Dolby Laboratories Licensing Corporation

Beijing Baidu Netcom Science and Technology Co., Ltd.

Guiping Wang

Lie Lu

Zhiwei Shuang

Jun Dai

Xuemei Yu

Ziyu Yang

Qingyuan Bin

Guorui Huang

Libin Luo

Jinli Dong

A method of audio content identification includes using a two-stage classifier. The first stage includes previously-existing classifiers and the second stage includes a new classifier. The outputs of the first and second stages calculated over different time periods are combined to generate a steering signal. The final classification results from a combination of the steering signal and the outputs of the first and second stages. In this manner, a new classifier may be added without disrupting existing classifiers.

Audio content identification

A method for steering binauralization of audio is provided. The method comprises steps of: receiving () an audio input signal, calculating () a confidence value indicating a likelihood that a current audio frame of the audio input signal comprises binauralized audio; determining () a state signal based on the confidence value; determining () a steering signal, based on the first confidence value, the state signal and an energy value of the audio frame; and generating () an audio output signal with steered binauralization by processing the audio input signal according to the steering signal.

Steering of binauralization of audio

The present disclosure provides a call processing method, apparatus, electronic device and storage medium and relates to the field of cloud computing. The method may comprise: obtaining a calling subscriber's status information in real time while an intelligent dialogue robot is used to make a call with the calling subscriber; when it is determined that a call form of the intelligent dialogue robot needs to be adjusted, correspondingly adjusting the call form of the intelligent dialogue robot according to current status information of the calling subscriber. The solution of the present disclosure may be employed to improve the call performance of the intelligent dialogue robot.

Call processing method, electronic device and storage medium

Embodiments of training signal processing models for component replacement in signal processing systems are disclosed. A device for training a third signal processing model via a machine learning method include a first calculating unit, a second calculating unit and a training unit. The first calculating unit calculates a first output from each first sample in a first sample set based on a first signal processing model. The second calculating unit calculates a second output from the first sample based on a second signal processing model. The training unit trains a third signal processing model by minimizing a first cost including a first error between the first output and a third output, so that the combination of the second signal processing model and the third signal processing model can simulate the behaviors of the first signal processing model on the first sample set. The third output is an output of the third signal processing model in response to an input including the second output.

Training signal processing model for component replacement in signal processing system

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Beijing, China

Guiping Wang