Abstract: A method, apparatus, electronic device, and storage medium for character recognition are provided. The method may perform image processing on an acquired original image to obtain a region to be recognized. The region may include a character. The method may determine an area ratio of the region to be recognized on the original image. The method may determine an angle between the region to be recognized and a preset direction. The method may determine a character density of the region to be recognized. The method may perform character recognition on the character in the region to be recognized in response to determining that the area ratio is greater than a ratio threshold, the angle is less than an angle threshold, and the character density is less than a density threshold.

Abstract: A whistling sound suppression method includes: obtaining an ambient audio signal, the ambient audio signal being a sound signal in a surrounding environment of an earphone; filtering the ambient audio signal according to a preset first filter group to obtain a first audio signal; obtaining an ear canal audio signal, the ear canal audio signal being a sound signal when the first audio signal propagates in an ear canal; and filtering a subsequently obtained ambient audio signal according to a preset second filter group to obtain a second audio signal in response to the ear canal audio signal meets a whistling condition.

Abstract: A method for audio and video synchronization, includes: receiving audio data sent by the video player, wherein the audio data includes more than one audio sampling points; obtaining a network delay change between the audio player and the video player, wherein the network delay change refers to a time difference between the network delay monitored for the first time and the network delay monitored for the last time; determining a total amount of audio data to be adjusted according to the network delay change; and adding audio sampling points to the received audio data or deleting audio sampling points from the received audio data according to the total amount of audio data to be adjusted and a preset adjustment strategy, so that the audio player is synchronized with the video player, and playing the added or deleted audio data.

Abstract: The present disclosure relates to a device wakeup method and apparatus, an electronic device, and a storage medium. The wakeup method is applied to a first electronic device and includes: a wakeup message from a second electronic device is received, and when it is determined that a present state is an unawakened state, locally collected voice data is acquired; MFCC extraction is performed on the voice data to acquire a first MFCC of the voice data; the wakeup message is parsed to obtain a second MFCC included in the wakeup message; the first MFCC and the second MFCC are matched, and when it is determined that a difference between the first MFCC and the second MFCC is less than or equal to a set threshold value, a wakeup instruction is generated; and responsive to the wakeup instruction, the first electronic device is woken up.

Abstract: A method includes obtaining a query content. The query content includes segment information representing a to-be-recognized audio. The method further includes selecting the preset quantity of candidate audios corresponding to the query content from a preset library. Each candidate audio includes a candidate audio segment matched with the segment information. The method further includes inputting the candidate audio segment into a trained detection model so as to obtain target segment information including the segment information and a target audio where the target segment information is located.

Abstract: A training method for an image denoising model that can include collecting multiple sample image groups through a shooting device, each sample image group including multiple frames of sample images with a same photographic sensitivity and sample images in different sample image groups having different photographic sensitivities.

Abstract: Aspects of the disclosure can provide method for identifying a face where multiple images to be identified are received. Each of the multiple images includes a face image part. Each face image of face images in the multiple images to be identified is extracted. An initial figure identification result of identifying a figure in the each face image is determined by matching a face in the each face image respectively to a face in a target image in an image identification library. The face images are grouped. A target figure identification result for each face image in each group is determined according to the initial figure identification result for the each face image in the each group.

Abstract: A man-machine dialogue method, includes: for each round of a plurality of rounds of dialogue wherein each round includes dialogue information input by a user, determining semantic information corresponding to the dialogue information; determining a target slot position corresponding to an item indicated by the semantic information, establishing a new pre-order data structure including the target slot position when there is no established pre-order data structure including the target slot position; outputting reply information responsive to the dialogue information, wherein the reply information is configured to guide the user to input new dialogue information in a subsequent round of dialogue; and in a case that the dialogue information input by the user in the subsequent round includes a keyword for indicating ordering, performing an ordering operation according to a finally-established pre-order data structure.

Abstract: A voice control method can be applied to a first terminal, and include: receiving a user's voice operation instruction after the first terminal is activated, the voice operation instruction being used for controlling the first terminal to perform a target operation; sending an instruction execution request to a server after the voice operation instruction is received, the instruction execution request being used for requesting the server to determine whether the first terminal is to respond to the voice operation instruction according to device information of the terminal in a device network, wherein the first terminal is located in the device network; and performing the target operation in a case where a response message is received from the server, the response message indicating that the first terminal is to respond to the voice operation instruction.

Abstract: A method for playing audio data, includes: detecting a number and position relationships of associated audio playback devices; performing audio signal processing on audio data to be played based on the number and the position relationships to obtain an audio signal matching the number of the audio playback devices; and playing the audio signal through the audio playback devices.

Abstract: A method for detecting a finger occlusion image includes: N first original occlusion images and M first non-occlusion images are acquired, and first training data set is generated based on first original occlusion images and first non-occlusion images; first training is performed, based on first training data set, on neural network model for detection of finger occlusion image; L second original occlusion images and K second non-occlusion images are acquired, and second training data set is generated based on second original occlusion images and second non-occlusion images; linear classifier in neural network model having completed first training is replaced with iterative training module to form finger occlusion image detection model; second training is performed on finger occlusion image detection model based on second training data set; image to be detected is input into trained finger occlusion image detection model, to determine whether image to be detected is finger occlusion image.

Abstract: At least two images are acquired. At least two crop images are acquired by cropping the at least two images for face-containing images. Triangular patch deformation is performed on two neighbour images, generating a first triangular patch deformation frame image sequence and a second triangular patch deformation frame image sequence. Similarity transformation is performed on each image sequence of the first triangular patch deformation frame image sequence, acquiring a first transform frame image sequence. Similarity transformation is performed on each image sequence of the second triangular patch deformation frame image sequence, acquiring a second transform frame image sequence. The first and the second transform frame image sequences are fused, acquiring a video frame sequence corresponding to the two neighbour images. Of the at least two images, a video frame sequence generated by all neighbour images is coded, acquiring a destined video.

Abstract: A method and apparatus for scheduling an item, and a computer-readable storage medium are provided. The method can include acquiring a first time sequence corresponding to a target item in a target warehouse, the first time sequence including shipment volume information of the target item corresponding to each unit time within a first historical period. The method can further include determining, according to the first time sequence and a target period to be predicted, total shipment volume information of the target item within a target period through a shipment prediction model, the shipment prediction model including a plurality of parallel first time sequence sub-models and a weighted sub-model which is connected to an output of each of the first time sequence sub-models, and scheduling the target item in the target warehouse according to the total shipment volume information and current inventory information of the target item in the target warehouse.

Abstract: Methods, electronic device, and non-transitory computer-readable storage mediums are provided for semantic parsing. The equipment may obtain a first recognition result of a target statement. The first recognition result may include a first intention recognition result and a first entity recognition result. The first entity recognition result may correspond to a plurality of vertical domains. The equipment may also determine one of the plurality of vertical domains corresponding to the first entity recognition result as a target vertical domain corresponding to the target statement according to the first intention recognition result. The equipment may further convert the first entity recognition result into a second entity recognition result in the target vertical domain. The equipment may also parse an intention of the target statement according to the second entity recognition result.

Abstract: An audio signal processing method includes: acquiring audio signals from at least two sound sources respectively through at least two microphones (MICs) to obtain respective original noisy signals of the at least two MICs in a time domain; for each frame in the time domain, using a first asymmetric window to perform a windowing operation on the respective original noisy signals of the at least two MICs to acquire windowed noisy signals; performing time-frequency conversion on the windowed noisy signals to acquire respective frequency-domain noisy signals of the at least two sound sources; acquiring frequency-domain estimated signals of the at least two sound sources according to the frequency-domain noisy signals; and obtaining audio signals produced respectively by the at least two sound sources according to the frequency-domain estimated signals.

Abstract: A video processing method includes: extracting a plurality of video frames from a source video with a first aspect ratio; clipping each of the plurality of video frames multiple times to generate a plurality of candidate clipping boxes with a second aspect ratio; respectively determining scores of the plurality of candidate clipping boxes of each of the plurality of video frames, the scores representing playing effects of the video frames in response to that each of the candidate clipping boxes is taken as a clipping range; selecting a clipping box of each of the video frames from the plurality of candidate clipping boxes based on the scores; and clipping each of the video frames by using the selected clipping box, and generating a target video with the second aspect ratio.

Abstract: Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for information processing. The method may be applied to an electronic equipment. The electronic equipment may collect environmental audio information when the electronic equipment plays multimedia. The electronic equipment may also perform noise detection on the environmental audio information to determine whether the environmental audio information represents a target noise scenario. The electronic equipment may also process a parameter of the multimedia played by the electronic equipment when the environmental audio information represents the target noise scenario.

Abstract: Aspects of the disclosure provide a training method and device for an image enhancement model and a storage medium. The method can include inputting each training input image group into the image enhancement model to obtain a predicted image output by the image enhancement model, and training the image enhancement model until convergence through each loss function respectively corresponding to each training pair. Each loss function can include a plurality of gray scale loss components corresponding to a plurality of frequency intervals one to one, and each gray scale loss component is determined based on a difference between a gray scale frequency division image of each predicted image and a gray scale frequency division image of the corresponding target image in each frequency interval, and different gray scale loss components correspond to different frequency intervals.

Abstract: The present disclosure relates to a corpus processing method, a corpus processing apparatus and a storage medium. The corpus processing method can include obtaining a message input by a user, retrieving a reply message matching the message input by the user from a plurality of candidate corpora, in which the plurality of the candidate corpora includes candidate corpora obtained after removing a negative emotion corpus, and sending the reply message.

Abstract: Text content is determined. The text content is input to a content classifying model. The content classifying model is adapted to determine a probability of the text content belonging to a category. An evaluated value of quality of the text content is determined according to the probability of the category and a weight of the category. The weight represents importance of the category.