Distortion sensing, prevention, and distortion-aware bass enhancement
Systems and methods to provide distortion sensing, prevention, and/or distortion-aware bass enhancement in audio systems can be implemented in a variety of applications. Sensing circuitry can generate statistics based on an input signal received for which an acoustic output is generated. In various embodiments, the statistics can be used such that a multi-notch filter can be used to provide input to a speaker to generate the acoustic output. In various embodiments, the statistics from the sensing circuitry can be provided to a bass parameter controller coupled to bass enhancement circuitry to operatively provide parameters to the bass enhancement circuitry. The bass enhancement circuitry can provide a bass enhanced signal for generation of the acoustic output, based on the parameters. Various combinations of a multi-notch filter and bass enhancement circuitry using statistics from sensing circuitry can be implemented to provide an enhanced acoustic output. Additional apparatus, systems, and methods are disclosed.
1. A system comprising:
a multi-notch filter arranged to receive a signal to generate an acoustic output;
sensing circuitry coupled to receive a filtered signal corresponding to the received signal and processed by the multi-notch filter, or corresponding to the acoustic output, and to generate statistics on the filtered signal;
a dynamic gain cell coupled to receive the statistics from the sensing circuitry and coupled to the multi-notch filter to operatively modify a depth of the multi-notch filter, the dynamic gain cell having inputs to receive a measure of bandpass energy and a measure of signal energy from the sensing circuitry and having a threshold parameter of distortion activating energy at which the multi-notch filter begins to engage and a sensitivity parameter such that operative modification of the depth includes operatively modifying the depth greater than a difference between the distortion activating energy and the threshold parameter; and
a speaker to receive the filtered signal to generate the acoustic output.
2. A system comprising:
a multi-notch filter arranged to receive a signal to generate an acoustic output;
sensing circuitry coupled to receive a filtered signal corresponding to the received signal and processed by the multi-notch filter, or corresponding to the acoustic output, and to generate statistics on the filtered signal;
a dynamic gain cell coupled to receive the statistics from the sensing circuitry and coupled to the multi-notch filter to operatively modify a depth of the multi-notch filter; and
a speaker to receive the filtered signal to generate the acoustic output, wherein the multi-notch filter, the sensing circuitry, and the dynamic gain cell are disposed in a digital signal processor.
3. The system of claim 1 , wherein the sensing circuitry is operable to generate statistics that measure a likelihood of distortion at an output of the speaker.
4. A system comprising:
a multi-notch filter arranged to receive a signal to generate an acoustic output;
sensing circuitry coupled to receive a filtered signal corresponding to the received signal and, processed by the multi-notch filter or corresponding to the acoustic output, and to generate statistics on the filtered signal, wherein the sensing circuitry is operable to generate statistics that measure a likelihood of distortion at an output of the speaker;
a dynamic gain cell coupled to receive the statistics from the sensing circuitry and coupled to the multi-notch filter to operatively modify a depth of the multi-notch filter; and
a speaker to receive the filtered signal to generate the acoustic output, wherein the sensing circuitry is operable to compute the measure of the likelihood of distortion at the output of the speaker using a technique selected from a group including machine learning, statistical learning, predictive learning, and artificial intelligence.
5. A system comprising:
a multi-notch filter arranged to receive a signal to generate an acoustic output;
sensing circuitry coupled to receive a filtered signal corresponding to the received signal and processed by the multi-notch filter, or corresponding to the acoustic output, and to generate statistics on the filtered signal, the sensing circuitry is operable to generate statistics that measure a likelihood of distortion at an output of the speaker;
a dynamic gain cell coupled to receive the statistics from the sensing circuitry and coupled to the multi-notch filter to operatively modify a depth of the multi-notch filter; and
a speaker to receive the filtered signal to generate the acoustic output, wherein the sensing circuitry is operable to compute a binary indicator of the distortion at the output of the speaker using a technique selected from a group including machine learning, statistical learning, predictive learning, or artificial intelligence.
6. A system comprising:
a multi-notch filter arranged to receive a signal to generate an acoustic output;
sensing circuitry coupled to receive a filtered signal corresponding to the received signal and processed by the multi-notch filter, or corresponding to the acoustic output, and to generate statistics on the filtered signal;
a dynamic gain cell coupled to receive the statistics from the sensing circuitry and coupled to the multi-notch filter to operatively modify a depth of the multi-notch filter; and
a speaker to receive the filtered signal to generate the acoustic output, wherein the sensing circuitry includes multiple infinite impulse response (IIR) or finite-impulse response (FIR) bandpass filters, and/or dynamic spectrum analysis filters, tuned to frequencies determined to contribute to distortion for the speaker and includes circuitry to estimate amount of distortion activating energy in a filtered signal from the multiple IIR or FIR bandpass filters, and/or dynamic spectrum analysis filters, as one or more statistics of energy across all bands of the multiple IIR or FIR bandpass filters and/or dynamic spectrum analysis filters, and includes circuitry to measure total energy of the filtered signal.
7. The system of claim 6 , wherein the dynamic gain cell is arranged to provide a gain to adjust the depth of the multi-notch filter based on the one or more statistics.
8. A method comprising:
applying a multi-notch filter to a signal, the signal provided to generate an acoustic output;
receiving a filtered signal, corresponding to the signal received and processed by the multi-notch filter or corresponding to the acoustic output, at sensing circuitry and generating statistics on the filtered signal using the sensing circuitry;
modifying depth of the multi-notch filter based on the statistics, using a dynamic gain cell coupled to receive the statistics, including a measure of bandpass energy and a measure of signal energy, from the sensing circuitry, the dynamic gain cell having a threshold parameter of distortion activating energy at which the multi-notch filter begins to engage and a sensitivity parameter such that modifying depth includes operatively modifying the depth greater than a difference between the distortion activating energy and the threshold parameter; and
receiving the filtered signal at a speaker and generating the acoustic output from the speaker.
9. The method of claim 8 , wherein receiving a filtered signal processed by the multi-notch filter, generating statistics on the filtered signal, and modifying the depth of the multi-notch filter are implemented in a digital signal processor.
10. The method of claim 8 , wherein generating statistics includes generating statistics that measure a likelihood of distortion at an output of the speaker.
11. A method comprising:
applying a multi-notch filter to a signal, the signal provided to generate an acoustic output;
receiving a filtered signal, corresponding to the signal received and processed by the multi-notch filter or corresponding to the acoustic output, at sensing circuitry and generating statistics on the filtered signal using the sensing circuitry, wherein generating statistics includes generating statistics that measure a likelihood of distortion at an output of the speaker,
modifying depth of the multi-notch filter based on the statistics, using a dynamic gain cell coupled to receive the statistics from the sensing circuitry; and
receiving the filtered signal at a speaker and generating the acoustic output from the speaker, wherein the method includes computing, in the sensing circuitry, the measure of the likelihood of distortion at the output of the speaker using a technique selected from a group including machine learning, statistical learning, predictive learning, and artificial intelligence.
12. A method comprising:
applying a multi-notch filter to a signal, the signal provided to generate an acoustic output;
receiving a filtered signal, corresponding to the signal received and processed by the multi-notch filter or corresponding to the acoustic output, at sensing circuitry and generating statistics on the filtered signal using the sensing circuitry;
modifying depth of the multi-notch filter based on the statistics, using a dynamic gain cell coupled to receive the statistics from the sensing circuitry; and
receiving the filtered signal at a speaker and generating the acoustic output from the speaker, wherein the method includes computing, in the sensing circuitry, a binary indicator of the distortion at the output of the speaker using a technique selected from a group including machine learning, statistical learning, predictive learning, or artificial intelligence.
13. A method comprising:
applying a multi-notch filter to a signal, the signal provided to generate an acoustic output;
receiving a filtered signal, corresponding to the signal received and processed by the multi-notch filter or corresponding to the acoustic output, at sensing circuitry and generating statistics on the filtered signal using the sensing circuit;
modifying depth of the multi-notch filter based on the statistics, using a dynamic gain cell coupled to receive the statistics from the sensing circuitry; and
receiving the filtered signal at a speaker and generating the acoustic output from the speaker, wherein generating statistics using the sensing circuitry includes using sensing circuitry having multiple infinite impulse response (IIR) or finite-impulse response (FIR) bandpass filters, and/or dynamic spectrum analysis filters, tuned to frequencies determined to contribute to distortion for the speaker, estimating an amount of distortion activating energy in a filtered signal from the multiple IIR bandpass filters and/or dynamic spectrum analysis filters as one or more statistics of energy across all bands of the multiple IIR or FIR bandpass filters and/or dynamic spectrum analysis filters, and measuring total energy of the filtered signal.
14. The method of claim 13 , wherein using the dynamic gain cell includes providing a gain to adjust the depth of the multi-notch filter based on the one or more statistics.