Search Results (2)

Singing pedagogy has increasingly adopted guide awareness through the use of meaningful real-time visual feedback. Technology typically used to study the voice can also be applied in a singing lesson, aiming at facilitating students’ awareness of the three subsystems involved in voice production—breathing, oscillatory and resonatory—and their underlying physiological, aerodynamical and acoustical mechanisms. Given the variety of real-time visual feedback tools, this article provides a comprehensive overview of such tools and their current and future pedagogical applications in the voice studio. The rationale for using real-time visual feedback is discussed, including both the theoretical and practical applications of visualizing physiological, aerodynamical and acoustical aspects of voice production. The monitorization of breathing patterns is presented, displaying lung volume as the sum of abdominal and ribcage movements signals. In addition, estimates of subglottal pressure are visually displayed using a subglottal pressure meter to assist with the shaping of musical phrases in singing. As to what concerns vibratory patterns of the vocal folds and phonatory airflow, the use of electroglottography and inverse filters is applied to monitor the phonation types, voice breaks, pitch and intensity range of singers of different music genres. These vocal features, together with intentional voice distortions and intonation adjustments, are also displayed using spectrographs. As the voice is invisible to the eye, the use of real-time visual feedback is proposed as a key pedagogical approach in current and future singing lessons. The use of such an approach corroborates the current trend of developing evidence-based practices in voice education. Full article

The term “bulbar involvement” is employed in ALS to refer to deterioration of motor neurons within the corticobulbar area of the brainstem, which results in speech and swallowing dysfunctions. One of the primary symptoms is a deterioration of the voice. Early detection is crucial for improving the quality of life and lifespan of ALS patients suffering from bulbar involvement. The main objective, and the principal contribution, of this research, was to design a new methodology, based on the phonatory-subsystem and time-frequency characteristics for detecting bulbar involvement automatically. This study focused on providing a set of 50 phonatory-subsystem and time-frequency features to detect this deficiency in males and females through the utterance of the five Spanish vowels. Multivariant Analysis of Variance was then used to select the statistically significant features, and the most common supervised classifications models were analyzed. A set of statistically significant features was obtained for males and females to capture this dysfunction. To date, the accuracy obtained (98.01% for females and 96.10% for males employing a random forest) outperformed the models in the literature. Adding time-frequency features to more classical phonatory-subsystem features increases the prediction capabilities of the machine-learning models for detecting bulbar involvement. Studying men and women separately gives greater success. The proposed method can be deployed in any kind of recording device (i.e., smartphone). Full article