI analysis showed that the OQ differed significantly among the registers, even when the fundamental frequency was the same; the OQ of the mix was higher than that of the chest but lower than that of the falsetto. The acoustic analysis showed that, for the mix, the harmonic structure was intermediate between the chest and falsetto. The results of the glottal volume-flow analysis revealed that the maximum volume velocity was the least for the mix register at every fundamental frequency. The first and second harmonic (H1-H2) difference of the voice source spectrum was the greatest for the falsetto, then the mix, and finally, the chest. CONCLUSIONS: We found differences in the registers in terms of the aeromechanical mechanisms and vibration patterns of the vocal folds. The mixed register proved to have a distinct voice-production mechanism, which can be differentiated from those of the chest or falsetto registers.

Automatic GRBAS Scoring of Pathological Voices using Deep Learning and a Small Set of Labeled Voice Data.

OBJECTIVES: Auditory-perceptual evaluation frameworks, such as the grade-roughness-breathiness-asthenia-strain (GRBAS) scale, are the gold standard for the quantitative evaluation of pathological voice quality. However, the evaluation is subjective; thus, the ratings lack reproducibility due to inter- and intra-rater variation. Prior researchers have proposed deep-learning-based automatic GRBAS score estimation to address this problem. However, these methods require large amounts of labeled voice data. Therefore, this study investigates the potential of automatic GRBAS estimation using deep learning with smaller amounts of data. METHODS: A dataset consisting of 300 pathological sustained /a/ vowel samples was created and rated by eight experts (200 for training, 50 for validation, and 50 for testing). A neural network model that predicts the probability distribution of GRBAS scores from an onset-to-offset waveform was proposed. Random speed perturbation, random crop, and frequency masking were investigated as data augmentation techniques, and power, instantaneous frequency, and group delay were investigated as time-frequency representations. RESULTS: Five-fold cross-validation was conducted, and the automatic scoring performance was evaluated using the quadratic weighted Cohen's kappa. The results showed that the kappa values of the automatic scoring performance were comparable to those of the inter-rater reliability of experts for all GRBAS items and the intra-rater reliability of experts for items G, B, A, and S. Random speed perturbation was the most effective data augmentation technique overall. When data augmentation was applied, power was the most effective for items G, R, A, and S; for Item B, combining group delay and power yielded additional performance gains. CONCLUSION: The automatic GRBAS scoring achieved by the proposed model using scant labeled data was comparable to that of experts. This suggests that the challenges resulting from insufficient data can be alleviated. The findings of this study can also contribute to performance improvements in other tasks such as automatic voice disorder detection.

Environmental DNA reflects spatial and temporal jellyfish distribution.

Recent development of environmental DNA (eDNA) analysis allows us to survey underwater macro-organisms easily and cost effectively; however, there have been no reports on eDNA detection or quantification for jellyfish. Here we present the first report on an eDNA analysis of marine jellyfish using Japanese sea nettle (Chrysaora pacifica) as a model species by combining a tank experiment with spatial and temporal distribution surveys. We performed a tank experiment monitoring eDNA concentrations over a range of time intervals after the introduction of jellyfish, and quantified the eDNA concentrations by quantitative real-time PCR. The eDNA concentrations peaked twice, at 1 and 8 h after the beginning of the experiment, and became stable within 48 h. The estimated release rates of the eDNA in jellyfish were higher than the rates previously reported in fishes. A spatial survey was conducted in June 2014 in Maizuru Bay, Kyoto, in which eDNA was collected from surface water and sea floor water samples at 47 sites while jellyfish near surface water were counted on board by eye. The distribution of eDNA in the bay corresponded with the distribution of jellyfish inferred by visual observation, and the eDNA concentration in the bay was ~13 times higher on the sea floor than on the surface. The temporal survey was conducted from March to November 2014, in which jellyfish were counted by eye every morning while eDNA was collected from surface and sea floor water at three sampling points along a pier once a month. The temporal fluctuation pattern of the eDNA concentrations and the numbers of observed individuals were well correlated. We conclude that an eDNA approach is applicable for jellyfish species in the ocean.

Environmental DNA as a 'Snapshot' of Fish Distribution: A Case Study of Japanese Jack Mackerel in Maizuru Bay, Sea of Japan.

Recent studies in streams and ponds have demonstrated that the distribution and biomass of aquatic organisms can be estimated by detection and quantification of environmental DNA (eDNA). In more open systems such as seas, it is not evident whether eDNA can represent the distribution and biomass of aquatic organisms because various environmental factors (e.g., water flow) are expected to affect eDNA distribution and concentration. To test the relationships between the distribution of fish and eDNA, we conducted a grid survey in Maizuru Bay, Sea of Japan, and sampled surface and bottom waters while monitoring biomass of the Japanese jack mackerel (Trachurus japonicus) using echo sounder technology. A linear model showed a high R(2) value (0.665) without outlier data points, and the association between estimated eDNA concentrations from the surface water samples and echo intensity was significantly positive, suggesting that the estimated spatial variation in eDNA concentration can reflect the local biomass of the jack mackerel. We also found that a best-fit model included echo intensity obtained within 10-150 m from water sampling sites, indicating that the estimated eDNA concentration most likely reflects fish biomass within 150 m in the bay. Although eDNA from a wholesale fish market partially affected eDNA concentration, we conclude that eDNA generally provides a 'snapshot' of fish distribution and biomass in a large area. Further studies in which dynamics of eDNA under field conditions (e.g., patterns of release, degradation, and diffusion of eDNA) are taken into account will provide a better estimate of fish distribution and biomass based on eDNA.