Ambient noise root mean square (RMS) detector

• US 9,107,010 B2
• Filed: 02/08/2013
• Issued: 08/11/2015
• Est. Priority Date: 02/08/2013
• Status: Active Grant

• Current Assignee: Cirrus Logic Incorporated
• Original Assignee: Cirrus Logic Incorporated
• Inventors: Abdollahzadeh Milani, Ali
• Primary Examiner(s): Eason, Matthew
• Assistant Examiner(s): Nguyen, Sean H
• Application Number:
  US13/762,504
• Publication Number:
  US 20140226827A1
• Time in Patent Office: 914 Days
• Field of Search: 381/56, 381/57
• US Class Current: 1/1
• CPC Class Codes: G10K 11/17823 Reference signals, e.g. ambient acoustic environment
• CPC Class Codes: G10K 11/17837 by retaining part of the ambient acoustic environment, e.g. speech or alarm signals that the user needs to hear
• CPC Class Codes: G10K 11/1785 Methods, e.g. algorithms; Devices G10K11/1781, G10K11/1783 take precedence
• CPC Class Codes: G10K 11/17881 the reference signal being an acoustic signal, e.g. recorded with a microphone
• CPC Class Codes: G10K 2210/108 Communication systems, e.g. where useful sound is kept and noise is cancelled
• CPC Class Codes: G10K 2210/3023 Estimation of noise, e.g. on error signals
• CPC Class Codes: G10L 2021/02165 Two microphones, one receiving mainly the noise signal and the other one mainly the speech signal
• CPC Class Codes: G10L 21/0216 characterised by the method used for estimating noise
• CPC Class Codes: G10L 21/0224 Processing in the time domain
• CPC Class Codes: H04R 29/00 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 Class Codes: H04R 29/004 for microphones H04R29/007 takes precedence

Abstract

An RMS detector uses the concept of the k-NN (classifying using nearest neighbors)—algorithm in order to obtain RMS values. A rms detector using first-order regressor with a variable smoothing factor is modified to penalize samples from center of data in order to obtain RMS values. Samples which vary greatly from the background noise levels, such as speech, scratch, wind and other noise spikes, are dampened in the RMS calculation. When background noise changes, the system will track the changes in background noise and include the changes in the calculation of the corrected RMS value. A minimum tracker runs more often (e.g. two or three times) than the rate as in prior art detectors and methods, tracks the minimum rms value, which is to compute a normalized distance value, which in turn is used to normalize the smoothing factor. From this data, a corrected or revised RMS value is determined as the function of the previous RMS value multiplied by one minus the smoothing factor plus the smooth factor times the minimum rms value to output the corrected RMS for the present invention. The rms value is used to generate a reset signal for the minimum tracker and is used to avoid deadlock in the tracker, for example, when the background signal increases/decreases over time.

Claims

1. A root mean square (RMS) detector detecting an RMS level of a background noise input signal while being substantially immune to voice, wind, scratch sounds, and any spike noise, the RMS detector comprising:

a raw rms detector receiving a background noise input signal and outputting a raw rms value;

a minimum rms tracker receiving the raw rms value and tracking a minimum rms value of the raw rms value;

a normalized distance tracker receiving the minimum rms value and calculating a distance value between the minimum rms value and a previous corrected RMS value;

a normalized smoothing factor calculator normalizing a smoothing factor by dividing the smoothing factor by a maximum of the distance value or 1; and

an RMS value calculator determining a corrected RMS value from the minimum rms value, a previous corrected RMS value, and the normalized smoothing factor, and outputting a corrected RMS value.

2. The RMS detector of claim 1, further comprising

a reset generator receiving the raw rms value and generating a reset signal to the minimum rms tracker to reset the minimum rms tracker when the raw rms value changes in value over time to prevent the minimum rms tracker from locking up.

3. The RMS detector of claim 2, wherein the raw rms detector determines raw rms by adding a previous raw rms value to an input signal value.

4. The RMS detector of claim 3, wherein the absolute value of the input signal value is multiplied by a smoothing factor prior to being added to the previous raw rms value.

5. The RMS detector of claim 4, wherein the previous rms value is multiplied by one minus the smoothing factor prior to being added to the input signal value.

6. The RMS detector of claim 5 wherein the smoothing factor is selected from one of two predetermined values depending on whether the absolute value of the input signal is greater or less than the previous raw rms value.

7. The RMS detector of claim 2, where in the raw rms detector determines raw rms by:

8. The RMS detector of claim 2, wherein the minimum tracker determines a short-term minimum rms value by taking the minimum of the previous minimum rms value and the current raw rms value, and

for every 0.1 to 1 seconds, calculating a long-term minimum rms value as the minimum of a previous temporary minimum rms value and the present raw rms value to reset the detector, where the temporary rms value tracks background noise changes.

9. The RMS detector of claim 8, wherein the minimum tracker sets the temporary rms value to a current raw rms value and the minimum rms value to a minimum of a previous temporary rms value and the current raw rms value at every 0.1 to 1 seconds to more closely track the minimum rms value.

10. The RMS detector of claim 9, wherein the normalized distance is calculated by dividing the difference between the current raw rms value and the previous corrected RMS value by the previous corrected RMS value.

11. The RMS detector of claim 10, wherein the normalized smoothing factor is calculated by dividing a standard predetermined smoothing factor by the maxima of the normalized distance and one.

12. The RMS detector of claim 11, wherein the corrected RMS value output by the RMS detector is calculated by the sum of the normalized smoothing factor times the minimum rms value determined by the minimum rms value tracker and the product of the previous corrected RMS value times one minus the normalized smoothing factor.

13. The RMS detector of claim 2, wherein the minimum tracker determines the minimum rms value by taking the minimum of the previous minimum rms value and the current raw rms value

14. The RMS detector of claim 13, wherein the normalized distance d is calculated by:

15. The RMS detector of claim 14, wherein the normalized smoothing factor is calculated by:

16. The RMS detector of claim 15, wherein the corrected RMS value output by the RMS detector is calculated by:

RMS(l)=(1−

α

_d(l))·

RMS(l−

1)+α

_d(l)·

R_min(l)

where RMS(l) is the corrected RMS value, and RMS(l−

1) is a previous corrected RMS value, α

_d(l) represents the normalized smoothing factor for sample l, determined by the normalized smoother factor calculator, and R_min is the minimum rms value determined by the minimum rms value tracker.

17. In an RMS detector, a method of detecting RMS level of a background noise input signal while being substantially immune to voice, scratch, wind sounds, and any spike noise, the method comprising:

generating in an initial RMS detector receiving a background noise input signal, a raw rms value;

tracking in a minimum rms tracker receiving the raw rms value, a minimum rms value of the raw rms value;

calculating in a normalized distance tracker receiving the minimum rms value, a distance value between the minimum rms value and a previous corrected RMS value;

normalizing, in a normalized smoothing factor calculator, a smoothing factor by dividing the smoothing factor by a maximum of the distance value or 1; and

calculating in an RMS value calculator, a corrected RMS value by determining a corrected RMS value from the minimum rms value, a previous corrected RMS value, and the normalized smoothing factor.

18. The method of claim 17, further comprising:

generating in a reset generator receiving the raw rms value, a reset signal to the minimum rms tracker to reset the minimum rms tracker when the raw rms value changes in value over time to prevent the minimum rms tracker from locking up.

19. The method of claim 18, wherein the raw rms detector determines raw rms by adding a previous raw rms value to an input signal value.

20. The method of claim 19, wherein the absolute value of the input signal value is multiplied by a smoothing factor prior to being added to the previous raw rms value.

21. The method of claim 20, wherein the previous raw rms value is multiplied by one minus the smoothing factor prior to being added to the input signal value.

22. The method of claim 21, wherein the smoothing factor is selected from one of two predetermined values depending on whether the absolute value of the input signal is greater or less than the previous raw rms value.

23. The method of claim 18, where in the raw rms detector determines raw rms by:

24. The method of claim 18, wherein the minimum tracker determines a short-term minimum rms value by taking the minimum of the previous minimum rms value and the current raw rms value, and

for every 0.1 to 1 seconds, calculating a long-term minimum rms value as the minimum of a previous temporary minimum rms value and the present raw rms value to reset the detector, where the temporary rms value tracks background noise changes.

25. The method of claim 24, wherein the minimum tracker sets the temporary rms value to a current raw rms value and the minimum rms value to a minimum of a previous temporary rms value and the current raw rms value at every 0.1 to 1 seconds to more closely track the minimum rms value.

26. The method of claim 25, wherein the normalized distance is calculated by dividing the difference between the current raw rms value and the previous corrected RMS value by the previous corrected RMS value.

27. The method of claim 26, wherein the normalized smoothing factor is calculated by dividing a standard predetermined smoothing factor by the maxima of the normalized distance and one.

28. The method of claim 27, wherein the corrected RMS value output by the RMS detector is calculated by the sum of the normalized smoothing factor times the minimum rms value determined by the minimum rms value tracker, and the product of the previous corrected RMS value times one minus the normalized smoothing factor.

29. The method of claim 18, wherein the minimum tracker determines the minimum rms value by taking the minimum of the previous minimum rms value and the current raw rms value

30. The method of claim 29, wherein the normalized distance d is calculated by:

31. The RMS detector of claim 30, wherein the normalized smoothing factor is calculated by:

32. The RMS detector of claim 31, wherein the corrected RMS value output by the RMS detector is calculated by:

RMS(l)=(1−

α

_d(l))·

RMS(l−

1)+α

_d(l)·

R_min(l)

where RMS(l) is the corrected RMS value, and RMS(l−

1) is a previous corrected RMS value, α

_d(l) represents the normalized smoothing factor for sample l, determined by the normalized smoother factor calculator, and R_min is the minimum rms value determined by the minimum rms value tracker.