Abstract: A full-electric drive cementing control system is disclosed.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for training and implementing pipeline error detection models to facilitate automated detection of data quality (DQ) issues within recurring data pipelines. For example, systems described herein involve training a pipeline error detection model by first constructing a plurality of DQ constraints for a recurring data pipeline based on ranges of values observed over a history of pipeline executions. The systems may further train the model to predict DQ issues by synthetically applying data variants to historical executions of the recurring data pipeline or to data pipelines having similar characteristics thereto. Once trained, the pipeline error detection model(s) can be applied to new executions of the data pipeline as they become available to quickly and efficiently predict whether a given execution includes a predicted DQ issue therein.

Abstract: In accordance with the implementations of the subject matter described herein, there is provided a solution for reusing infographics. In this solution, a group of visual element units is determined from a visual element set comprised in the original infographic. A visual element unit in the group represents an information item in the original infographic. A first correlation between a visual element contained in the visual element unit and the information item and a second correlation between the visual element unit and a further visual element unit in the group are determined. A description for the original infographic is generated based on the first and second correlations. A target infographic is generated by updating the group of visual element units at least based on the description and the target information. In this way, infographics can be converted into reusable templates, thereby simplifying reuse of such infographics and improving user experience.

Abstract: A vaneless contra-rotating compressor with multiple contra-rotating interfaces and application of the vaneless contra-rotating compressor is provided, which includes at least two vaneless contra-rotating interfaces. For each of the at least two contra-rotating interfaces, the contra-rotating interface corresponds to two of vaneless contra-rotating rotors. A rotating direction of an upstream rotor of the two vaneless contra-rotating rotors and a rotating direction of a downstream rotor of the two vaneless contra-rotating rotors are opposite. Rectifier stator vanes are not provided among all the rotors to supply sufficient inlet negative pre-swirl for the downstream rotors through the upstream rotors. Only the outlet guide vanes of the last stage, of the vaneless contra-rotating rotors are provided. The number of stages and the number of vaneless contra-rotating interfaces may be set flexibly according to the actual pressurization requirements.

Abstract: Various implementations of the present disclosure relate to text to visualization In a method, information items are extracted from a natural language sentence. Visual elements associated with the information items are determined. A visual representation of the natural language sentence based on the visual elements is determined, the visual representation indicating information expressed by the natural language sentence.

Abstract: A speech enhancement method is provided. The speech enhancement method includes: estimating a direction of a speaker by using an input signal, generating direction information indicating the estimated direction, detecting speech of a speaker based on a result of the estimating the direction, and enhancing the speech of the speaker by using the direction information based on a result of the detecting the speech.

Abstract: Embodiments of the present disclosure provide a speech noise reduction method and a speech noise reduction device based on artificial intelligence and a computer device. The method includes the followings. A first noisy speech to be processed is received. The first noisy speech to be processed is pre-processed, to obtain the first noisy speech in a preset format. The first noisy speech in the preset format is sampled according to a sampling rate indicated by the preset format, to obtain first sampling point information of the first noisy speech. A noise reduction is performed on the first sampling point information through a deep-learning noise reduction model, to generate noise-reduced first sampling point information. A first clean speech is generated according to the noise-reduced first sampling point information.

Abstract: A speech enhancement method is provided. The speech enhancement method includes: estimating a direction of a speaker by using an input signal, generating direction information indicating the estimated direction, detecting speech of a speaker based on a result of the estimating the direction, and enhancing the speech of the speaker by using the direction information based on a result of the detecting the speech.

Abstract: A speech enhancement method is provided. The speech enhancement method includes: estimating a direction of a speaker by using an input signal, generating direction information indicating the estimated direction, detecting speech of a speaker based on a result of the estimating the direction, and enhancing the speech of the speaker by using the direction information based on a result of the detecting the speech.

Abstract: A speech enhancement method is provided. The speech enhancement method includes: estimating a direction of a speaker by using an input signal, generating direction information indicating the estimated direction, detecting speech of a speaker based on a result of the estimating the direction, and enhancing the speech of the speaker by using the direction information based on a result of the detecting the speech.

Abstract: The present application discloses a voice recognition method and apparatus. A specific implementation of the method includes: in response to detecting a microphone receiving voice signal containing interfering sound signal, performing high-pass filtering on the voice signal; cancelling the interfering sound signal in the voice signal subjected to high-pass filtering; performing automatic gain control on the voice signal subjected to cancelling the interfering sound signal, to obtain target voice signal; and extracting a feature vector from the target voice signal and inputting the feature vector into a pre-trained acoustic model, to obtain a voice recognition result matching the target voice signal, the acoustic model being used for representing a corresponding relationship between the feature vector and the voice recognition result. This implementation improves the success rate of voice recognition.

Abstract: The present disclosure provides a method and a device for processing a speech based on artificial intelligence. The method includes: receiving a speech processing request, in which the speech processing request includes a first digital speech signal and a first sample frequency corresponding to the first digital speech signal; selecting a target speech processing model from a pre-trained speech processing model base according to the first sample frequency; performing up-sampling processing on the first digital speech signal using the target speech processing model to generate a second digital speech signal having a second sample frequency, in which the second sample frequency is larger than the first sample frequency.

Abstract: A speech enhancement method is provided. The speech enhancement method includes: estimating a direction of a speaker by using an input signal, generating direction information indicating the estimated direction, detecting speech of a speaker based on a result of the estimating the direction, and enhancing the speech of the speaker by using the direction information based on a result of the detecting the speech.

Abstract: Embodiments of the present disclosure provide a speech noise reduction method and a speech noise reduction device based on artificial intelligence and a computer device. The method includes the followings. A first noisy speech to be processed is received. The first noisy speech to be processed is pre-processed, to obtain the first noisy speech in a preset format. The first noisy speech in the preset format is sampled according to a sampling rate indicated by the preset format, to obtain first sampling point information of the first noisy speech. A noise reduction is performed on the first sampling point information through a deep-learning noise reduction model, to generate noise-reduced first sampling point information. A first clean speech is generated according to the noise-reduced first sampling point information.

Abstract: The present disclosure provides a method and a device for processing a speech based on artificial intelligence. The method includes: receiving a speech processing request, in which the speech processing request includes a first digital speech signal and a first sample frequency corresponding to the first digital speech signal; selecting a target speech processing* model from a pre-trained speech processing model base according to the first sample frequency; performing up-sampling processing on the first digital speech signal using the target speech processing model to generate a second digital speech signal having a second sample frequency, in which the second sample frequency is larger than the first sample frequency.

Abstract: The present application discloses a voice recognition method and apparatus. A specific implementation of the method includes: in response to detecting a microphone receiving voice signal containing interfering sound signal, performing high-pass filtering on the voice signal; cancelling the interfering sound signal in the voice signal subjected to high-pass filtering; performing automatic gain control on the voice signal subjected to cancelling the interfering sound signal, to obtain target voice signal; and extracting a feature vector from the target voice signal and inputting the feature vector into a pre-trained acoustic model, to obtain a voice recognition result matching the target voice signal, the acoustic model being used for representing a corresponding relationship between the feature vector and the voice recognition result. This implementation improves the success rate of voice recognition.

Abstract: A speech enhancement method is provided. The speech enhancement method includes: estimating a direction of a speaker by using an input signal, generating direction information indicating the estimated direction, detecting speech of a speaker based on a result of the estimating the direction, and enhancing the speech of the speaker by using the direction information based on a result of the detecting the speech.

Abstract: An apparatus for estimating a Direction of Arrival (DOA) of a wideband includes a first signal receiving unit and a second signal receiving unit to receive a wideband signal while satisfying d?Mc/2fs, wherein ‘d’ denotes a distance the first signal receiving unit and the second signal receiving unit are spaced apart from each other, ‘c’ denotes the speed of sound, ‘M’ denotes a number of wideband frequencies being a number of fast Fourier transformation (FFT) points of a wideband signal, and ‘fs’ denotes a sampling frequency, and a DOA calculating unit to calculate a DOA (?) using a normalized frequency ( f) which is obtained by performing an FFT on the respective wideband signals transmitted from the first signal receiving unit and the second signal receiving unit, and using the distance d.

Abstract: A microphone signal compensation apparatus includes a plurality of audio input units to respectively receive a target signal, each audio input unit of the plurality of audio input units including a microphone; a constant filter unit to selectively apply a constant filtering calibration scheme to signals output by the plurality of audio input units to compensate for a difference in at least one characteristic among the audio input units, the constant filtering calibration scheme being estimated from an average value of a ratio of a desired signal to a reference signal among the signals output by the plurality of audio input units; and a noise remover to remove noise from the signals processed by the constant filter unit, and to separate the target signal from the signals from which the noise has been removed.

Abstract: A method and apparatus for removing signal noise using multiple bands are provided. The noise removal apparatus may divide the entire frequency band into a plurality of sub-bands using a multiband filter that has characteristics similar to an auditory system of a human being and may effectively remove noise in each of the sub-bands according to a frequency subtraction scheme.