Noise Estimation Using Coherence

US2020186115A1 · US · A1

Patent metadata
FieldValue
Publication numberUS-2020186115-A1
Application numberUS-202016794765-A
CountryUS
Kind codeA1
Filing dateFeb 19, 2020
Priority dateOct 26, 2017
Publication dateJun 11, 2020
Grant date

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Abstract

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The technology described herein can be embodied in a method for estimating a power spectral density of noise, the method including receiving an input signal representing audio captured using a microphone. The input signal includes a first portion that represents acoustic outputs from two or more audio sources, and a second portion that represents a noise component. The method also includes iteratively modifying a frequency domain representation of the input signal, such that the modified frequency domain representation represents a portion of the input signal in which effects due to the first portion are substantially reduced. The method further includes determining, from the modified frequency domain representation, an estimate of a power spectral density of the noise, and generating a control signal configured to adjust one or more gains of an acoustic transducer. The control signal is generated based on the estimate of the power spectral density of the noise.

First claim

Opening claim text (preview).

What is claimed is: 1 . A method for estimating a power spectral density of noise, the method comprising: receiving, at one or more processing devices, an input signal having a first portion that includes acoustic outputs from two or more audio sources; iteratively modifying, by the one or more processing devices, a frequency domain representation of the input signal, such that the modified frequency domain representation represents a portion of the input signal in which effects due to the first portion are substantially reduced; determining, from the modified frequency domain representation, an estimate of a power spectral density of a noise portion of the input signal; and adjusting a gain of an acoustic transducer based on the estimate of the power spectral density of the noise. 2 . The method of claim 1 , wherein the frequency domain representation includes, for each frequency bin: (i) values that each represent a level of coherence between acoustic outputs from a pair of the two or more audio sources, (ii) values that each represent a level of coherence between an acoustic output of a particular audio source of the two or more audio sources and the input signal, and (iii) values that each represent the power of the acoustic output for the particular frequency bin, of an individual audio source of the two or more audio sources. 3 . The method of claim 2 , wherein, (i) the values that each represent a level of coherence between acoustic outputs from a pair of the two or more audio sources include one value for every permutation of pairs of the two or more audio sources, (ii) the values that each represent a level of coherence between an acoustic output of a particular audio source of the two or more audio sources and the input signal include two values for each of the two or more audio sources, and (iii) the values that each represent the power of the acoustic output for the particular frequency bin, of an individual audio source of the two or more audio sources include one value for each of the two or more audio sources. 4 . The method of claim 1 , wherein the gain of the acoustic transducer is adjusted to increase with an increase in the estimate of the power spectral density of the noise, and decrease with a decrease in the estimate of the power spectral density. 5 . The method of claim 1 , wherein the frequency domain representation comprises a cross-spectral density matrix computed based on outputs of the two or more audio sources. 6 . The method of claim 5 , wherein iteratively modifying the frequency domain representation comprises executing a matrix diagonalization process on the cross-spectral density matrix. 7 . A system comprising: a noise analysis engine comprising one or more processing devices, the noise analysis engine configured to: receive an input signal having a first portion that represents acoustic outputs from two or more audio sources; iteratively modify a frequency domain representation of the input signal, such that the modified frequency domain representation represents a portion of the input signal in which effects due to the first portion are substantially reduced; determine, from the modified frequency domain representation, an estimate of a power spectral density of a noise portion of the input signal; and adjust a gain of an acoustic transducer based on the estimate of the power spectral density of the noise. 8 . The system of claim 7 , wherein the frequency domain representation includes, for each frequency bin: (i) values that each represent a level of coherence between acoustic outputs from a pair of the two or more audio sources, (ii) values that each represent a level of coherence between an acoustic output of a particular audio source of the two or more audio sources and the input signal, and (iii) values that each represent the power of the acoustic output for the particular frequency bin, of an individual audio source of the two or more audio sources. 9 . The system of claim 8 , wherein, (i) the values that each represent a level of coherence between acoustic outputs from a pair of the two or more audio sources include one value for every permutation of pairs of the two or more audio sources, (ii) the values that each represent a level of coherence between an acoustic output of a particular audio source of the two or more audio sources and the input signal include two values for each of the two or more audio sources, and (iii) the values that each represent the power of the acoustic output for the particular frequency bin, of an individual audio source of the two or more audio sources include one value for each of the two or more audio sources. 10 . The system of claim 7 , wherein the gain of the acoustic transducer is adjusted to increase with an increase in the estimate of the power spectral density of the noise, and decrease with a decrease in the estimate of the power spectral density. 11 . The system of claim 7 , wherein the frequency domain representation comprises a cross-spectral density matrix computed based on outputs of the two or more audio sources. 12 . The system of claim 11 , wherein iteratively modifying the frequency domain representation comprises executing a matrix diagonalization process on the cross-spectral density matrix. 13 . One or more machine-readable storage devices having encoded thereon computer readable instructions for causing one or more processing devices to perform operations comprising: receiving an input signal having a first portion that represents acoustic outputs from two or more audio sources; iteratively modifying a frequency domain representation of the input signal, such that the modified frequency domain representation represents a portion of the input signal in which effects due to the first portion are substantially reduced; determining, from the modified frequency domain representation, an estimate of a power spectral density of a noise portion of the input signal; and adjusting a gain of an acoustic transducer based on the estimate of the power spectral density of the noise. 14 . The one or more machine-readable storage devices of claim 13 , wherein the frequency domain representation includes, for each frequency bin: (i) values that each represent a level of coherence between acoustic outputs from a pair of the two or more audio sources, (ii) values that each represent a level of coherence between an acoustic output of a particular audio source of the two or more audio sources and the input signal, and (iii) values that each represent the power of the acoustic output for the particular frequency bin, of an individual audio source of the two or more audio sources. 15 . The one or more machine-readable storage devices of claim 14 , wherein, (i) the values that each represent a level of coherence between acoustic outputs from a pair of the two or more audio sources include one value for every permutation of pairs of the two or more audio sources, (ii) the values that each represent a level of coherence between an acoustic output of a particular audio source of the two or more audio sources and the input signal include two values for each of the two or more audio sources, and (iii) the values that each represent the power of the acoustic output for the particular frequency bin, of an individual audio source of the two or more audio sources include one value for each of the two or more audio sources. 16 . The one or more machine-readable storage devices of claim 13 , wherein the gain of the acoustic transducer is adjusted to increase with a

Assignees

Inventors

Classifications

  • Automatic adjustment · CPC title

  • H03G3/32Primary

    the control being dependent upon ambient noise level or sound level · CPC title

  • frequency-dependent volume compression or expansion, e.g. multiple-band systems (H03G9/10, H03G9/18 take precedence) · CPC title

  • Equalizers; Volume or gain control in limited frequency bands · CPC title

  • Monitoring arrangements; Testing arrangements {(for hearing aids H04R25/30; detection of loudspeaker connection H04R5/04; sound-field adaptation dependent on speaker detection H04S7/308)} · CPC title

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What does patent US2020186115A1 cover?
The technology described herein can be embodied in a method for estimating a power spectral density of noise, the method including receiving an input signal representing audio captured using a microphone. The input signal includes a first portion that represents acoustic outputs from two or more audio sources, and a second portion that represents a noise component. The method also includes iter…
Who is the assignee on this patent?
Bose Corp
What technology area does this patent fall under?
Primary CPC classification H03G3/32. Mapped technology areas include Electricity.
When was this patent published?
Publication date Thu Jun 11 2020 00:00:00 GMT+0000 (Coordinated Universal Time) (A1). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).