Multisensory biofeedback: Promoting the recessive somatosensory control in operatic singing pedagogy

Angelakis, Evangelos; Andreopoulou, Areti; Georgaki, Anastasia

doi:10.1016/j.bspc.2020.102400

Cited by 5 publications

(5 citation statements)

References 61 publications

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“…where w d and w b are the observation noise and R is the distance from the hip joint to the sensor. Similarly, it is assumed that the observation noise is Gaussian noise with independent 0-means [17]:…”

Section: Methodsmentioning

confidence: 99%

[Retracted] Sensor‐Based Exercise Rehabilitation Robot Training Method

Xie

Zhang

2023

Journal of Sensors

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In order to provide convenience for rehabilitation doctors to formulate rehabilitation plans for patients, this paper proposes a training method for exercise rehabilitation robots based on sensors. In this research, the customized wearable sensor and universal mobile terminal are used as the hardware. Based on the sensor, the motion capture algorithm and motion reconstruction algorithm are developed. The table of experimental results shows that the cost can be saved by using the sensor, and the data can be captured accurately, which can meet the needs of rehabilitation medicine for motor function evaluation and training guidance. The range of motion of the joint and the manual measurement value are within 5°, which can meet the needs of rehabilitation medicine for motor function evaluation and training guidance. The system delay is less than 0.5 s, which has good real-time performance and can respond quickly to emergencies, ensuring the safety of patients’ out-of-hospital rehabilitation. The training method of motion rehabilitation robot based on sensor is helpful for rehabilitation doctors to carry out statistical data of functional evaluation and is of great significance for rehabilitation doctors to make training plans for patients and carry out rehabilitation training.

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Section: Methodsmentioning

confidence: 99%

[Retracted] Sensor‐Based Exercise Rehabilitation Robot Training Method

Xie

Zhang

2023

Journal of Sensors

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“…Professional operatic singers have been used as participants in research studies pertinent to the singing voice, as their extremely demanding profession [ 10 ] requires extensive training and practice. The genre generally known as “operatic singing” or “Western classical singing” is furthermore one of the most demanding types of vocal music.…”

Section: Methodsmentioning

confidence: 99%

“…Such a venture requires the employment of more complex systems. In a previous positional/review journalpaper, analyzing the reported decline of operatic singing quality, as well as the challenges and recourse of vocal pedagogy [ 10 ], we have suggested the need for development of research and education tools, utilizing multiple sensors to transduce data regarding primary kinematic and acoustic functions of the vocal mechanism. We also proposed that a series of experiments be actualized, aiming towards the clarification of biomechanical-acoustical correlations.…”

Section: Introductionmentioning

confidence: 99%

“…Another study on singers employing both invasive and non-invasive sensors regarded the definition of “supported singing voice” through the comparison of the singers’ self-perception of supported and un-supported singing with quantitative measures [ 26 ]. Additional sensors that can be (or have already been) used for singing voice biofeedback include spirometers, skeleton tracking cameras, videokymograph, and functional magnetic resonance imaging devices [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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

Angelakis

Kotsani

Georgaki

2021

Sensors

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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.

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“…A significant number of singing tutoring interfaces focus on the evaluation of vocal qualities which can be detected by computer audio analysis, such as pitch accuracy [8] [9], formants [10], harmonics [9], vibrato [11] [12], larynx closed quotient [12], and vocal energy [13]. Receiving the above scores help with evaluating the performances, but the learners need more direct tutoring on how to improve their basic singing skills.…”

Section: Singing Tutoring Interfacementioning

confidence: 99%

Sensing the Breath: A Multimodal Singing Tutoring Interface with Breath Guidance

Piao¹,

Xia²

Nime 2022

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Breath is a significant component in singing performance, which is still underresearched in most singing-related music interfaces. In this paper, we present a multimodal system that detects the learner's singing pitch and breathing states and provides real-time visual tutoring feedback. Specifically, the breath detector is a wearable belt with pressure sensors and flexible fabric. It monitors real-time body movement of the abdomen, back waist, and twin ribs. A breath visualization algorithm is developed to display real-time breath states, together with the singing pitch contours on an interactive score interface. User studies show that our system can help users not only gain deeper breath during singing but also improve pitch accuracy in vocal training, especially for those with some musical background.

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Multisensory biofeedback: Promoting the recessive somatosensory control in operatic singing pedagogy

Cited by 5 publications

References 61 publications

[Retracted] Sensor‐Based Exercise Rehabilitation Robot Training Method

[Retracted] Sensor‐Based Exercise Rehabilitation Robot Training Method

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

Sensing the Breath: A Multimodal Singing Tutoring Interface with Breath Guidance

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