A method for hybrid noise suppression involves receiving a first processed audio signal and a second processed audio signal from an audio device. The first processed audio signal results from a comprehensive audio processing including a noise spectrum estimate-based noise suppression performed on a noisy audio input signal obtained by the audio device. The second processed signal results from a partial audio processing excluding the noise spectrum estimate-based noise suppression performed on the noisy audio input signal. The method further involves temporally aligning the second processed audio signal with the first processed audio signal, predicting a noise suppression parameter using a neural network model operating on the second processed audio signal after the temporal alignment, generating a noise-suppressed audio output signal from the first processed audio signal after the temporal alignment using the noise suppression parameter, and outputting the noise-suppressed audio output signal.

A noise suppressor includes a memory; and a processor coupled to the memory, the processor is configured to: acquire a noise signal in a vicinity of a head region of an occupant seated in a vehicle seat; generate a control sound having an opposite phase to the acquired noise signal; acquire a sound signal having a predetermined frequency that is different from the noise signal; and output, as an output sound signal, the acquired sound signal in combination with the generated control sound.

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.

A method for hybrid noise suppression involves receiving a first processed audio signal and a second processed audio signal from an audio device. The first processed audio signal results from a comprehensive audio processing including a noise spectrum estimate-based noise suppression performed on a noisy audio input signal obtained by the audio device. The second processed signal results from a partial audio processing excluding the noise spectrum estimate-based noise suppression performed on the noisy audio input signal. The method further involves temporally aligning the second processed audio signal with the first processed audio signal, predicting a noise suppression parameter using a neural network model operating on the second processed audio signal after the temporal alignment, generating a noise-suppressed audio output signal from the first processed audio signal after the temporal alignment using the noise suppression parameter, and outputting the noise-suppressed audio output signal.

A vehicle noise cancellation system and method, operable for: receiving local contextual information affecting a soundwave present in a vehicle; receiving remote contextual information affecting the soundwave present in the vehicle; receiving vehicle occupant information; processing the local contextual information, the remote contextual information, and the vehicle occupant information to generate an augmented anti-soundwave; and delivering the augmented anti-soundwave to an occupant of the vehicle to mitigate the soundwave present in the vehicle. Delivering the augmented anti-soundwave to the occupant of the vehicle to mitigate the soundwave present in the vehicle may include delivering the first augmented anti-soundwave to an occupant present in the first zone of the vehicle and delivering the second augmented anti-soundwave to an occupant present in the second zone of the vehicle using a plurality of speakers.

A system for controlling an engine tone by an artificial intelligence based on a sound quality index of a vehicle may include a sound output device for generating a reinforcing sound to reinforce an engine sound of the vehicle; an engine characteristic measurement sensor for measuring sound source characteristics of the engine sound; an interior noise measurement sensor for detecting interior noise of the vehicle; a signal processing controller that receives signals from the engine characteristic measurement sensor in real time and controls the sound output device such that the engine sound reaches a target tone; and a tone control operation unit connected to the signal processing controller to optimize the sound quality index such that the engine sound reaches the target tone through the artificial intelligence.

A noise suppressor includes a memory; and a processor coupled to the memory, the processor is configured to: acquire a noise signal in a vicinity of a head region of an occupant seated in a vehicle seat; generate a control sound having an opposite phase to the acquired noise signal; acquire a sound signal having a predetermined frequency that is different from the noise signal; and output, as an output sound signal, the acquired sound signal in combination with the generated control sound.

A system for cancelling an internally generated noise includes a processing device disposed on a patient. The processing device includes a sensor configured to record a pulse sound waveform generated by a pulse of the patient and at least one waveform processing circuit configured to output a cancellation sound waveform based on the recorded pulse sound waveform. The system also includes a sound output device coupled to the processing device. The sound output device is configured to output the cancellation sound waveform to cancel or minimize the pulse sound waveform corresponding to the internally generated noise.

An acoustic device includes: an imaging device configured to take a sample image of a space as a sound field and create an image data on the space based on the taken sample image; a sound collector configured to collect a sound generated in the space or to collect a previously-collected acoustic data therein; and a computation part configured to previously compute a plurality of parameters relevant to a coefficient of spatial acoustic filter corresponding to the sample image of the space and previously learn a sound field model of the space shown in the sample image. The computation part is configured to construct a sound field model of the sample image taken by the imaging device or of a previously-taken sample image, from the acoustic data collected by the sound collector, using the coefficient of spatial acoustic filter.

A method includes receiving a signal that includes noise, generating a reference signal that comprises an estimate of the noise included in the received signal, and using the reference signal to remove at least part of the noise from the received signal. The reference signal is generated by a model built using machine learning. A system includes a first apparatus that carries a signal that includes noise, and a processor based apparatus configured to execute steps including receiving the signal that includes the noise, generating a reference signal that comprises an estimate of the noise included in the received signal, and using the reference signal to remove at least part of the noise from the received signal. A storage medium storing one or more computer programs is also provided.