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 and an apparatus for processing a temporal envelope of an audio signal, and an encoder are disclosed. When multiple temporal envelopes are solved, continuity of signal energy can be well maintained, and in addition, complexity of calculating a temporal envelope is reduced. The method includes: obtaining a high-band signal of the current frame audio signal according to the received current frame audio signal; dividing the high-band signal of the current frame signal into M subframes according to a predetermined temporal envelope quantity M, where M is an integer that is greater than or equal to 2; calculating a temporal envelope of each of the subframes; performing windowing on the first subframe of the M subframes and the last subframe of the M subframes by using an asymmetric window function; and performing windowing on a subframe except the first subframe and the last subframe of the M subframes.

A method and an apparatus for processing a temporal envelope of an audio signal, and an encoder are disclosed. When multiple temporal envelopes are solved, continuity of signal energy can be well maintained, and in addition, complexity of calculating a temporal envelope is reduced. The method includes: obtaining a high-band signal of the current frame audio signal according to the received current frame audio signal; dividing the high-band signal of the current frame signal into M subframes according to a predetermined temporal envelope quantity M, where M is an integer that is greater than or equal to 2; calculating a temporal envelope of each of the subframes; performing windowing on the first subframe of the M subframes and the last subframe of the M subframes by using an asymmetric window function; and performing windowing on a subframe except the first subframe and the last subframe of the M subframes.

According to an embodiment, a pattern recognition device is configured to divide an input signal into a plurality of elements, convert the divided elements into feature vectors having the same dimensionality to generate a set of feature vectors, and evaluate the set of feature vectors using a recognition dictionary including models corresponding to respective classes, to output a recognition result representing a class or a set of classes to which the input signal belongs. The models each include sub-models each corresponding to one of possible division patterns in which a signal to be classified into a class corresponding to the model can be divided into a plurality of elements. A label expressing a model including a sub-model conforming to the set of feature vectors, or a set of labels expressing a set of models including sub-models conforming to the set of feature vectors is output as the recognition result.

According to an embodiment, a pattern recognition device is configured to divide an input signal into a plurality of elements, convert the divided elements into feature vectors having the same dimensionality to generate a set of feature vectors, and evaluate the set of feature vectors using a recognition dictionary including models corresponding to respective classes, to output a recognition result representing a class or a set of classes to which the input signal belongs. The models each include sub-models each corresponding to one of possible division patterns in which a signal to be classified into a class corresponding to the model can be divided into a plurality of elements. A label expressing a model including a sub-model conforming to the set of feature vectors, or a set of labels expressing a set of models including sub-models conforming to the set of feature vectors is output as the recognition result.

It discloses a serial FFT-based low-power MFCC speech feature extraction circuit, and belongs to the technical field of calculation, reckoning or counting. The circuit is oriented toward the field of intelligence, and is adapted to a hardware circuit design by optimizing an MFCC algorithm, and a serial FFT algorithm and an approximation operation on a multiplication are fully used, thereby greatly reducing a circuit area and power. The entire circuit includes a preprocessing module, a framing and windowing module, an FFT module, a Mel filtration module, and a logarithm and DCT module. The improved FFT algorithm uses a serial pipeline manner to process data, and a time of an audio frame is effectively utilized, thereby reducing a storage area and operation frequency of the circuit under the condition of meeting an output requirement.

It discloses a serial FFT-based low-power MFCC speech feature extraction circuit, and belongs to the technical field of calculation, reckoning or counting. The circuit is oriented toward the field of intelligence, and is adapted to a hardware circuit design by optimizing an MFCC algorithm, and a serial FFT algorithm and an approximation operation on a multiplication are fully used, thereby greatly reducing a circuit area and power. The entire circuit includes a preprocessing module, a framing and windowing module, an FFT module, a Mel filtration module, and a logarithm and DCT module. The improved FFT algorithm uses a serial pipeline manner to process data, and a time of an audio frame is effectively utilized, thereby reducing a storage area and operation frequency of the circuit under the condition of meeting an output requirement.

There are provided methods and apparatus for performing modified cosine transformation (MDCT) with an analysis/synthesis windowing function, using an analysis windowing function having a meandering portion which passes a linear function in correspondence of at least four points.

There are provided methods and apparatus for performing modified cosine transformation (MDCT) with an analysis/synthesis windowing function, using an analysis windowing function having a meandering portion which passes a linear function in correspondence of at least four points.