Correlations between oropharyngeal and laryngeal anatomy and the frequency of singing voice: A population-based study, in Greece

The aim of this study is to explore if there is correlation between the typical voice classification and oropharyngeal anatomy, using cervical posterior-anterior radiography on professional singers in Epirus, Greece. Methods: 70 professional singers, 35 men and 35 women, were recruited for this study. All participants underwent a cervical posterior-anterior radiographic imaging of their oral pharyngeal and laryngeal area. Results: A statistically significant difference of mean distance was observed for the CI-MHP area (p=0,004), the MHP- SCV area (F=2,62, p=0,032), as well as SCV-AI area (F=11,82, p=0,000). For the average length measured in mm of the phonetic area PA, statistically significant differences were computed among all the singers in the group (F [5] = 5.368, p = 0.001), as well as the OPC area (F = 6,48, p = 0,000). Conclusions: The cervical posteroanterior radiography provided new correlations of the voice category of professional singers with their Oropharyngeal and Laryngeal Anatomy.

Identifying individuals with recent COVID-19 through voice classification using deep learning

Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration in COVID-19 patients after the recent resolution of disease using deep learning. The study was a prospective study of 76 post COVID-19 patients and 40 healthy individuals. The diagnoses of post COVID-19 patients were based on more than the eighth week after onset of symptoms. Voice samples of an ‘ah’ sound, coughing sound and a polysyllabic sentence were collected and preprocessed to log-mel spectrogram. Transfer learning using the VGG19 pre-trained convolutional neural network was performed with all voice samples. The performance of the model using the polysyllabic sentence yielded the highest classification performance of all models. The coughing sound produced the lowest classification performance while the ability of the monosyllabic ‘ah’ sound to predict the recent COVID-19 fell between the other two vocalizations. The model using the polysyllabic sentence achieved 85% accuracy, 89% sensitivity, and 77% specificity. In conclusion, deep learning is able to detect the subtle change in voice features of COVID-19 patients after recent resolution of the disease.

Recognition System Of The Al Qur’an Surah Al-Falaq Verse 1-5 Through Voice Using Ada-Boost

In everyday life, it is not uncommon when we hear the sound of chanting the holy verses of the Al Al Qur’an which are read in mosques before prayer time or in other conditions we seem interested in knowing what Surah and which verse is being recited. This is due to the love of Muslims themselves for the Al Qur’an but not all Muslims memorize the entire contents of the Al Qur’an . Based on the limitations and the magnitude of curiosity about Surah and Verse information, the writer is interested in developing a computer system that can recognize and provide information on the recited Surah and Verse. Advances in computer technology not only make it easier for humans to carry out activities. One of the human intelligences that are planted into computer technology is to recognize the verses of the Al Al Qur’an Surah Al-Falaq through voice. Ada-Boost method is one method to identify or recognize voice classification, and by using this method the success rate in recognizing verse numbers reaches 72%. This system can only recognize the number of verses of the Al Al Qur’an Surah Al-Falaq, recorded sound files with the .wav file extension and built using the Delphi programming language.

On a Vector towards a Novel Hearing Aid Feature: What Can We Learn from Modern Family, Voice Classification and Deep Learning Algorithms

(1) Background: The application of machine learning techniques in the speech recognition literature has become a large field of study. Here, we aim to (1) expand the available evidence for the use of machine learning techniques for voice classification and (2) discuss the implications of such approaches towards the development of novel hearing aid features (i.e., voice familiarity detection). To do this, we built and tested a Convolutional Neural Network (CNN) Model for the identification and classification of a series of voices, namely the 10 cast members of the popular television show “Modern Family”. (2) Methods: Representative voice samples were selected from Season 1 of Modern Family (N = 300; 30 samples for each of the classes of the classification in this model, namely Phil, Claire, Hailey, Alex, Luke, Gloria, Jay, Manny, Mitch, Cameron). The audio samples were then cleaned and normalized. Feature extraction was then implemented and used as the input to train a basic CNN model and an advanced CNN model. (3) Results: Accuracy of voice classification for the basic model was 89%. Accuracy of the voice classification for the advanced model was 99%.; (4) Conclusions: Greater familiarity with a voice is known to be beneficial for speech recognition. If a hearing aid can eventually be programmed to recognize voices that are familiar or not, perhaps it can also apply familiar voice features to improve hearing performance. Here we discuss how such machine learning, when applied to voice recognition, is a potential technological solution in the coming years.

Ag2Se Nanowire Network as an Effective In-Materio Reservoir Computing Device

Modern applications of artificial intelligence (AI) are generally algorithmic in nature and implemented using either general-purpose or application-specific hardware systems that have high power requirements. In the present study, physical (in-materio) reservoir computing (RC) implemented in hardware was explored as an alternative to software-based AI. The device, made up of a random, highly interconnected network of nonlinear Ag2Se nanojunctions, demonstrated the requisite characteristics of an in-materio reservoir, including but not limited to nonlinear switching, memory, and higher harmonic generation. As a hardware reservoir, the devices successfully performed waveform generation tasks, where tasks conducted at elevated network temperatures were found to be more stable than those conducted at room temperature. Finally, a comparison of voice classification, with and without the network device, showed that classification performance increased in the presence of the network device.