A speech feature extraction apparatus 100 includes a voice activity detection unit 103 that drops non-voice frames from frames corresponding to an input speech utterance, and calculates a posterior of being voiced for each frame, a voice activity detection process unit 106 calculates a function value as weights in pooling frames to produce an utterance-level feature, from a given a voice activity detection posterior, and an utterance-level feature extraction unit 112 that extracts an utterance-level feature, from the frame on a basis of multiple frame-level features, using the function values.

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.

According to a first aspect of the present disclosure, a method for facilitating the detection of one or more time series patterns is conceived, comprising building one or more artificial neural networks, wherein, for at least one time series pattern to be detected, a specific one of said artificial neural networks is built. According to a second aspect of the present disclosure, a corresponding computer program is provided. According to a third aspect of the present disclosure, a non-transitory computer-readable medium is provided that comprises a computer program of the kind set forth. According to a fourth aspect of the present disclosure, a corresponding system for facilitating the detection of one or more time series patterns is provided.

An electronic device for improving a quality of an audio includes: a microphone configured to obtain an audio input including a voice; at least one memory; and at least one processor. The at least one processor is configured to execute one or more instructions stored in the memory to: obtain a first voice fingerprint corresponding to the obtained audio input; obtain a second voice fingerprint corresponding to the voice; estimate, based on the first voice fingerprint and the second voice fingerprint, noise caused by an acoustic environment of the obtained audio input; and remove the estimated noise from the obtained audio input.

An electronic device for improving a quality of an audio includes: a microphone configured to obtain an audio input including a voice; at least one memory; and at least one processor. The at least one processor is configured to execute one or more instructions stored in the memory to: obtain a first voice fingerprint corresponding to the obtained audio input; obtain a second voice fingerprint corresponding to the voice; estimate, based on the first voice fingerprint and the second voice fingerprint, noise caused by an acoustic environment of the obtained audio input; and remove the estimated noise from the obtained audio input.

An audio transmitter processor for generating an error protected frame using encoded audio data of an audio frame, the encoded audio data for the audio frame having a first amount of information units and a second amount of information units, has: a frame builder for building a codeword frame having a codeword raster, wherein the frame builder is configured to determine a border between a first amount of information units and a second amount of information units so that a starting information unit of the second amount of information units coincides with a codeword border; and an error protection coder to obtain a plurality of processed codewords representing the error protected frame.

A punctuation mark delete model learning device is a device that generates, through machine learning, a punctuation mark delete model, and comprises a first learning data generation unit that generates first learning data consisting of a pair of an input sentence including a punctuation mark, a preceding sentence that is a sentence with the punctuation mark assigned at an end of the sentence, and a subsequent sentence following the punctuation mark, and a label indicating whether or not the assignment of the punctuation mark is correct, on the basis of a first text corpus consisting of text obtained by speech recognition processing, and a model learning unit that updates parameters of the punctuation mark delete model on the basis of an error between a probability obtained by inputting the input sentences of the first learning data to the punctuation mark delete model and the label.

A punctuation mark delete model learning device is a device that generates, through machine learning, a punctuation mark delete model, and comprises a first learning data generation unit that generates first learning data consisting of a pair of an input sentence including a punctuation mark, a preceding sentence that is a sentence with the punctuation mark assigned at an end of the sentence, and a subsequent sentence following the punctuation mark, and a label indicating whether or not the assignment of the punctuation mark is correct, on the basis of a first text corpus consisting of text obtained by speech recognition processing, and a model learning unit that updates parameters of the punctuation mark delete model on the basis of an error between a probability obtained by inputting the input sentences of the first learning data to the punctuation mark delete model and the label.

Two-stage speech/music classification device and method classify an input sound signal and select a core encoder for encoding the sound signal. A first stage classifies the input sound signal into one of a number of final classes. A second stage extracts high-level features of the input sound signal and selects the core encoder for encoding the input sound signal in response to the extracted high-level features and the final class selected in the first stage.