Baidu Online Network Technology (beijing) Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Xiaomi Mobile Software Co., Ltd.

Beijing Smartmi Technology Co., Ltd.

General Electric Company

Boe Technology Group Co., Ltd.

Ordos Yuansheng Optoelectronics Co., Ltd.

Lei Jia

Jinfeng Bai

Tao Sun

Xu Chen

Ce Zhang

Zhijie Chen

Wenfu Wang

Chao Tian

Sheng Qian

Xiaoyin Fu

Mingxin Liang

Guibin Wang

Zhengkun Gao

Junteng Zhang

Xingyuan Peng

Xin Li

Ying Li

Jizhou Huang

Jingyi Xu

Yanwei Ren

Zhen Wu

Bin Huang

Zhijian Wang

Yanan Yu

Cong Gao

Junyao Shao

Saisai Zou

Xiaokong Ma

Guolei Zhi

Guang Xi

Chuanlei Zhai

Xilei Wang

Jiankang Hou

Liqiang Zhang

Yongzhi Ji

Yi Wang

Chao Li

Qiang Li

Bin Yang

Xi Chen

Haifeng Wang

Tao Chen

Wei Wei

Bo Li

Qi Zhang

Zheng Li

Lin Lin

Yan Gao

Wulijibaier Tang

Yuelin Wang

Yong Guan

Wenlong Zhang

Zhihui Sun

Zhengxiang Jiang

Liao Zhang

Hao Dong

Xiaoguang Li

Gang Ji

Qiguang Zang

Guanbo Bao

Zhe Peng

Junhui Wen

Yuqiang Liu

Jingwei Hou

Yanhao Sun

Kenan Zhu

Chenxi Sun

Na Hu

Runqiang Han

Jiaxiang Ge

Ran Ji

Zhipeng Nie

Yufang Wu

Xiangyu Mu

Zhiqian Wang

Hua Liang

Wenkai Zhang

Shijun Cong

Mingshun Yang

Xinyong Zhou

Han Yuan

Yangyang Lu

Zhipeng Chen

A data processing method for a neural network accelerator, an electronic device and a storage medium are provided. The technical solution includes: obtaining data to be processed and an operation to be executed; obtaining a real-number full-connection operation corresponding to the operation to be executed; and performing the real-number full-connection operation on the data based on a real-number full-connection unit of the neural network accelerator to obtain a result of the operation to be executed for the data.

Data processing method for neural network accelerator, device and storage medium

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 pail 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.

Audio signal processing method, training method, apparatus and storage medium

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.

Negative ion generating device and air purifier

The disclosure provides a speech synthesis method, and an electronic device. The technical solution is described as follows. A text to be synthesized and speech features of a target user are obtained. Predicted first acoustic features based on the text to be synthesized and the speech features are obtained. A target template audio is obtained from a template audio library based on the text to be synthesized. Second acoustic features of the target template audio are extracted. Target acoustic features are generated by splicing the first acoustic features and the second acoustic features. Speech synthesis is performed on the text to be synthesized based on the target acoustic features and the speech features, to generate a target speech of the text to be synthesized.

Speech synthesis method, and electronic device

The present disclosure provides a method of recognizing speech offline, electronic device, and a storage medium, relating to a field of artificial intelligence such as speech recognition, natural language processing, and deep learning. The method may include: decoding speech data to be recognized into a syllable recognition result; transforming the syllable recognition result into a corresponding text as a speech recognition result of the speech data.

Method of recognizing speech offline, electronic device, and storage medium

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.

Speech recognition and codec method and apparatus, electronic device and storage medium

The disclosure provides a method and an apparatus for allocating memory, and an electronic device. Multiple frames of speech data are received and input to a neural network model. The neural network model is configured to ask for multiple data tensors when processing the multiple frames of speech data, and the multiple data tensors share a common memory.

Method and apparatus for allocating memory and electronic device

The present disclosure provides a speech processing method, and a method for generating a speech processing model, related to a field of signal processing technologies. The speech processing method includes: obtaining M speech signals to be processed and N reference signals; performing sub-band decomposition on each of the M speech signals and each of the N reference signals to obtain frequency-band components in each speech signal and each reference signal; processing the frequency-band components in each speech signal and each reference signal by using an echo cancellation model, to obtain an ideal ratio mask corresponding to the N reference signals in each frequency band of each speech signal; and performing echo cancellation on each frequency-band component of each speech signal based on the ideal ratio mask corresponding to the N reference signals in each frequency band of each speech signal, to obtain M echo-cancelled speech signals.

Speech processing method and method for generating speech processing model

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.

Voice generating method and apparatus, electronic device and storage medium

A method of registering an attribute in a speech synthesis model, an apparatus of registering an attribute in a speech synthesis model, an electronic device, and a medium are provided, which relate to a field of an artificial intelligence technology such as a deep learning and intelligent speech technology. The method includes: acquiring a plurality of data associated with an attribute to be registered; and registering the attribute in the speech synthesis model by using the plurality of data associated with the attribute, wherein the speech synthesis model is trained in advance by using a training data in a training data set.

Method of registering attribute in speech synthesis model, apparatus of registering attribute in speech synthesis model, electronic device, and medium

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.

Speech synthesis method and apparatus, device and computer storage medium

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.

Method of processing audio data, electronic device and storage medium

An array substrate of a display device includes a pixel electrode layer on a substrate, which includes active pixel electrodes in an active display region; outermost active pixel electrodes include a first active pixel electrode including a first pixel electrode edge and a second pixel electrode edge; in a first direction, the first pixel electrode edge is between the second pixel electrode edge and a frame region. One of the array substrate and an opposite substrate of the display device includes a common electrode layer including a first extended common electrode which includes a first extended portion extending beyond the first active pixel electrode; a first extended portion edge of the first extended portion and a first substrate edge of the substrate respectively extend in a second direction; in the first direction, the first extended portion edge is located between the first substrate edge and the first pixel electrode edge.

Display device and array substrate

The present disclosure provides a method and an apparatus for managing navigation broadcast, and a device, related to an Intelligent transportation technology field. A specific implementation solution includes: obtaining a geographical identifier of a user; obtaining a statement-conversion template set corresponding to the geographical identifier based on the geographical identifier of the user; converting a standard navigation broadcast statement based on the statement-conversion template set to generate a geographical navigation broadcast statement; and performing navigation broadcast based on the geographical navigation broadcast statement. Thereby, the navigation broadcasts are matched with respective regions, users in different regions are provided with diversified and personalized navigation broadcasts.

Method for managing navigation broadcast, and device

A method for compressing a neural network model includes acquiring a to-be-compressed neural network model. A first bit width, a second bit width and a target thinning rate corresponding to the to-be-compressed neural network model are determined. A target value is obtained according to the first bit width, the second bit width and the target thinning rate. Then the to-be-compressed neural network model is compressed 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.

Method and apparatus for compressing neural network model

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Beijing, China

Lei Jia