# US 11,232,778 B1 · Systems and methods for detecting divergence in an adaptive system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,232,778 B1 - Document ID: US11232778B1 - Status: Granted - Kind: B1 - Application number: 16/935,979 - Filed: 2020-07-22 - Published: 2022-01-27 - Granted: 2022-01-25 - Earliest claimed: 2020-07-22 - Assignees: Bose Corporation - Applicants: Bose Corporation - Inventors: Siamak Farahbakhsh - Web page: https://patentgrep.com/patent/US11232778B1 ## CPC - G10K 11/17835: {using detection of abnormal input signals} - G10K 11/17854: {the filter being an adaptive filter} - G10K 11/17825: {Error signals} - G10K 11/17875: {using an error signal without a reference signal, e.g. pure feedback} - G10K 11/17879: {using both a reference signal and an error signal} - G10K 2210/12821: Rolling noise; Wind and body noise - G10K 2210/3023: Estimation of noise, e.g. on error signals - G10K 2210/3025: Determination of spectrum characteristics, e.g. FFT - G10K 2210/3026: Feedback - G10K 2210/3028: Filtering, e.g. Kalman filters or special analogue or digital filters - G10K 2210/3226: Sensor details, e.g. for producing a reference or error signal - G10K: SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR ## Abstract Detecting a divergence in an adaptive system includes the steps of determining a power of a component of an error signal at a first frequency, the component being correlated to a noise-cancellation signal, the noise-cancellation signal being produced by an adaptive filter and being configured to cancel noise within a predetermined volume when transduced into acoustic signal, wherein the error signal represents a magnitude of a residual noise within the predetermined volume; determining a time gradient of the power of the component of the error signal; and comparing a metric to a threshold, wherein the metric is based, at least in part, on a value of the time gradient of the power of the component of the error signal over a period of time. ## Claims ### Claim 1 A non-transitory storage medium storing program code for detecting divergence or instability in a noise-cancellation system, the program code, being executed by a processor, comprising the steps of: determining a power of a component of an error signal at a first frequency, the component being correlated to a noise-cancellation signal, the noise-cancellation signal being produced by an adaptive filter and being configured to cancel noise within a predetermined volume when transduced into acoustic signal, wherein the error signal represents a magnitude of a residual noise within the predetermined volume; determining a time gradient of the power of the component of the error signal; and comparing a metric to a threshold, wherein the metric is based, at least in part, on a value of the time gradient of the power of the component of the error signal over a period of time. ### Claim 2 (depends on 1) The non-transitory storage medium of claim 1, wherein the program code further comprises the step of: transitioning a first set of coefficients of the adaptive filter to a second set of coefficients of the adaptive filter upon determining that the metric exceeds the threshold. ### Claim 3 (depends on 2) The non-transitory storage medium of claim 2, wherein the program code further comprises the step of slowing a rate of adaptation of the adaptive filter if the power of the component begins decreasing as a result of transitioning the first set of coefficients to the second set of coefficients. ### Claim 4 (depends on 1) The non-transitory storage medium of claim 1, wherein the program code further comprises the step of: transitioning to a third set of coefficients of the adaptive filter upon determining that the metric exceeds the threshold within a second period of time of storing a second set of coefficients. ### Claim 5 (depends on 1) The non-transitory storage medium of claim 1, wherein the metric is a filtered representation of the time gradient over the period of time, wherein the representation of the time gradient is filtered with a low-pass filter. ### Claim 6 (depends on 5) The non-transitory storage medium of claim 5, wherein a cut-off frequency of the low-pass filter is selected according the first frequency. ### Claim 7 (depends on 1) The non-transitory storage medium of claim 1, wherein the program code further comprises the steps of: comparing a second metric to a second threshold, wherein the second metric is based on a comparison of the power of the component of the error signal at the first frequency to the power of the component of the error signal at at least a second frequency. ### Claim 8 (depends on 7) The non-transitory storage medium of claim 7, wherein the program code further comprises the step of: transitioning a first set of coefficients of the adaptive filter to a second set of coefficients of the adaptive filter, upon determining that either the metric exceeds the threshold or the second metric exceeds the second threshold. ### Claim 9 (depends on 8) The non-transitory storage medium of claim 8, wherein the program code further comprises the step of slowing a rate of adaptation of the adaptive filter if the power of the correlated component begins decreasing as a result of transitioning the first set of coefficients to the second set of coefficients. ### Claim 10 (depends on 7) The non-transitory storage medium of claim 7, wherein the second metric is a filtered representation of a relative power of the component of the error signal at the first frequency and the component of the error signal at the second frequency, wherein the representation of the relative power is filtered with a low-pass filter. ### Claim 11 A method for detecting divergence in a noise-cancellation system, comprising: determining a power of a component of an error signal at a first frequency, the component being correlated to a noise-cancellation signal, the noise-cancellation signal being produced by an adaptive filter and being configured to cancel noise within a predetermined volume when transduced into acoustic signal, wherein the error signal represents a magnitude of a residual noise within the predetermined volume; determining a time gradient of the power of the component of the error signal; and comparing a metric to a threshold, wherein the metric is based, at least in part, on a value of the time gradient of the power of the component of the error signal over a period of time. ### Claim 12 (depends on 11) The method of claim 11, further comprising the step of: transitioning a first set of coefficients of the adaptive filter to a second set of coefficients of the adaptive filter upon determining that the metric exceeds the threshold. ### Claim 13 (depends on 12) The method of claim 12, further comprising the step of: slowing a rate of adaptation of the adaptive filter if the power of the component begins decreasing as a result of transitioning the first set of coefficients to the second set of coefficients. ### Claim 14 (depends on 12) The method of claim 12, further comprising the step of: transitioning to a third set of coefficients of the adaptive filter upon determining that the metric exceeds the threshold within a second period of time of storing a second set of coefficients. ### Claim 15 (depends on 11) The method of claim 11, wherein the metric is a filtered representation of the time gradient over the period of time, wherein the representation of the time gradient is filtered with a low-pass filter. ### Claim 16 (depends on 15) The method of claim 15, wherein a cut-off frequency of the low-pass filter is selected according the first frequency. ### Claim 17 (depends on 11) The method of claim 11, further comprising the step of: comparing a second metric to a second threshold, wherein the second metric is based on a comparison of the power of the component of the error signal at the first frequency to the power of the component of the error signal at at least a second frequency. ### Claim 18 (depends on 17) The method of claim 17, further comprising the step of: transitioning a first set of coefficients of the adaptive filter to a second set of coefficients of the adaptive filter upon determining that either the metric exceeds the threshold or the second metric exceeds the second threshold. ### Claim 19 (depends on 18) The method of claim 18, further comprising the step of: slowing a rate of adaptation of the adaptive filter if the power of the correlated component begins decreasing as a result of transitioning the first set of coefficients to the second set of coefficients. ### Claim 20 (depends on 17) The method of claim 17, wherein the second metric is a filtered representation of a relative power of the component of the error signal at the first frequency and the component of the error signal at the second frequency, wherein the representation of the relative power is filtered with a low-pass filter.