# US 10,504,537 B2 · Wind noise measurement

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- Number: US 10,504,537 B2
- Document ID: US10504537B2
- Status: Granted
- Kind: B2
- Application number: 15/887,019
- Filed: 2018-02-02
- Published: 2019-08-08
- Granted: 2019-12-10
- Earliest claimed: 2018-02-02
- Assignees: Cirrus Logic, Inc.
- Applicants: Cirrus Logic International Semiconductor Ltd.
- Inventors: Vitaliy Sapozhnykov; Thomas Ivan Harvey; Robert Luke
- Web page: https://patentgrep.com/patent/US10504537B2

## CPC

- G10L 21/0216: characterised by the method used for estimating noise
- G10L 21/0264: characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques
- H04R 1/1083: {Reduction of ambient noise (active noise reduction per se G10K11/175; protective devices for the ear, e.g. providing acoustic protection A61F11/06)}
- H04R 3/00: Circuits for transducers (arrangements for producing a reverberation or echo sound G10K15/08; amplifiers H03F)
- H04R 3/005: {for combining the signals of two or more microphones (specially adapted for hearing aids H04R25/407)}
- H04R 2410/07: Mechanical or electrical reduction of wind noise generated by wind passing a microphone
- H04R 2430/03: Synergistic effects of band splitting and sub-band processing
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- 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)

## Abstract

A device for measuring wind noise comprises at least a first microphone and a processor. A first signal and a second signal are obtained from the at least one microphone, the first and second signals reflecting a common acoustic input, and the first and second signals being at least one of temporally distinct and spatially distinct. The first signal is processed to determine a first distribution of the samples of the first signal. The second signal is processed to determine a second distribution of the samples of the second signal. From a difference between the first distribution and the second distribution a scalar non-binary metric reflecting an intensity of wind noise present in the first and second signals is derived, and output.

## Claims

### Claim 1

A device for measuring wind noise, the device comprising:
at least two microphones; and
a processor configured to:
based on a control signal, obtain a first signal and a second signal from the at least two microphones, the first and second signals reflecting a common acoustic input, and the first and second signals being selected, in response to the control signal, to be either first and second temporally distinct signals each obtained from the same one of the at least two microphones, or first and second spatially distinct signals obtained from two of the at least two microphones;
process the first signal to determine a first distribution of the samples of the first signal;
process the second signal to determine a second distribution of the samples of the second signal;
derive from a difference between the first distribution and the second distribution a scalar non-binary metric reflecting an intensity of wind noise present in the first and second signals; and
output the scalar metric.

### Claim 2 (depends on 1)

The device of claim 1 wherein the scalar metric reflecting the intensity of wind noise is a single scalar non-binary value.

### Claim 3 (depends on 2)

The device of claim 2 wherein the scalar metric reflecting the intensity of wind noise is expressed as a probability between 0 and 1, reflecting a probability of the presence of wind noise.

### Claim 4 (depends on 1)

The device of claim 1 wherein the scalar non-binary metric reflecting an intensity of wind noise comprises a plurality of measures respectively determined from distinct microphone signals.

### Claim 5 (depends on 4)

The device of claim 4 wherein at least some of the plurality of measures comprise scalar non-binary values.

### Claim 6 (depends on 1)

The device of claim 1 wherein the scalar metric reflecting the intensity of wind noise is a measure of wind noise power.

### Claim 7 (depends on 1)

The device of claim 1, wherein the processor is configured to execute at least one wind noise measurement cell configured to perform the steps of obtaining the first signal and the second signal, processing the first signal, processing the second signal, and deriving the difference between the first distribution and the second distribution.

### Claim 8 (depends on 7)

The device of claim 7 wherein the control signal is configured to exclude a particular microphone signal from the cell measurements at times when the respective microphone is occluded.

### Claim 9 (depends on 7)

The device of claim 7 wherein wind noise measures from at least two wind noise measurement cells are passed to a decision function module configured to produce a combined output measure from the individual wind noise measures.

### Claim 10 (depends on 1)

The device of claim 1 wherein the first and second signals are made to be temporally distinct by taking temporally distinct samples of a single microphone signal.

### Claim 11 (depends on 1)

The device of claim 1 wherein the first and second signals are made to be spatially distinct by taking the first signal from a first microphone and taking the second signal from a second microphone spaced apart from the first microphone.

### Claim 12 (depends on 1)

The device of claim 1 configured to derive, for each sub-band of a plurality of sub-bands, a scalar non-binary metric reflecting an intensity of wind noise present in the first and second signals in that sub-band.

### Claim 13 (depends on 12)

The device of claim 12 configured to measure wind noise first in respect of a lower frequency sub-band, and to only measure wind noise in respect of a higher frequency sub-band if non-negligible wind noise is measured in the lower frequency sub-band.

### Claim 14 (depends on 12)

The device of claim 12, further configured to apply wind noise reduction only in each sub-band in which the measurement of wind noise is greater than a respective sub-band threshold.

### Claim 15 (depends on 1)

The device of claim 1, configured to calculate the difference between the first distribution and the second distribution and to copy the output of the calculation to more than one wind noise measurement block.

### Claim 16 (depends on 9)

The device of claim 9 wherein the decision function module is configured to produce the combined output measure as a scalar metric from the individual wind noise measures by applying a neural network.

### Claim 17 (depends on 9)

The device of claim 9 wherein the decision function module is configured to produce the combined output measure as a scalar metric from the individual wind noise measures by applying a hidden Markov model.

### Claim 18 (depends on 9)

The device of claim 9 wherein the decision function module is configured to produce the combined output measure as a binary metric from the individual wind noise measures by applying a truth table.

### Claim 19 (depends on 1)

The device of claim 1, comprising at least one of a telephony headset or handset, a still camera, a video camera, a tablet computer, a cochlear implant or a hearing aid.

### Claim 20

A non-transitory computer readable medium comprising computer program code means to make a computer execute a procedure for wind noise measurement, the computer program product comprising:
computer program code means for, based on a control signal, obtaining a first signal and a second signal from at least two microphones, the first and second signals reflecting a common acoustic input, and the first and second signals being selected, in response to the control signal, to be either first and second temporally distinct signals each obtained from the same one of the at least two microphones, or first and second spatially distinct signals obtained from two of the at least two microphones;
computer program code means for processing the first signal to determine a first distribution of the samples of the first signal;
computer program code means for processing the second signal to determine a second distribution of the samples of the second signal;
computer program code means for deriving from a difference between the first distribution and the second distribution a scalar non-binary metric reflecting an intensity of wind noise present in the first and second signals; and
computer program code means for outputting the scalar metric.

### Claim 21

A method for measuring wind noise, the method comprising:
based on a control signal, obtaining a first signal and a second signal from at least two microphones, the first and second signals reflecting a common acoustic input, and the first and second signals being selected, in response to the control signal, to be either first and second temporally distinct signals obtained from the same one of the at least two microphones, or first and second spatially distinct signals obtained from two of the at least two microphones;
processing the first signal to determine a first distribution of the samples of the first signal;
processing the second signal to determine a second distribution of the samples of the second signal;
deriving from a difference between the first distribution and the second distribution a scalar non-binary metric reflecting an intensity of wind noise present in the first and second signals; and
outputting the scalar metric.

### Claim 22 (depends on 21)

The method of claim 21 wherein the scalar metric reflecting the intensity of wind noise is a single scalar non-binary value.

### Claim 23 (depends on 22)

The method of claim 22 wherein the scalar metric reflecting the intensity of wind noise is expressed as a probability between 0 and 1, reflecting a probability of the presence of wind noise.

### Claim 24 (depends on 21)

The method of claim 21 wherein the scalar non-binary metric reflecting an intensity of wind noise comprises a plurality of measures respectively determined from distinct microphone signals.

### Claim 25 (depends on 24)

The method of claim 24 wherein at least some of the plurality of measures comprise scalar non-binary values.

### Claim 26 (depends on 21)

The method of claim 21 wherein the scalar metric reflecting the intensity of wind noise is a measure of wind noise power.

### Claim 27 (depends on 21)

The method of claim 21, wherein the steps of obtaining the first signal and the second signal, processing the first signal, processing the second signal, and deriving the difference between the first distribution and the second distribution are performed by at least one wind noise measurement cell.

### Claim 28 (depends on 27)

The method of claim 27 wherein the controlling is configured to exclude a particular microphone signal from the cell measurements at times when the respective microphone is occluded.

### Claim 29 (depends on 27)

The method of claim 27 comprising passing wind noise measures from at least two wind noise measurement cells to a decision function module, and the decision function module producing a combined output measure from the individual wind noise measures.

### Claim 30 (depends on 21)

The method of claim 21 configured to derive, for each sub-band of a plurality of sub-bands, a scalar non-binary metric reflecting an intensity of wind noise present in the first and second signals in that sub-band.

### Claim 31 (depends on 21)

The method of claim 21, comprising calculating the difference between the first distribution and the second distribution and copying the output of the calculation to more than one wind noise measurement block.

### Claim 32 (depends on 29)

The method of claim 29 wherein producing the combined output measure as a scalar metric from the individual wind noise measures comprises applying a neural network.

### Claim 33 (depends on 29)

The method of claim 29 wherein producing the combined output measure as a scalar metric from the individual wind noise measures comprises applying a hidden Markov model.

### Claim 34 (depends on 29)

The method of claim 29 wherein producing the combined output measure as a binary metric from the individual wind noise measures comprises applying a truth table.