Beijing Didi Infinity Technology and Development Co., Ltd.

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

Beijing Baidu Netcom Science and Technology Co., Ltd.

Hui Song

Yongtao Sha

Chengyun Deng

Yi Zhang

Si Qin

Shiqian Ma

A system and method for concurrent multi-path processing of audio signals for automatic speech recognition is presented. Audio information defining a set of audio signals may be obtained (). The audio signals may convey mixed audio content produced by multiple audio sources. A set of source-specific audio signals may be determined by demixing the mixed audio content produced by the multiple audio sources. Determining the set of source-specific audio signals may comprises providing the set of audio signals to both a first signal processing path and a second signal processing path (). The first signal processing path may determine a value of a demixing parameter for demixing the mixed audio content (). The second signal processing path may apply the value of the demixing parameter to the individual audio signals of the set of audio signals () to generate the individual source-specific audio signals ().

Concurrent multi-path processing of audio signals for automatic speech recognition systems

A method of echo path delay destination and echo cancellation is described in this disclosure. The method includes: obtaining a reference signal, a microphone signal, and a trained multi-task deep neural network, wherein the multi-task deep neural network comprises a first neural network and a second neural network; generating, using the first neural network of the multi-task deep neural network, an estimated echo path delay based on the reference signal and the microphone signal; updating the reference signal based on the estimated echo path delay; and generating, using the second neural network of the multi-task deep neural network, an enhanced microphone signal based on the microphone signal and the updated reference signal.

Multi-task deep network for echo path delay estimation and echo cancellation

Embodiments of the disclosure provide systems and methods for enhancing audio signals. The system may include a communication interface configured to receive multi-channel audio signals acquired from a common signal source. The system may further include at least one processor. The at least one processor may be configured to separate the multi-channel audio signals into a first audio signal and a second audio signal in a time domain. The at least one processor may be further configured to decompose the first audio signal and the second audio signal in a frequency domain to obtain a first decomposition data and a second decomposition data, respectively. The at least one processor may be also configured to estimate a noise component in the frequency domain based on the first decomposition data and the second decomposition data. The at least one processor may be additionally configured to enhance the first audio signal based on the estimated noise component. The system may also include a speaker configured to output the enhanced first audio signal.

Systems and methods for enhancing audio signals

Embodiments of the disclosure provide systems and methods for audio signal processing. An exemplary system may include a communication interface configured to receiving a first audio signal acquired from an audio source through a first channel, and a second audio signal acquired from the same audio source through a second channel. The system may also include at least one processor coupled to the communication interface. The at least one processor may be configured to determine channel features based on the first audio signal and the second audio signal individually and determine a cross-channel feature based on the first audio signal and the second audio signal collectively. The at least one processor may further be configured to concatenate the channel features and the cross-channel feature and estimate spectral-spatial masks for the first channel and the second channel using the concatenated channel features and the cross-channel feature. The at least one processor may also be configured to perform beamforming based on the spectral-spatial masks for the first channel and the second channel.

Systems and methods for audio signal processing using spectral-spatial mask estimation

A speech communication system for improving speech intelligibility may comprise one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the system to perform: determining a cutoff frequency based on an estimation of a spectrum of noise, wherein the cutoff frequency defines a noise dominant region of frequency; lifting a spectrum of a speech above the noise dominant region of frequency, wherein a frequency range of the spectrum of the speech increases by the cutoff frequency; and applying an adaptive filter to the speech to achieve echo cancelation, wherein the adaptive filter is controlled by a volume of the noise.

Speech communication system and method for improving speech intelligibility

The present disclosure discloses a method for processing a voice in interior environment of a vehicle, an electronic device and a storage medium. The method includes the following. A reference audio is acquired, and the reference audio is recorded to obtain a recorded audio. A pure voice is acquired. Noise data for each part or period of the recorded audio satisfying a target signal-to-noise ratio condition pertaining to that part is selected from the recorded audio, and the noise data is superimposed to the pure data to obtain a noisy voice. The noisy voice and the reference audio are inputted to an acoustic echo canceller (AEC) module as inputted data. The AEC module is configured to perform an echo cancellation operation on the inputted data to obtain training data having AEC residual noise.

Method for processing voice in interior environment of vehicle and electronic device using noise data based on input signal to noise ratio

The present disclosure provides a speech recognition method and device based on artificial intelligence. The method includes: collecting signals of an array of microphones to obtain a plurality of first speech signals; filtering out a reverberation signal in each first speech signal to obtain a plurality of second speech signals, and obtaining a third speech signal based on the plurality of second speech signals; performing noise extraction on each first speech signal based on the third speech signal to obtain a plurality of first noise signals; and filtering and adding the plurality of first noise signals to obtain a second noise signal, and subtracting the second noise signal from the third speech signal to obtain a target speech signal.

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Beijing, China

Hui Song