Abstract: The present disclosure discloses a method for erasing handwriting on an electronic whiteboard, including: obtaining first contact points; enabling an erasing mode when a quantity of the first contact points is greater than a preset value; obtaining second contact points in the erasing mode, and calculating an erasing region according to the first contact points and the second contact points; and deleting handwriting data in the erasing region. A method, an apparatus and a device for erasing handwriting on an electronic whiteboard provided by the present disclosure can enter an erasing mode rapidly and erase handwriting effectively, thus improving the practicability of the electronic whiteboard.

Abstract: The present disclosure discloses a multi-device recording synchronization method and system, and a conference system, and belongs to the field of recording marking. For the problem in the prior art that a device that supports a recording marking function only supports generation and use on a single device, the present disclosure provides a system, including one master and a plurality of slaves. The master is connected to the plurality of slaves. The master operates according to the instruction sent by the slave and broadcasts state information globally. Through a network connection between the master and the slave, the master stores, on the master, recording and marking data sent by the slave, and synchronizes the data between devices, and a multi-device recording marking and synchronization system is applied to a conference system. The present disclosure is very convenient for use and has simple operation logic and low system costs.

Abstract: The present disclosure discloses a method and system for generating mixed voice data, and belongs to the technical field of voice recognition. In the method for generating mixed voice data according to the present disclosure, a pure voice and noise are collected first, normalization processing is performed on the collected voice data, randomization processing is performed on processed data, then GAIN processing is performed on the data, and finally filter processing is performed to obtain mixed voice data. The system for generating mixed voice data according to the present disclosure includes a collecting unit, a calculating unit, and a storage unit, the collecting unit being electrically connected to the calculating unit, and the calculating unit being connected to the storage unit through a data transmitting unit. The present disclosure provides the method and the system to meet the data requirement of deep learning.

Abstract: The present disclosure discloses a method for constructing a voice detection model and a voice endpoint detection system, and belongs to the technical field of voice recognition. In the method for constructing a voice detection model according to the present disclosure, audio data is first collected and a mixed voice is synthesized, feature extraction is performed on the mixed voice to obtain a 62-dimensional feature, and then the 62-dimensional feature is input to a recurrent neural network (RNN) model for training to obtain a voice detection model. The voice endpoint detection system according to the present disclosure includes a collecting unit, a calculating unit, a transmitting unit, and a terminal, the collecting unit being electrically connected to the calculating unit, and the calculating unit and the terminal being respectively connected to the transmitting unit. The voice detection model can be applied to a real-time conference communication device.

Abstract: The disclosure discloses a voice signal enhancing method and device, which divide a voice signal at the present scene into multiple frame signals based on a preset time interval; feed multiple frame signals into a trained neural network based on a preset step size, perform convolution operations on multiple frame signals through skip-connected convolutional layers to obtain multiple enhanced frame signals; superpose each enhanced frame signal according to the time domain of each enhanced frame signal to obtain an enhanced voice signal. Compared with the prior art, the present disclosure automatically enhances voice signals through the neural network without manual interference, so the effects and the application scenes of voice enhancement is not necessary to be limited by the preset method and method designers, thereby reducing the occurrence frequency of signal distortion and extra noises, which in turn improves the effects of the voice signal enhancement.

Abstract: The embodiments of the present disclosure disclose a firmware boot implementation method based on Flash chip simulation, the method comprising: when there are at least two MCUs, one of the MCUs is selected as a master MCU and each remaining MCU is as slave MCU which is connected with the master MCU respectively, and the master MCU is connected with a Flash chip; when the master MCU is started, the master MCU is started by reading firmware data in the Flash chip; when the slave MCU is started, the master MCU controls the slave MCU to be powered on, and the slave MCU sends a request for reading the firmware data to the master MCU; the master MCU transmits the request to the Flash chip, and transmits the firmware data returned by the Flash chip to the slave MCU, so as to start the slave MCU.