Abstract: The present disclosure relates to a negative ion generating device and an air purifier. The negative ion generating device includes a housing, a negative ion assembly, and a cleaning assembly. The negative ion assembly is arranged to the housing, and the negative ion assembly includes a conductive fiber brush. The cleaning assembly is arranged to the housing, the cleaning assembly includes a driving mechanism and a cleaning member, and the cleaning member can move under action of the power provided by the driving mechanism, and contact or separate from the conductive fiber brush during movement.

Abstract: The present disclosure provides a method and an apparatus for training a voice wake-up model, a method and an apparatus for voice wake-up, a device and a storage medium, which relates to the field of artificial intelligence and particularly to the field of deep learning and voice technology. A specific implementation lies in: acquiring voice recognition training data and voice wake-up training data that are created, and firstly performing training on a base model according to the voice recognition training data to obtain a model parameter of the base model when a model loss function converges; then updating, based on a model configuration instruction, a configuration parameter of a decoding module in the base model to obtain a first model; and finally performing training on the first model according to the voice wake-up training data to obtain a trained voice wake-up model when the model loss function converges.

Abstract: Disclosed are an unmanned airborne ground penetrating radar system and an inspection method for a dam hidden danger detection, including an unmanned aerial vehicle (UAV) system; the UAV system includes an unmanned aerial vehicle, a sensor platform, a radar platform, a forward-looking laser rangefinder and a ground penetrating radar; the sensor platform is installed on the UAV, and the forward-looking laser rangefinder is installed on the sensor platform, and the radar platform is installed on the UAV at one side of the sensor platform; moreover, the ground penetrating radar is installed on the radar platform, and a variable polarization ground penetrating radar antenna array is arranged in the ground penetrating radar; the variable polarization ground penetrating radar antenna array includes a substrate, and a plurality of groups of orthogonal dual-polarization Vivaldi antenna transmitting subarrays and receiving subarrays are mounted on the substrate.

Abstract: The present disclosure provides a method and apparatus of synthesizing a speech, a method and apparatus of training a speech synthesis model, an electronic device, and a storage medium. The method of synthesizing a speech includes acquiring a style information of a speech to be synthesized, a tone information of the speech to be synthesized, and a content information of a text to be processed; generating an acoustic feature information of the text to be processed, by using a pre-trained speech synthesis model, based on the style information, the tone information, and the content information of the text to be processed; and synthesizing the speech for the text to be processed, based on the acoustic feature information of the text to be processed.

Abstract: Provided are compositions comprising recombinant DNA polymerases that include amino acid substitutions, insertions, deletions, and/or exogenous features that confer modified properties upon the polymerase for enhanced single molecule sequencing. Such properties can include enhanced metal ion coordination, reduced exonuclease activity, reduced reaction rates at one or more steps of the polymerase kinetic cycle, decreased branching fraction, altered cofactor selectivity, increased yield, increased thermostability, increased accuracy, increased speed, increased readlength, and the like. Also provided are nucleic acids which encode the polymerases with the aforementioned phenotypes, as well as methods of using such polymerases to make a DNA or to sequence a DNA template.

Abstract: A method and an apparatus for recognizing a voice are provided. The method may include: inputting a target voice into a pre-trained voice recognition model to obtain an initial text output by at least one recognition network in the voice recognition model, the recognition network including a plurality of preset types of processing layers, and at least one type of processing layer of the recognition network being obtained by training based on a voice sample in a preset direction interval; and determining a voice recognition result of the target voice, based on the initial text.

Abstract: An audio recognizing method, including: performing acoustic feature prediction on the audio to be recognized to obtain first audio prediction result and an acoustic feature reference quantity for predicting an audio recognition result; obtaining second audio prediction result based on the acoustic feature reference quantity; and determining the audio recognition result of the audio to be recognized based on the first audio prediction result and the second audio prediction result, the audio recognition result including unvoiced sound or voiced sound. When determining that the audio is unvoiced sound or voiced sound, the first audio prediction result obtained by performing acoustic feature prediction on the audio to be recognized is used, and the second audio prediction result is obtained in combination with other acoustic feature reference quantities, thereby making the determination result of unvoiced sound or voiced sound of the audio more accurate, to improve the audio quality in speech processing.

Abstract: Provided are an audio signal processing method, a training method, an apparatus and a storage medium, relating to the field of data processing, in particular to, the field of voice. The audio signal processing method includes: eliminating at least part of a linear echo signal from a mixed voice signal, to obtain an intermediate processing signal, where the mixed voice signal is obtained by mixing a target voice signal with an echo signal, and the echo signal is generated in an environment where the target voice signal is located and includes the linear echo signal and a nonlinear echo signal; and removing the nonlinear echo signal and a residual part of the linear echo signal from the intermediate processing signal, by using a target full convolution neural network model, to obtain an approximate target voice signal, the target full convolution neural network model including at least two convolution layers.

Abstract: A method of training a speech synthesis method, a method of synthesizing a speech, a device and a storage medium are provided, which relate to a field of artificial intelligence technology, in particular to a field of speech synthesis technology. The specific implementation scheme includes: processing training data by using the speech synthesis model, so as to determine a content encoding sequence, a style encoding sequence, a timbre encoding vector, a noise environment vector and a target Mel spectrum sequence corresponding to the training data; determine a total loss value according to the content encoding sequence, the style encoding sequence, the timbre encoding vector, the noise environment vector and the target Mel spectrum sequence; and adjusting a parameter of the speech synthesis model according to the total loss value.

Abstract: A technical solution for compressing a neural network model which relates to the field of artificial intelligence technologies, such as deep learning technologies, cloud service technologies, is disclosed. The method for compressing a neural network model includes: acquiring a to-be-compressed neural network model; determining a first bit width, a second bit width and a target thinning rate corresponding to the to-be-compressed neural network model; obtaining a target value according to the first bit width, the second bit width and the target thinning rate; and compressing the to-be-compressed neural network model using the target value, the first bit width and the second bit width to obtain a compression result of the to-be-compressed neural network model.

Abstract: A task processing method, an electronic device and a storage medium, which relate to the field of artificial intelligence, such as intelligent voices, artificial intelligence chips, or the like, are disclosed. The method may include: for to-be-executed tasks, in at least one round of processing, performing the following operations: in response to determining that one or more high-priority tasks exist in the to-be-executed tasks, calling the one or more high-priority tasks to process audio data cached in a memory; and after execution of the one or more high-priority tasks is completed, and in response to determining that one or more low-priority task exist in the to-be-executed tasks, calling the one or more low-priority tasks to process the audio data.

Abstract: A voice generating method and apparatus, an electronic device and a storage medium. The specific implementation solution includes: acquiring a text to be processed, and determining an associated text of the text to be processed; acquiring an associated prosodic feature of the associated text; determining an associated text feature of the associated text based on the text to be processed; determining a spectrum feature to be processed of the text to be processed based on the associated prosodic feature and the associated text feature; and generating a target voice corresponding to the text to be processed based on the spectrum feature to be processed.

Abstract: Compounds of Formulae I? and I are described, which are useful as stimulators of sGC, particularly NO-independent, heme-dependent stimulators. These compounds are also useful for treating, preventing or managing various disorders that are herein disclosed.

Abstract: The disclosure provides a speech recognition method, a device and a computer-readable storage medium. The method includes obtaining a first voice signal collected from a first microphone in a microphone array and a second voice signal collected from a second microphone in the microphone array, the microphone array including at least two microphones, such as two, three or six microphones. The method further includes extracting enhanced features associated with the first voice signal and the second voice signal through a neural network, and obtaining a speech recognition result based on the enhanced features extracted.

Abstract: The present disclosure discloses a control method and a control apparatus for speech interaction. The detailed implementation solution of the control method for the speech interaction includes: collecting an audio signal; detecting a wake-up word in the audio signal to obtain a wake-up word result; and playing a prompt tone and/or executing a speech instruction in the audio signal based on the wake-up word result.

Abstract: The present disclosure provides speech recognition and codec methods and apparatuses, an electronic device and a storage medium, and relates to the field of artificial intelligence such as intelligent speech, deep learning and natural language processing. The speech recognition method may include: acquiring an audio feature of to-be-recognized speech; encoding the audio feature to obtain an encoding feature; truncating the encoding feature to obtain continuous N feature fragments, N being a positive integer greater than one; and acquiring, for any one of the feature segments, corresponding historical feature abstraction information, encoding the feature segment in combination with the historical feature abstraction information, and decoding an encoding result to obtain a recognition result corresponding to the feature segment, wherein the historical feature abstraction information is information obtained by feature abstraction of recognized historical feature fragments.

Abstract: A method of processing audio data, an electronic device, and a storage medium, which relates to a field of artificial intelligence, in particular to a field of speech processing technology. The method includes: processing spectral data of the audio data to obtain a first feature information; obtaining a fundamental frequency indication information according to the first feature information, wherein the fundamental frequency indication information indicates valid audio data of the first feature information and invalid audio data of the first feature information; obtaining a fundamental frequency information and a spectral energy information according to the first feature information and the fundamental frequency indication information; and obtaining a harmonic structure information of the audio data according to the fundamental frequency information and the spectral energy information.

Abstract: The present disclosure provides a method for a human-computer interaction, an apparatus for a human-computer interaction, a device, and a storage medium, and the present disclosure relates to the field of artificial intelligence, such as deep learning and voice. A specific implementation includes: acquiring a voice command; performing voice recognition on the voice command to determine a corresponding voice text; sending, in response to satisfying a preset information sending condition, the voice text to a cloud; receiving a resource for the voice command returned from the cloud; and responding to the voice command based on the resource.

Abstract: The present disclosure discloses a speech processing method and apparatus, a device and a computer storage medium, and relates to speech and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: acquiring a vocoder feature obtained for text; correcting a value of an unvoiced and voiced (UV) feature in the vocoder feature according to an energy feature and/or a speech spectrum feature in the vocoder feature; and providing the corrected vocoder feature for a vocoder, so as to obtain synthesized speech.

Abstract: The present disclosure discloses a speech synthesis method and apparatus, a device and a computer storage medium, and relates to speech and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: acquiring to-be-synthesized text; acquiring a prosody feature extracted from the text; inputting the text and the prosody feature into a speech synthesis model to obtain a vocoder feature; and inputting the vocoder feature into a vocoder to obtain synthesized speech.