An active noise control system and associated auto-selection method for modeling a secondary path for the active noise control system are provided. The method includes the steps of: receiving a reference signal; filtering the reference signal with a secondary-path estimation filter to obtain a filtered reference signal, wherein the secondary path estimation filter is determined from a plurality of candidate secondary-path estimation filters; filtering the reference signal with an adaptive filter to provide a compensation signal; sensing a residual noise signal at a listening position of the active noise control system; and adapting filter coefficients of the adaptive filter according to the residual noise signal and the filtered reference signal.

Systems and methods are provided an audio signal enhancement system that attenuates platform fan noise. Fan noise is a common type of self-noise in laptops and other devices, and fan noise can significantly degrade the quality of audio captured by built-in microphones. A neural network model is provided that enhances microphone Signal-to-Noise Ratio (SNR) and Signal-to-Distortion-plus-Noise Ratio (SDNR). The systems and methods also reduce algorithmic latency. The model architecture includes a Recurrent Neural Network, and a custom Gated Recurrent Unit layer is provided that uses fewer unique matrix weights and fewer biases and has fewer compute operations using fewer parameters. A platform self-noise suppression system is provided that eliminates low-amplitude platform self-noise signals. The model can predict when the platform fan is active, and remove the platform noise. In some examples, when the model predicts that the platform fan is not active, the model focuses on removing microphone self-noise.

A noise cancelling system for a vehicle includes a microphone, at least one first sensor configured to collect first data related to an element that generates a noise sound, at least one second sensor configured to collect second data related to an element that changes a secondary path of the noise sound, a controller configured to select a secondary path model corresponding to the second data from among a plurality of pre-stored secondary path models, input the first data to a secondary path filter corresponding to the selected secondary path model, and generate an anti-noise signal based on output data of the secondary path filter and error data received from the microphone, and a speaker configured to output an anti-noise sound based on the anti-noise signal.

System and methods are provided for estimating the sound field from partial observations. Estimating an acoustic environment for virtual reality and augmented reality applications is a step in the creation of simulated acoustic sound scenes. In particular, the impulse responses of room can be estimated with a generative model. In a teleconferencing scenario with remote participants and a group of participants in a common physical space, giving the remote participants the impression that all other participants are sitting is in the same room acoustically requires filtering the speech of the remote participants with impulse responses estimated at the desired rendering position in the conference room.

An echo filtering method, an electronic device, a computer-readable storage medium, and an echo filtering apparatus are disclosed. The electronic device includes M microphones and N speakers. M and N are integers greater than 1. The method includes: obtaining N speaker signals corresponding to the N speakers; obtaining M microphone signals corresponding to the M microphones; and performing at least direct sound filtering on the N speaker signals and the M microphone signals to obtain a target signal. By using this method, better echo filtering effect can be obtained.

A noise reduction system for actively compensating background noise in a passenger transport area of a vehicle. The noise reduction system includes a nonlinearity filter unit having a model of a non-linear transfer function of the sound generator, wherein the nonlinearity filter unit is configured to receive the anti-noise signal and to generate a filtered anti-noise signal by applying a non-linear filter function on the anti-noise signal, which is based on the model of the non-linear transfer function in that the non-linear response of the sound generator is at least partially corrected when driven by the filtered anti-noise signal. Wherein the nonlinearity filter unit is further configured to output the filtered anti-noise signal to a sound generator.

Various embodiments disclose a computer-implemented method that can include driving an audio output device to reproduce a stimulus signal when a wearable device is placed along an ear canal of a user, receiving a sound signal from a microphone based on the stimulus signal, and determining, based on the sound signal and a calculated response of the microphone retrieved from a memory, one or more characteristics of the ear canal of the user, wherein the one or more characteristics of the ear canal comprises an ear canal impedance, and applying, based on the one or more characteristics of the ear canal, an ear canal response correction to an output signal played back by the audio output device.

Various implementations include a method of training a road noise cancelation (RNC) system for a vehicle, including: providing inputs to RNC system, the inputs obtained from: a set of ear-mounted microphones on a user, at least one transducer, an accelerometer, a set of cabin microphones in the vehicle, and a controller area network (CAN) bus, the inputs from the set of ear-mounted microphones on the user approximating a signal detected by the ears of the user; adapting a set of parameters in the RNC system defining an estimated signal detected at respective ears of the user based on the inputs; and generating at least one of the following for input during an operating mode of the RNC system: estimated ear microphone signals based on the adapted set of parameters, or a set of projection filters for use in determining an estimated ear signal at the respective ears of the user.

System and methods are provided for estimating the sound field from partial observations. Estimating an acoustic environment for virtual reality and augmented reality applications is a step in the creation of simulated acoustic sound scenes. In particular, the impulse responses of room can be estimated with a generative model. In a teleconferencing scenario with remote participants and a group of participants in a common physical space, giving the remote participants the impression that all other participants are sitting is in the same room acoustically requires filtering the speech of the remote participants with impulse responses estimated at the desired rendering position in the conference room.