A device, system and related methods to provide assessment and treatment of amblyaudia through standardized methods that do not require advanced training or a booth with loudspeakers for the operator to administer. The ARIA stimuli protocols for both assessment and treatment, encoded in or to be used by a software program or application, are transferred to a stand-alone set of specialized noise-cancelling headphones attached or connected to, wired or wirelessly, a software platform on an electronic computing device. or integrated with the headphones. The program administers assessment tests to individuals through the noise-cancelling earphones. The device enables someone with minimal instructions to administer automatically or semi-automatically both assessment and treatment protocols, generate results, make interpretations, store data, and produce reports. The device or system may be loaded with standard protocols for English-speaking individuals, as well as dichotic speech material in any language.

A device, system and related methods to provide assessment and treatment of amblyaudia through standardized methods that do not require advanced training or a booth with loudspeakers for the operator to administer. The ARIA stimuli protocols for both assessment and treatment, encoded in or to be used by a software program or application, are transferred to a stand-alone set of specialized noise-cancelling headphones attached or connected to, wired or wirelessly, a software platform on an electronic computing device. or integrated with the headphones. The program administers assessment tests to individuals through the noise-cancelling earphones. The device enables someone with minimal instructions to administer automatically or semi-automatically both assessment and treatment protocols, generate results, make interpretations, store data, and produce reports. The device or system may be loaded with standard protocols for English-speaking individuals, as well as dichotic speech material in any language.

A method for identifying cough sounds in an audio recording of a subject including: operating at least one electronic processor to identify potential cough sounds in the audio recording; operating the at least one electronic processor to transform one or more of the potential cough sounds into corresponding one or more image representations; operating the at least one electronic processor to apply the one or more image representations to a representation pattern classifier trained to confirm that a potential cough sound is a cough sound or is not a cough sound; and operating the at least one electronic processor to flag one or more of the potential cough sounds as confirmed cough sounds based on an output of the representation pattern classifier.

A method for identifying cough sounds in an audio recording of a subject including: operating at least one electronic processor to identify potential cough sounds in the audio recording; operating the at least one electronic processor to transform one or more of the potential cough sounds into corresponding one or more image representations; operating the at least one electronic processor to apply the one or more image representations to a representation pattern classifier trained to confirm that a potential cough sound is a cough sound or is not a cough sound; and operating the at least one electronic processor to flag one or more of the potential cough sounds as confirmed cough sounds based on an output of the representation pattern classifier.

To enable accurately determining, based on a sound emitted by an inspection target, a classification of the sound. A control apparatus (1) according to an embodiment includes a classification information acquiring unit (13) that acquires classification information of a sound, a sound acquiring unit (11) that acquires a sound data including information of the sound, a storage unit (20) that stores definition data (25), an extraction unit (12) that extracts a plurality of features of the sound data, and a model construction unit (15) that constructs a learned model where machine learning, based on the plurality of features of the sound data and the classification information, on a correlation between the plurality of features and the classification of the sound is performed.

To enable accurately determining, based on a sound emitted by an inspection target, a classification of the sound. A control apparatus (1) according to an embodiment includes a classification information acquiring unit (13) that acquires classification information of a sound, a sound acquiring unit (11) that acquires a sound data including information of the sound, a storage unit (20) that stores definition data (25), an extraction unit (12) that extracts a plurality of features of the sound data, and a model construction unit (15) that constructs a learned model where machine learning, based on the plurality of features of the sound data and the classification information, on a correlation between the plurality of features and the classification of the sound is performed.

Disclosed herein are systems, devices, and methods for evaluating or analyzing complex audio signals using multi-dimensional statistical signatures and machine learning algorithms. One advantage of the present disclosure is the ability for remote evaluation of respiratory tract health using speech analysis. The need for remote collection capabilities that can sensitively and reliably characterize respiratory tract function is particularly pertinent in view of the recent Covid-19 pandemic, which may adversely affect the health of individuals who could already be experiencing health problems with respiratory tract function.

Disclosed herein are systems, devices, and methods for evaluating or analyzing complex audio signals using multi-dimensional statistical signatures and machine learning algorithms. One advantage of the present disclosure is the ability for remote evaluation of respiratory tract health using speech analysis. The need for remote collection capabilities that can sensitively and reliably characterize respiratory tract function is particularly pertinent in view of the recent Covid-19 pandemic, which may adversely affect the health of individuals who could already be experiencing health problems with respiratory tract function.

The present disclosure relates to a difficult airway evaluation method and device based on a machine learning voice technology. The method includes the following steps: acquiring voice data of a patient; carrying out feature extraction on the voice data, obtaining a pitch period of pronunciations, and acquiring a voiced sound feature and unvoiced sound features based on the pitch period of pronunciations; and constructing a difficult airway evaluation classifier based on the machine learning voice technology, analyzing the received voiced sound feature and unvoiced sound features by the trained difficult airway evaluation classifier, and carrying out scoring on the severity of a difficult airway to obtain an evaluation result of the difficult airway.

The present disclosure relates to a difficult airway evaluation method and device based on a machine learning voice technology. The method includes the following steps: acquiring voice data of a patient; carrying out feature extraction on the voice data, obtaining a pitch period of pronunciations, and acquiring a voiced sound feature and unvoiced sound features based on the pitch period of pronunciations; and constructing a difficult airway evaluation classifier based on the machine learning voice technology, analyzing the received voiced sound feature and unvoiced sound features by the trained difficult airway evaluation classifier, and carrying out scoring on the severity of a difficult airway to obtain an evaluation result of the difficult airway.