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.

A method of modelling speech of a user of a headset comprising a microphone, the method comprising: receiving a first sample, from a bone-conduction sensor, representing bone-conducted speech of the user; obtaining a measure of fundamental frequency of the bone-conducted speech in each of a plurality of speech frames of the first sample; obtaining a first distribution of the fundamental frequencies of the bone-conducted speech over the plurality of speech frames; receiving, from the microphone, a second sample; determining a first acoustic condition at the headset based on the second signal; performing a biometric process based on the first distribution of fundamental frequencies and the first acoustic condition.

A method of modelling speech of a user of a headset comprising a microphone, the method comprising: receiving a first sample, from a bone-conduction sensor, representing bone-conducted speech of the user; obtaining a measure of fundamental frequency of the bone-conducted speech in each of a plurality of speech frames of the first sample; obtaining a first distribution of the fundamental frequencies of the bone-conducted speech over the plurality of speech frames; receiving, from the microphone, a second sample; determining a first acoustic condition at the headset based on the second signal; performing a biometric process based on the first distribution of fundamental frequencies and the first acoustic condition.

An audio signal processing apparatus is configured to: transform a first audio signal includes n channels to generate a first audio data in a frequency domain, generate a frequency feature signal for each channel from the first audio data in the frequency domain, based on a first deep neural network (DNN), generate a second audio signal includes m channels from the first audio signal, based on a second DNN, and generate an output audio signal by encoding the second audio signal and the frequency feature signal. The first audio signal is a high order ambisonic signal includes a zero.sup.th order signal and a plurality of first order signals. The second audio signal includes a mono signal or a stereo signal. m is smaller than n.

Provided is a device performing machine learning by extracting an acoustic feature value from conversational voice data and predicting a disease level of a subject on the basis of a disease prediction model to be generated by the machine learning, the device including: a matrix calculation unit 23 calculating a spatial delay matrix using a relation value of a plurality of types of acoustic feature values; and a matrix decomposition unit 24 calculating a matrix decomposition value from the spatial delay matrix, in which a relation value reflecting a non-linear and non-stationary relationship of the feature values can be obtained by calculating at least one of a DCCA coefficient and a mutual information amount as the relation value of the plurality of types of acoustic feature values, and the disease level of the subject can be predicted on the basis of the relation value.

A method and an electronic device for detecting crosstalk data are provided. The method for detecting crosstalk data can detect whether an audio data stream includes crosstalk data. The method includes: receiving a first audio data block, a second audio data block, and a reference time difference, wherein the first audio data block and the second audio data block separately include a plurality of audio data segments; using a time difference between an acquisition time of an audio data segment in the first audio data block and a corresponding audio data segment in the second audio data block as an audio segment time difference; and determining that the audio data segment of the first audio data block includes crosstalk data when the audio segment time difference does not match the reference time difference.

A method and an electronic device for detecting crosstalk data are provided. The method for detecting crosstalk data can detect whether an audio data stream includes crosstalk data. The method includes: receiving a first audio data block, a second audio data block, and a reference time difference, wherein the first audio data block and the second audio data block separately include a plurality of audio data segments; using a time difference between an acquisition time of an audio data segment in the first audio data block and a corresponding audio data segment in the second audio data block as an audio segment time difference; and determining that the audio data segment of the first audio data block includes crosstalk data when the audio segment time difference does not match the reference time difference.

A method comprising determining feature values of an input audio window and determining a formant attenuation/amplification coefficient for the input audio window based on the processing of the feature values by a neural network.

A method comprising determining feature values of an input audio window and determining a formant attenuation/amplification coefficient for the input audio window based on the processing of the feature values by a neural network.