Search Results (9)

This study explores the relationship between formant frequencies and the directivity patterns of the Greek singing voice. Recordings were conducted in a controlled acoustic environment with four professional singers, two trained in classical music and two in Byzantine chant. Using microphones placed symmetrically on a hemispherical structure, participants sang the Greek vowels across different registers. Directivity patterns were analyzed in third-octave bands centered on each singer’s first three formant frequencies (F1, F2, F3). The results indicate that directivity patterns vary with register and center frequency, with differences observed across vowels and singers. These findings contribute to vocal production research and the development of simulation, auralization, and virtual reality applications for speech and music. Full article

The estimation of the frequency of sinusoids has been the object of intense research for more than 40 years. Its importance in classical fields such as telecommunications, instrumentation, and medicine has been extended to numerous specific signal processing applications involving, for example, speech, audio, and music processing. In many cases, these applications run in real-time and, thus, require accurate, fast, and low-complexity algorithms. Taking the normalized Cramér–Rao lower bound as a reference, this paper evaluates the relative performance of nine non-iterative discrete Fourier transform-based individual sinusoid frequency estimators when the target sinusoid is affected by full-bandwidth quasi-harmonic interference, in addition to stationary noise. Three levels of the quasi-harmonic interference severity are considered: no harmonic interference, mild harmonic interference, and strong harmonic interference. Moreover, the harmonic interference is amplitude-modulated and frequency-modulated reflecting real-world conditions, e.g., in singing and musical chords. Results are presented for when the Signal-to-Noise Ratio varies between −10 dB and 70 dB, and they reveal that the relative performance of different frequency estimators depends on the SNR and on the selectivity and leakage of the window that is used, but also changes drastically as a function of the severity of the quasi-harmonic interference. In particular, when this interference is strong, the performance curves of the majority of the tested frequency estimators collapse to a few trends around and above 0.4% of the DFT bin width. Full article

Vocal arts medicine provides care and prevention strategies for professional voice disorders in performing artists. The issue of correct “Fach” determination depending on the presence of a lyric or dramatic voice structure is of crucial importance for opera singers, as chronic overuse often leads to vocal fold damage. To avoid phonomicrosurgery or prevent a premature career end, our aim is to offer singers an improved, objective fach counseling using digital sound analyses and machine learning procedures. For this purpose, a large database of 2004 sound samples from professional opera singers was compiled. Building on this dataset, we employed a classic ensemble learning method, namely the Random Forest algorithm, to construct an efficient fach classifier. This model was trained to learn from features embedded within the sound samples, subsequently enabling voice classification as either lyric or dramatic. As a result, the developed system can decide with an accuracy of about 80% in most examined voice types whether a sound sample has a lyric or dramatic character. To advance diagnostic tools and health in vocal arts medicine and singing voice pedagogy, further machine learning methods will be applied to find the best and most efficient classification method based on artificial intelligence approaches. Full article

To date, no established protocol exists for measuring functional voice changes in singers with subclinical singing-voice complaints. Hence, these may go undiagnosed until they progress into greater severity. This exploratory study sought to (1) determine which scale items in the self-perceptual Evaluation of Ability to Sing Easily (EASE) are associated with instrumental voice measures, and (2) construct as proof-of-concept an instrumental index related to singers’ perceptions of their vocal function and health status. Eighteen classical singers were acoustically recorded in a controlled environment singing an /a/ vowel using soft phonation. Aerodynamic data were collected during a softly sung /papapapapapapa/ task with the KayPENTAX Phonatory Aerodynamic System. Using multi and univariate linear regression techniques, CPPS, vibrato jitter, vibrato shimmer, and an efficiency ratio (SPL/P_Sub) were included in a significant model (p < 0.001) explaining 62.4% of variance in participants’ composite scores of three scale items related to vocal fatigue. The instrumental index showed a significant association (p = 0.001) with the EASE vocal fatigue subscale overall. Findings illustrate that an aeroacoustic instrumental index may be useful for monitoring functional changes in the singing voice as part of a multidimensional diagnostic approach to preventative and rehabilitative voice healthcare for professional singing-voice users. Full article

Singing voice directivity for five sustained German vowels /a:/, /e:/, /i:/, /o:/, /u:/ over a wide pitch range was investigated using a multichannel microphone array with high spatial resolution along the horizontal and vertical axes. A newly created dataset allows to examine voice directivity in classical singing with high resolution in angle and frequency. Three voice production modes (phonation modes) modal, breathy, and pressed that could affect the used mouth opening and voice directivity were investigated. We present detailed results for singing voice directivity and introduce metrics to discuss the differences of complex voice directivity patterns of the whole data in a more compact form. Differences were found between vowels, pitch, and gender (voice types with corresponding vocal range). Differences between the vowels /a:, e:, i:/ and /o:, u:/ and pitch can be addressed by simplified metrics up to about d2/D5/587 Hz, but we found that voice directivity generally depends strongly on pitch. Minor differences were found between voice production modes and found to be more pronounced for female singers. Voice directivity differs at low pitch between vowels with front vowels being most directional. We found that which of the front vowels is most directional depends on the evaluated pitch. This seems to be related to the complex radiation pattern of the human voice, which involves a large inter-subjective variability strongly influenced by the shape of the torso, head, and mouth. All recorded classical sung vowels at high pitches exhibit similar high directionality. Full article

Vitamin D has well-defined classical functions related to metabolism and bone health but also has non-classical effects that may influence pregnancy. Maternal morbidity remains a significant health care concern worldwide, despite efforts to improve maternal health. Nutritional deficiencies of vitamin D during pregnancy are related to adverse pregnancy outcomes, but the evidence base is difficult to navigate. The primary purpose of this review is to map the evidence on the effects of deficiencies of vitamin D on pregnancy outcome and the dosage used in such studies. A systematic search was performed for studies on vitamin D status during pregnancy and maternal outcomes. A total of 50 studies came from PubMed, 15 studies came from Cochrane, and 150 studies came from Embase, for a total of 215 articles. After screening, 34 were identified as candidate studies for inclusion. Finally, 28 articles met the inclusion criteria, which originated from 15 countries. The studies included 14 original research studies and 13 review studies conducted between 2012 and 2021. This review was finally limited to the 14 original studies. This systematic review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, and the quality and strength of the evidence was evaluated using the Navigation Guide Systematic Review Methodology (SING). We found evidence that supports the idea that supplementary vitamin D for pregnant women is important for reducing the risk of gestational diabetes, hypertension, preeclampsia, early labor, and other complications. The data retrieved from this review are consistent with the hypothesis that adequate vitamin D levels might contribute to a healthy pregnancy. Full article

Introduction: Opera singers are continuously subjected to cardiopulmonary exercise. The impact on cardiac performance has not been studied. Our aim was to verify the impact of singing on heart performance, particularly by the evaluation of ECG and deformation parameters as strain, rotation and twist. Methods: A population of 17 OS (opera singers) underwent a 12-lead ECG and 2D echocardiographic evaluation. A post-processing analysis of the images to obtain the deformation parameters was included. The data expressed as mean as SD were compared to a group of 15 high-level athletes (A). Results: In both groups, the ECG parameters, 2D standard systodiastolic parameters and pulmonary pressure were normal, and in the OS group—LVDd: 47 ± 2.75 mm, LVSd: 31 ± 3.38 mm, E/A: 1.08 ± 0.23, RV: 27.63 ± 3.38 mm; in the A group—LVDd: 51 ± 1.50 mm, LVSd: 32 ± 2.50 mm, E/A: 2.37 ± 0.73, RV: 25.00 ± 3.00 mm. Indexed LV mass was significantly greater in athletes, while ejection fraction (EF) results were higher in OS. Deformation parameters did not differ among the two groups, with the exclusion of GLS expressing a major value in athletes. Rotational parameters resulted in the OS group similar to the athletes. Conclusions: OS show myocardial performance as high as the athletes. The data obtained suggest a positive impact of regular training as an opera singer. Deformation parameters highlight the fitness status in this group with a specific remodeling in RV in the presence of normal PP. Classic music singing appears to have a training effect on the heart. Further studies are necessary to confirm this hypothesis. Full article

In this publication, we present a deep learning-based method to transform the $f_0$ in speech and singing voice recordings. $f_0$ transformation is performed by training an auto-encoder on the voice signal’s mel-spectrogram and conditioning the auto-encoder on the $f_0$. Inspired by AutoVC/F0, we apply an information bottleneck to it to disentangle the $f_0$ from its latent code. The resulting model successfully applies the desired $f_0$ to the input mel-spectrograms and adapts the speaker identity when necessary, e.g., if the requested $f_0$ falls out of the range of the source speaker/singer. Using the mean $f_0$ error in the transformed mel-spectrograms, we define a disentanglement measure and perform a study over the required bottleneck size. The study reveals that to remove the $f_0$ from the auto-encoder’s latent code, the bottleneck size should be smaller than four for singing and smaller than nine for speech. Through a perceptive test, we compare the audio quality of the proposed auto-encoder to $f_0$ transformations obtained with a classical vocoder. The perceptive test confirms that the audio quality is better for the auto-encoder than for the classical vocoder. Finally, a visual analysis of the latent code for the two-dimensional case is carried out. We observe that the auto-encoder encodes phonemes as repeated discontinuous temporal gestures within the latent code. Full article

Singing voice detection or vocal detection is a classification task that determines whether there is a singing voice in a given audio segment. This process is a crucial preprocessing step that can be used to improve the performance of other tasks such as automatic lyrics alignment, singing melody transcription, singing voice separation, vocal melody extraction, and many more. This paper presents a survey on the techniques of singing voice detection with a deep focus on state-of-the-art algorithms such as convolutional LSTM and GRU-RNN. It illustrates a comparison between existing methods for singing voice detection, mainly based on the Jamendo and RWC datasets. Long-term recurrent convolutional networks have reached impressive results on public datasets. The main goal of the present paper is to investigate both classical and state-of-the-art approaches to singing voice detection. Full article