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

Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

University of Science and Technology Beijing

Beijing New Building Materials Public Limited Company

Ce Zhang

Xin Li

Tao Chen

Peng Wang

Lei Jia

Jinfeng Bai

Xu Chen

Bin Huang

Xiaokong Ma

Zheng Li

Wei Xu

Yanjun Liu

Bowen Liu

Sheng Qian

Zhen Wu

Zhijian Wang

Haidong Wang

Xuanhui Qu

Wei Xin

Guibin Wang

Xingyuan Peng

Desheng Li

Wenkai Zhang

Xin Lu

Yu Pan

Chuanlei Zhai

Jiazhen Zhang

Jianzhuo Sun

Yucheng Yang

Tieliang Yu

Dayong Tang

Liang Feng

Huigang Wang

Hongxu Zhang

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 invention provides a high-strength and high-plasticity titanium matrix composite and a preparation method thereof. The preparation method includes: preparing high-oxygen hydride-dehydride titanium powder using a high-temperature rotary ball grinding treatment process, in which the prepared hydride-dehydride titanium powder has a particle size of 10-40 μm, and has an oxygen content of 0.8-1.5 wt. %; preparing high-purity ultra-fine oxygen adsorbent powder using a wet grinding method of high-energy vibration ball grinding treatment process; in which a purity of the oxygen adsorbent powder is ≥99.9%, and a particle size of the oxygen adsorbent powder is ≤8 μm; mixing the high-oxygen hydride-dehydride titanium powder with the oxygen adsorbent powder in a protective atmosphere, and then press-forming the powder obtained after mixing to obtain a raw material blank; and performing atmosphere protective sintering treatment on the raw material blank to obtain a titanium matrix composite. The method prepares a titanium matrix composite reinforced by in-situ self-generating multi-scale Ca—Ti—O, TiC, TiB particles. The microstructure and grains are effectively refined, and the strength and plasticity of the material are significantly improved.

High-strength and high-plasticity titanium matrix composite and preparation method thereof

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.

Method and apparatus for recognizing voice

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.

Speech recognition method, device, and computer-readable storage medium

The present disclosure discloses an interactive voice-control method and apparatus, a device and a medium. The method includes: obtaining a sound signal at a voice interaction device and recognized information that is recognized from the sound signal; determining an interaction confidence of the sound signal based at least on at least one of an acoustic feature representation of the sound signal and a semantic feature representation associated with the recognized information; determining a matching status between the recognized information and the sound signal; and providing the interaction confidence and the matching status for controlling a response of the voice interaction device to the sound signal.

Interactive voice-control method and apparatus, device and medium

A method, apparatus, device, and storage medium for voice interaction. A specific embodiment of the method includes: extracting an acoustic feature from received voice data, the acoustic feature indicating a short-term amplitude spectrum characteristic of the voice data; applying the acoustic feature to a type recognition model to determine an intention type of the voice data, the intention type being one of an interaction intention type and a non-interaction intention type, and the type recognition model being constructed based on the acoustic feature of training voice data; and performing an interaction operation indicated by the voice data, based on determining that the intention type is the interaction intention type.

Method and apparatus for voice interaction, device and computer-readable storage medium

Growing community of inventors

Beijing, China

Ce Zhang