SPATIAL ADAPTATION IN MULTI-MICROPHONE SOUND CAPTURE

• US 20170078791A1
• Filed: 11/23/2016
• Published: 03/16/2017
• Est. Priority Date: 02/10/2011
• Status: Active Grant

• Current Assignee: Dolby International AB (Dolby Laboratories Incorporated)
• Original Assignee: Dolby International AB (Dolby Laboratories Incorporated)
• Inventors: SAMUELSSON, Leif Jonas
• Granted Patent: US 10,154,342 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G10L 21/0232 Processing in the frequency domain
• CPC Class Codes: 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
• CPC Class Codes: H04R 29/006 Microphone matching
• CPC Class Codes: H04R 3/005 for combining the signals of two or more microphones specially adapted for hearing aids H04R25/407
• CPC Class Codes: H04R 3/04 for correcting frequency response

Abstract

A spatial adaptation system for multiple-microphone sound capture systems and methods thereof are described. A spatial adaptation system includes an inference and weight module configured to receive a inputs. The inputs based on two or more input signals captured by at least two microphones. The inference and weight module to determine one or more weight values base on at least one of the inputs. The spatial adaptation system also including a noise magnitude ratio update module coupled with the inference and weight module. The noise magnitude ratio update module to determine an updated noise target based on the one or more weight values from the inference and weight module.

Claims

1. A spatial adaptation method for providing long term symmetry among a plurality of microphones, comprising the steps of:

calculating a magnitude ratio (MR) by computing the ratio between a first energy representing first group of one or more near-field microphones and a second energy representing a second group of one more far-field microphones,

wherein the spatial adaptation method provides self-calibration for microphone matching of large variations without any situational assumptions to lower manufacturing cost and complexities.

2. The method of claim 1, further comprising the step of:

estimating the calibration of the one or more microphone during real-time use of a device.

3. The method of claim 1, wherein the step of calculating the magnitude ratio further comprising:

calculating a minima follower and a maxim followers configured to track the minimum and maximum magnitude ratios over time, applied separately and independently in each frequency band.

4. The method of claim 1, wherein the spatial adaptation employs a Gaussian mixture model (GMM) based inference that is optimized for a source or voice dominated signal (clean voice or speech), and one model is optimized for noise dominated signals (noise) to classify a desired source.

5. The method of claim 1, wherein the spatial adaptation is not performed if self noise is detected.

6. The method of claim 5, wherein the method of detecting microphone self noise is implemented in each decision band.

7. The method of claim 5, wherein the self noise detection is based on aggregated features aggregated across frequencies.

8. The method of claim 7, wherein the aggregated features is selected from the group comprising:

a scalar aggregated of frame power;

a scalar aggregate of phase differences;

or

a scalar aggregate of coherences.

9. The method of claim 5, wherein if self noise is detected if one or more of the following conditions are met:

if the scalar aggregated of frame power is less than a first predefined threshold;

or

if the scalar aggregated of frame power is less than a second predefined threshold that is greater than the first predefined threshold and the scalar aggregated phase is greater than a third predefined threshold and the scalar aggregated coherence is less than a fourth predefined threshold.

10. The method of claim 1, wherein the spatial adaptation method determines a maximum magnitude ratio to protect against interfering sources by comparing the magnitude ratio of a current frame with a threshold derived from an estimate of maximum ratio that is produced by a near-field talker.

11. The method of claim 3, further comprising the step of processing an output of a minima and maxima search configured to smooth or compensation for a minimum bias or a maximum bias.

12. The method of claim 4, wherein the GMM, for each frequency band is optimized offline to model distributions of MR and a maximum ratio subtracted from a maximum ratio minimum from near-field and far-field training data.

13. The method of claim 1, wherein the spatial adaptation method is based on an estimation of a ratio between the energies of a rear and front signal instead of a ratio between a front and a rear signal.

14. The method of claim 6, wherein the decision bands include information representing a level of interference and a decision to update a noise target or not in the frequency band and are not the same as the bands where features are determine.

15. The method of claim 14, wherein the noise target is a long-term average of a magnitude ratio for noise and is used to modify one or more received signals and is employed to match those signals.

16. The method of claim 1, wherein the spatial adaptation method maintains a variable to determine when to update one or more noise targets.

17. The method of claim 4, wherein if speech is found in a decision band, a noise target in one or more feature bands are associated with that decision band and is updated using a different set of weights than when noise is found in the decision band.

18. The method of claim 4, where the GMM is optimized offline on features selected from the group comprising the following features;

a magnitude ratio feature;

a phase difference feature;

a frame power feature;

a coherence feature;

or

a delta magnitude ratio feature;

a delta phase difference feature;

a delta frame power feature;

or

a delta coherence feature.

19. A non-transitory computer readable storage medium, storing software instructions, which when executed by one or more processors cause performance of the method as recited in claim 1.

20. A computing device comprising one or more processors and one or more storage media storing a set of instructions which, when executed by the one or more processors, cause performance of the method as recited in claim 1.