A method for detecting a prominent tone of an input audio includes establishing a first analysis audio signal based on the input audio signal, establishing a second analysis audio signal based on the input audio signal, wherein an analysis audio signal of the first analysis audio signal and the second analysis audio signal is established by applying an analysis audio filter to the input audio signal, comparing the first analysis audio signal and the second analysis audio signal to obtain an energy level contrast, and determining a representation of the prominent tone by converting the energy level contrast by a contrast-to-frequency mapping function.

A sound control system for dental treatment, includes: ear-worn speakers equipped to be worn by a patient in the dentist; a microphone that converts, into an electrical signal, a sound including the voice of a medical staff in charge of the patient; a voice recognition module for recognizing the voice of the medical staff in charge from an electrical sound signal input from the microphone; a sound source module that stores a plurality of sound sources for mental and physical stability of the patient; a user interface including a sound source selection unit that allows the patient to select a play sound source provided via the ear-worn speakers from among the plurality of sound sources; and an output signal generation module for generating an output sound signal output via the ear-worn speakers.

Disclosed is an operation device including an interaction member, a microphone, a control circuit, and an audio signal processing circuit. The interaction member is used for interacting with a user. The control circuit periodically acquires scan data indicating the acting status of the interaction member. The audio signal processing circuit executes a noise removal process of removing noise from a collected audio signal collected by the microphone. The control circuit periodically transmits previously acquired scan data to the audio signal processing circuit. The audio signal processing circuit executes the noise removal process by using the scan data transmitted from the control circuit.

An action identification device acquires sound data from a microphone, calculates a feature amount of the sound data, determines whether or not a user is present in a space in which the microphone is installed, calculates a noise feature amount indicating a feature amount of noise based on the calculated feature amount and stores the calculated noise feature amount in a noise feature amount storage unit in a case where the user is not present in the space, subtracts the noise feature amount stored in the noise feature amount storage unit from the calculated feature amount to extract an action sound feature amount indicating a feature amount of an action sound generated by an action of the user in a case where the user is present in the space, and identifies an action of the user by using the action sound feature amount.

A computing device may receive audio data from a microphone representing audio in an environment of the device, which may correspond to an utterance and noise. A model may be trained to process the audio data to cancel noise from the audio data. The model may include an encoder that includes one or more dense layers, one or more recurrent layers, and a decoder that includes one or more dense layers.

A method for preparing reference signals for an echo cancellation system disposed in a vehicle, comprising the steps of: receiving a plurality of drive signals, each drive signal being provided to an associated transducer of a plurality of acoustic transducers such that the associated acoustic transducer transduces the drive signal into an acoustic signal, filtering each drive signal with a respective filter of a plurality of filters to produce a plurality of filtered signals, wherein each of the plurality of filters approximates a transfer function from an associated acoustic transducer to a microphone disposed within the vehicle such that the plurality of filtered signals each estimate a respective acoustic signal at the microphone; summing together at least a subset of the plurality of filtered signals to produce a summed reference signal; and outputting the summed reference signal to an echo cancellation system.

A noise estimation method including steps of generating gap confidence values in response to microphone output and playback signals, and using the gap confidence values to generate an estimate of background noise in a playback environment. Each gap confidence value is indicative of confidence of presence of a gap at a corresponding time in the playback signal, and may be a combination of candidate noise estimates weighted by the gap confidence values. Generation of the candidate noise estimates may but need not include performance of echo cancellation. Optionally, noise compensation is performed on an audio input signal using the generated background noise estimate. Other aspects are systems configured to perform any embodiment of the noise estimation method.

Provided in the present disclosure are a voice processing method, an apparatus, an electronic device, and a storage medium, the method comprising: detecting the working state of a current call system, and when the working state is a two-end speaking state or a remote-end speaking state, performing compression processing on a subsequent remote-end voice signal, acquiring a near-end voice signal by means of a microphone, performing echo processing on the basis of the near-end voice signal and the compression-processed remote-end voice signal to obtain an echo-processed near-end voice signal and a remaining echo signal, performing non-linear suppression processing on the near-end voice signal and the remaining echo signal, and performing gain control on the suppression-processed near-end voice signal.

A method for hybrid noise suppression. The method involves obtaining a noisy audio input signal, generating a noise-suppressed audio output signal by performing a combination of a noise spectrum estimate-based noise suppression and a machine learning-based noise suppression, and outputting the noise-suppressed audio output signal.

A method of suppressing wind noise of a microphone and/or an electronic device are disclosed. The method of suppressing wind noise of a microphone includes receiving an audio signal, obtaining a frequency spectrum of the audio signal and a power spectrum of the audio signal, determining a wind noise power spectrum of the audio signal based on the power spectrum, determining a wind noise suppression gain based on the wind noise power spectrum and the power spectrum, correcting the frequency spectrum according to the determined wind noise suppression gain, and converting the corrected frequency spectrum into a time domain to obtain a corrected audio signal.