Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to receive multiple audio signals obtained from ones of a plurality of microphones of a microphone array, implement an audio zooming based on the audio signals by selectively focusing and enhancing first ones of the audio signals and by attenuating other ones of the audio signals, and control an output of audio based on the audio zooming, and the audio zooming includes a consolidating of a plurality of directional features of the first ones of the audio signals within a field around the microphone array and a countering based on determining directional aspects of the other ones of the audio signals from outside of the field.

Abstract: A method includes inputting a mixture signal comprising speech, background noise, and reverberation into a denoising stage that generates a denoised mixture signal with reduced noise compared to the mixture signal; fusing the denoised mixture signal with the mixture signal to generate a fused signal; and inputting the fused signal into a dereverberation and restoration (DR) stage to generate an output signal with reduced reverberation compared to the mixture signal.

Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to obtain an audio signal from a channel of at least one microphone of a plurality of microphones, estimate a Kalman gain based on the audio signal, share the Kalman gain to a plurality of channels of other ones of the plurality of microphones, and output an AHS signal from the channel and the plurality of channels, wherein the AHS signal is a version of the audio signal in which acoustic howling noise of the audio signal is suppressed and target audio of the audio signal is sustained.

Abstract: Method, apparatus, and non-transitory storage medium for hybrid acoustic howling suppression based on a frequency filter model and a deep neural network are provided. The method may include receiving a speech signal, the speech signal including target speech, feedback, and noise, and inputting the speech signal into a trained hybrid neural-network based howling suppression model, wherein the trained hybrid neural-network based howling suppression model is trained using training speech signal and pre-processed acoustic feedback from a first frequency filter model. The method may also include generating an enhanced speech signal with suppressed howling as an output of the trained hybrid neural-network based howling suppression model, wherein the enhanced speech signal is used to update parameters of the first frequency filter model.

Abstract: A roadside photovoltaic site selection method considering glare impact includes: obtaining latitudes, longitudes, ground elevations, and contour vertex eights of photovoltaic modules above ground of a photovoltaic array; calculating a sun movement path, determining an installation manner based on predicted solar radiation on inclined surfaces of the photovoltaic array; calculating an elevation angle and an azimuth angle of each reflected light caused by the inclined surfaces; arranging observation points and conducting a glare impact analysis for each observation point; determining whether to adjust an installation tilt angle of each photovoltaic module or distances between adjacent photovoltaic modules; comparing solar radiation received on the inclined surfaces between the critical tilt angle and the optimal tilt angle; in response to a difference in the solar radiation less than a set value, accepting the critical tilt angle; and completing the glare impact analysis for each observation point, and providin

Abstract: A roadside photovoltaic site selection method considering glare impact includes: obtaining latitudes, longitudes, ground elevations, and contour vertex eights of photovoltaic modules above ground of a photovoltaic array; calculating a sun movement path, determining an installation manner based on predicted solar radiation on inclined surfaces of the photovoltaic array; calculating an elevation angle and an azimuth angle of each reflected light caused by the inclined surfaces; arranging observation points and conducting a glare impact analysis for each observation point; determining whether to adjust an installation tilt angle of each photovoltaic module or distances between adjacent photovoltaic modules; comparing solar radiation received on the inclined surfaces between the critical tilt angle and the optimal tilt angle; in response to a difference in the solar radiation less than a set value, accepting the critical tilt angle; and completing the glare impact analysis for each observation point, and providin

Abstract: Method, apparatus, and non-transitory storage medium for training a deep neural-network model jointly for acoustic echo suppression and acoustic howling suppression are provided. The method may include generating a teacher speech signal for training the deep neural-network model based on a input speech from a speech system and at least one reference signal. The deep neural-network model is trained jointly for both acoustic echo suppression and acoustic howling suppression by using the teacher speech signal and a correlation loss. During training of the deep neural-network model, the training task formulates a recurrent feedback suppression process as an instantaneous speech separation task using the teacher-forced training strategy.

Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to obtain an audio signal from a channel of at least one microphone of a plurality of microphones, estimate a Kalman gain based on the audio signal, share the Kalman gain to a plurality of channels of other ones of the plurality of microphones, and output an AHS signal from the channel and the plurality of channels, wherein the AHS signal is a version of the audio signal in which acoustic howling noise of the audio signal is suppressed and target audio of the audio signal is sustained.

Abstract: Method, apparatus, and non-transitory storage medium for hybrid acoustic howling suppression based on a frequency filter model and a deep neural network are provided. The method may include receiving a speech signal, the speech signal including target speech, feedback, and noise, and inputting the speech signal into a trained hybrid neural-network based howling suppression model, wherein the trained hybrid neural-network based howling suppression model is trained using training speech signal and pre-processed acoustic feedback from a first frequency filter model. The method may also include generating an enhanced speech signal with suppressed howling as an output of the trained hybrid neural-network based howling suppression model, wherein the enhanced speech signal is used to update parameters of the first frequency filter model.

Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to receive an audio signal obtained from a microphone, input the audio signal into frequency-domain Kalman filter (FDKF), input the audio signal and an output from the FDKF into a neural network, estimate, based on the audio signal and the output from the FDKF, and removing feedback signals from the audio signal by the neural network, and output a version of the audio signal in which a target vocal signal is enhanced by removal of the feedback signals from the audio signal by the neural network.

Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to receive an audio signal obtained from a microphone, input the audio signal into frequency-domain Kalman filter (FDKF), input the audio signal and an output from the FDKF into a neural network, estimate, based on the audio signal and the output from the FDKF, and removing feedback signals from the audio signal by the neural network, recover, by a codec receiving an output from the neural network, vocal quality of a target vocal signal, and output a version of the audio signal in which the target vocal signal is enhanced by removal of the feedback signals from the audio signal by the neural network and by recovery of the vocal quality by the codec.

Abstract: A method includes receiving a mixture signal comprising at least a first speaker, a second speaker, and background noise, the first speaker having a first distance to a microphone that outputs the mixture signal, the second speaker having a second distance to the microphone; training one or more neural networks to output a target channel and an interference channel by: inputting, into the one or more neural networks, the mixture signal and a task ID associated with one of the first speaker and the second speaker as a target speaker; determining a loss function based on the first distance and the second distance; and updating the neural network based on the loss function.

Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to receive an audio signal obtained from a microphone, input the audio signal into a neural-network based AHS model, train the neural-network based AHS model based on input signals which are recursively generated from the audio signal during training of the AHS model, and output an AHS signal from the neural-network based AHS model in which AHS is applied to the audio signal, wherein the AHS signal is a version of the audio signal in which acoustic howling noise of the audio signal is suppressed and target audio of the audio signal is sustained.

Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to receive an audio signal obtained from a microphone, input the audio signal into a neural-network based AEC model, and output an AEC signal from the neural-network based AEC model in which AEC is applied to the audio signal, and the AEC signal is a version of the audio signal in which acoustic echo noise of the audio signal is suppressed and target audio of the audio signal is sustained.

Abstract: A method, computer program, and computer system is provided for automated speech recognition. Audio data corresponding to one or more speakers is received. Covariance matrices of target speech and noise associated with the received audio data are estimated based on a gated recurrent unit-based network. A predicted target waveform corresponding to a target speaker from among the one or more speakers is generated by a minimum variance distortionless response function based on the estimated covariance matrices.

Abstract: A battery management system can include a host microcontroller, which can be operated in accordance with a first clock signal, and an analog front end (AFE) circuit, where the AFE circuit can be operated in accordance with a second clock signal that can be unsynchronized with the first clock signal. The AFE circuit can include an input which can be configured to receive a representation of a battery current associated with one or more cells in a battery system. The AFE circuit can also include current measurement circuitry, included in or coupled to the AFE circuit, which can be configured to generate an average current value representative of an average of the battery current for a specified time period. The host microcontroller can be configured to determine a time value representative of a duration of the specified time period, and determine, based on the indication of the average current value, a current transfer value of the battery current during the specified time period.

Abstract: The present disclosure provides a negative electrode plate for a lithium battery and a lithium-ion secondary battery comprising same. The negative electrode plate includes a negative electrode current collector and a negative electrode material, wherein the negative electrode material includes: a negative electrode active material including a silicon-based material or a mixture of graphite and silicon-based material; a conductive agent; and a polyacrylic acid-based binder having a pH of 4.5 to 8.5 and a viscosity of 3500 Pa·s to 30000 Pa·s.

Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to receive an audio signal obtained from a microphone, input the audio signal into a neural-network based AEC model, and output an AEC signal from the neural-network based AEC model in which AEC is applied to the audio signal, and the AEC signal is a version of the audio signal in which acoustic echo noise of the audio signal is suppressed and target audio of the audio signal is sustained.

Abstract: A video data processing method includes obtaining candidate video(s), performing feature extraction on each candidate video to obtain video attribute information corresponding thereto and including work and episode attribute information, obtaining source label information corresponding to each candidate video, classifying the candidate video(s) according to the source label information to obtain an initial video set containing candidate video(s) having same source label information, determining candidate video(s) that have target work attribute information as target video(s), sorting the target video(s) according to episode attribute information corresponding to the target video(s) to obtain sorted video(s), and, if the episode attribute information corresponding thereto satisfies episode legitimacy condition, determining the sorted video(s) as ordered album video(s) and generating a video album set containing the ordered album video(s).