A method for automatic for sound recognition, comprising a) raw spectrogram generation from a sound signal spectrum; b) wide-band spectrum determination; c) wide-band continuous spectrum determination; d) tonal and time-transient spectrum determination; wide-band continuous spectrogram and tonal and time-transient spectrogram determination; and) spectrogram image generation.

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.

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.

Non-voice data is embedded in a voice bit stream that includes frames of voice bits by selecting a frame of voice bits to carry the non-voice data, placing non-voice identifier bits in a first portion of the voice bits in the selected frame, and placing the non-voice data in a second portion of the voice bits in the selected frame. The non-voice identifier bits are employed to reduce a perceived effect of the non-voice data on audible speech produced from the voice bit stream.

Non-voice data is embedded in a voice bit stream that includes frames of voice bits by selecting a frame of voice bits to carry the non-voice data, placing non-voice identifier bits in a first portion of the voice bits in the selected frame, and placing the non-voice data in a second portion of the voice bits in the selected frame. The non-voice identifier bits are employed to reduce a perceived effect of the non-voice data on audible speech produced from the voice bit stream.

Provided is a system which allows a learner who is a non-native speaker of a given language to intuitively improve pronunciation of the language. A pronunciation conversion apparatus includes a conversion section which converts a first feature value corresponding to a first speech signal obtained when a first speaker who speaks a given language as his/her native language speaks another language such that the first feature value approaches a second feature value corresponding to a second speech signal obtained when a second speaker who speaks the other language as his/her native language speaks the other language, each of the first feature value and the second feature value is a feature value capable of representing a difference in pronunciation, and a speech signal obtained from the first feature value after the conversion is presented to the first speaker.