Agora Lab, Inc.

Xiaohan Zhao

Ruofei Chen

Siqiang Yao

Bo Wu

Fan Li

Jimeng Zheng

Liangliang Wang

Yunyun Ji

Zihe Liu

A computer-implemented method for suppressing noise from audio signal uses both statistical noise estimation and neural network noise estimation to achieve more desirable noise reduction. The method is performed by a noise suppression computer software application running on an electronic device. The noise suppression computer software application first transforms the speech signal in time domain into frequency domain before determining a statistical noise estimate and a neural network noise estimate. The noise suppression computer software application merges the two noise estimates to derive a final noise estimate, and determines and refines a noise suppression filter. The filter is applied to the speech signal in frequency domain to obtain an enhanced signal. The enhanced signal is transformed back into time domain.

System and method for suppressing noise from audio signal

A method for recovering a current frame of an audio stream includes detecting that a current packet is lost, the current packet including an audio signal; splitting one or more frames into respective high-band signals and respective low-band signals, the one or more frames precede the current frame in the audio stream; inferring a current low-band signal of the current frame using, as inputs to a machine-learning model, the respective low-band signals; combining the inferred current low-band signal with the respective high-band signals to obtain the current frame; and adding the current frame to a playout buffer.

Digital voice packet loss concealment using deep learning

A new audio quality assessment system includes an assessment system running in a receiver system of a VoIP communication system. The new audio quality assessment system determines an accurate MOS of a VoIP call within a time window. The audio quality assessment system determines an effective PLC counter, a PLC impact factor, an effective AS counter, an AS impact factor, a network impact factor, a codec type of the received voice packets, a bitrate of the received voice packets, an initial MOS from a configured codec-bitrate MOS table, and determines the accurate MOS based on these data. The determined MOS is more accurate and efficiently obtained since it is based on efficiently collected statistics of the receiver system's modules and a pre-configured codec-bitrate MOS table.

Voice quality assessment system

A method includes extracting, from a frame of the audio sample, spectral features indicative of cochlear impulse response of an auditory system; obtaining an estimate of a speech signal in the frame from a neural network that is trained, during a training phase, to accept the spectral features as input and output the estimate of the speech signal, where the estimate of the speech signal includes reverberation and excludes noise present in the frame; mapping the estimate of the speech signal to a frequency domain of the frame using mapping parameters obtained during the training phase to obtain an estimate of a reverberant speech spectrum in the frame; and obtaining, from a time-distributed neural network, a dereverberant frame of the frame, where the estimate of the reverberant speech spectrum in the frame is used as an input to the time-distributed neural network.

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Shanghai, China

Xiaohan Zhao