# US 12,120,492 B2 · Non-coherent noise reduction for audio enhancement on mobile device

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- Number: US 12,120,492 B2
- Document ID: US12120492B2
- Status: Granted
- Kind: B2
- Application number: 17/876,388
- Filed: 2022-07-28
- Published: 2024-02-01
- Granted: 2024-10-15
- Earliest claimed: 2022-07-28
- Assignees: Mediatek Inc.
- Applicants: MediaTek Inc.
- Inventors: Chi Sheng Wu; Liang-Che Sun; Yiou-Wen Cheng; Shun-Chang Zhong
- Web page: https://patentgrep.com/patent/US12120492B2

## CPC

- H04R 3/04: for correcting frequency response
- G10L 21/0208: Noise filtering
- H04R 3/005: {for combining the signals of two or more microphones (specially adapted for hearing aids H04R25/407)}
- H04R 5/04: Circuit arrangements, {e.g. for selective connection of amplifier inputs/outputs to loudspeakers, for loudspeaker detection, or for adaptation of settings to personal preferences or hearing impairments (combinations of amplifiers H03F3/68; stereophonic systems H04S)}
- H04S 7/307: {Frequency adjustment, e.g. tone control (H04S7/301 takes precedence)}
- G10L 2021/02166: {Microphone arrays; Beamforming}
- H04R 2410/07: Mechanical or electrical reduction of wind noise generated by wind passing a microphone
- H04R 2430/20: Processing of the output signals of the acoustic transducers of an array for obtaining a desired directivity characteristic (H04R2203/12 takes precedence)
- H04R: LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; ELECTRIC HEARING AIDS; PUBLIC ADDRESS SYSTEMS (producing sounds with frequency not determined by supply frequency G10K)
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- H04S: STEREOPHONIC SYSTEMS

## Abstract

Various techniques pertaining to non-coherent noise reduction for audio enhancement on a multi-microphone mobile device are proposed. A processor receives a plurality of signals from a plurality of audio sensors corresponding to a plurality of channels responsive to sensing by the plurality of audio sensors. The processor then performs a non-coherent noise reduction on one or more signals of the plurality of signals to suppress one or more non-coherent noises in each of the one or more signals based on a respective signal-to-noise ratio (SNR) associated with each of the one or more signals. The processor further combines the plurality of signals subsequent the noise reduction to generate an output signal.

## Claims

### Claim 1

A method, comprising:
receiving, by a processor, a plurality of signals from a plurality of audio sensors corresponding to a plurality of channels responsive to sensing by the plurality of audio sensors;
performing non-coherent noise reduction, by a non-coherent noise estimator in the processor, on one or more signals of the plurality of signals to suppress at least one non-coherent noise in each of the one or more signals based on a respective signal-to-noise ratio (SNR) associated with each of the one or more signals;
combining, by the processor, the plurality of signals subsequent the noise reduction to generate an output signal; and
performing beamforming on the plurality of signals using:
the plurality of signals subsequent filtering by all-pass filters; and
an output of the non-coherent noise estimator to generate the output signal.

### Claim 2 (depends on 1)

The method of claim 1, wherein the performing of the non-coherent noise reduction comprises:
individually estimating a respective non-coherent noise corresponding to each frequency band of a plurality of frequency bands of each channel of the plurality of channels; and
determining, for each frequency band of each channel, a respective gain control parameter to provide a plurality of gain control parameters each of which corresponding to a respective frequency band of a plurality of frequency bands of each channel of the plurality of channels such that the respective non-coherent noise associated with a first frequency band of a first channel of the plurality of channels which is worse than the respective non-coherent noise associated with a second frequency band of the first channel is suppressed.

### Claim 3 (depends on 1)

The method of claim 1, wherein the performing of the non-coherent noise reduction comprises:
individually estimating a respective non-coherent noise associated with each channel of the plurality of channels to determine, for each channel, a plurality of gain control parameters each of which corresponding to a respective frequency band of a plurality of frequency bands of each channel of the plurality of channels; and
suppressing the respective non-coherent noise associated with at least one channel of the plurality of channels based on a combination of the gain control parameters corresponding to the at least one channel.

### Claim 4 (depends on 1)

The method of claim 1, wherein the performing of the non-coherent noise reduction comprises performing the non-coherent noise reduction by using a deep learning model or machine learning.

### Claim 5 (depends on 1)

The method of claim 1, wherein the combining of the plurality of signals comprises filtering the plurality of signals subsequent the noise reduction before combining the plurality of signals.

### Claim 6 (depends on 1)

The method of claim 1, wherein:
the output signal comprises a mono-audio output signal in an event that a quantity of the plurality of audio sensors is two; and
the output signal comprises a stereo-audio output signal in an event that a quantity of the plurality of audio sensors is three or more.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
performing artificial-intelligence (AI) noise reduction on the plurality of signals subsequent the beamforming to generate the output signal.

### Claim 8

A method, comprising:
receiving, by a processor, a plurality of signals from a plurality of audio sensors corresponding to a plurality of channels responsive to sensing by the plurality of audio sensors;
performing non-coherent noise reduction, by a non-coherent noise estimator in the processor, on one or more signals of the plurality of signals to suppress at least one non-coherent noise in each of the one or more signals by:
individually estimating a respective non-coherent noise corresponding to each frequency band of a plurality of frequency bands of each channel of the plurality of channels; and
determining, for each frequency band of each channel, a respective gain control parameter to provide a plurality of gain control parameters each of which corresponding to a respective frequency band of a plurality of frequency bands of each channel of the plurality of channels such that the respective non-coherent noise associated with a first frequency band of a first channel of the plurality of channels which is worse than the respective non-coherent noise associated with a second frequency band of the first channel is suppressed;
combining, by the processor, the plurality of signals subsequent the noise reduction to generate an output signal; and
performing beamforming on the plurality of signals using:
the plurality of signals subsequent filtering by all-pass filters; and
an output of the non-coherent noise estimator to generate the output signal.

### Claim 9 (depends on 8)

The method of claim 8, wherein the performing of the non-coherent noise reduction comprises performing the non-coherent noise reduction by using a deep learning model or machine learning.

### Claim 10 (depends on 8)

The method of claim 8, wherein the combining of the plurality of signals comprises filtering the plurality of signals subsequent the noise reduction before combining the plurality of signals.

### Claim 11 (depends on 8)

The method of claim 8, wherein:
the output signal comprises a mono-audio output signal in an event that a quantity of the plurality of audio sensors is two; and
the output signal comprises a stereo-audio output signal in an event that a quantity of the plurality of audio sensors is three or more.

### Claim 12 (depends on 8)

The method of claim 8, further comprising:
performing artificial-intelligence (AI) noise reduction on the plurality of signals subsequent the beamforming to generate the output signal.

### Claim 13

An apparatus, comprising:
a plurality of audio sensors configured to sense a plurality of channels; and
a processor coupled to the plurality of audio sensors, the processor configured to perform operations comprising:
receiving a plurality of signals from the plurality of audio sensors responsive to sensing by the plurality of audio sensors;
performing a non-coherent noise reduction, by a non-coherent noise estimator in the processor, on one or more signals of the plurality of signals to suppress at least one non-coherent noise in each of the one or more signals based on a respective signal-to-noise ratio (SNR) associated with each of the one or more signals;
combining the plurality of signals subsequent the noise reduction to generate an output signal;
performing beamforming on the plurality of signals using:
the plurality of signals subsequent filtering by all-pass filters; and
an output of the non-coherent noise estimator to generate the output signal; and
performing artificial-intelligence (AI) noise reduction on the plurality of signals subsequent the beamforming to generate the output signal.

### Claim 14 (depends on 13)

The apparatus of claim 13, wherein, in performing the non-coherent noise reduction, the processor is configured to perform operations comprising:
individually estimating a respective non-coherent noise corresponding to each frequency band of a plurality of frequency bands of each channel of the plurality of channels; and
determining, for each frequency band of each channel, a respective gain control parameter to provide a plurality of gain control parameters each of which corresponding to a respective frequency band of a plurality of frequency bands of each channel of the plurality of channels such that the respective non-coherent noise associated with a first frequency band of a first channel of the plurality of channels which is worse than the respective non-coherent noise associated with a second frequency band of the first channel is suppressed.

### Claim 15 (depends on 13)

The apparatus of claim 13, wherein, in performing the non-coherent noise reduction, the processor is configured to perform the non-coherent noise reduction by using a deep learning model or machine learning.

### Claim 16 (depends on 13)

The apparatus of claim 13, wherein, in combining the plurality of signals, the processor is configured to filter the plurality of signals subsequent the noise reduction before combining the plurality of signals.

### Claim 17 (depends on 13)

The apparatus of claim 13, wherein:
the output signal comprises a mono-audio output signal in an event that a quantity of the plurality of audio sensors is two; and
the output signal comprises a stereo-audio output signal in an event that a quantity of the plurality of audio sensors is three or more.