Active noise equalization
View Patent ↗An active noise equalization (ANE) system may be run on the existing audio/infotainment system as a software library. The ANE system may share components (e.g., microphones and sensors) with other audio applications. Some ANEs include a complex-domain formulation of a multiple-frequency multiple-channel ANE that requires less memory and processing requirements. The complex-domain system replaces the multiplication of multiple real gains with multiple real signals with a single complex multiplication operation.
1. A method for active noise equalization comprising:
obtaining a sync signal synchronized to an engine speed from a noise source reproduced into an acoustic space;
generating a noise model, representing one or more complex tones, responsive to the sync signal;
receiving an audio signal representing a sound field in the acoustic space;
applying an estimate of a secondary path transfer function to the noise model in a frequency domain; and
applying a transformation function to the noise model where the transform function is responsive to reducing a sum of an output of the transformation function and the received audio signal;
where applying the estimate of the secondary path transfer function comprises an online calculation; and
where an output of the secondary path transfer function is limited to a range of frequencies that lies within an aural range.
2. The method for active noise equalization of claim 1 , where the synchronized engine speed is generated from a tachometer.
3. The method for active noise equalization of claim 1 , where the synchronized engine speed is generated from an engine control unit.
4. The method for active noise equalization of claim 1 , where applying the estimate of the secondary path transfer function to the noise model comprises calculating a plurality of frequency components.
5. The method for active noise equalization of claim 4 , where the estimate of the secondary path transfer function for the plurality frequency components is stored in a non-transitory media.
6. The method for active noise equalization of claim 1 , where the transformation function comprises an adaptive filtering algorithm.
7. The method for active noise equalization of claim 6 , where the adaptive filtering algorithm comprises one of a least-mean-square, a normalized least-mean-square, an affine projection or a recursive least-square.
8. A head-unit system for active noise equalization comprising:
one or more processors in the head-unit; and
memory storing instructions accessible by the one or more processors, the instructions, when executed by the one or more processors, configuring the system to:
obtain a sync signal synchronized to an engine speed associated with a noise source reproduced in an acoustic space;
generate a noise model representing one or more complex tones responsive to the sync signal;
receive an audio signal representing a sound field in the acoustic space;
apply an estimate of a secondary path transfer function to the noise model in a frequency domain; and
apply a transformation function to the noise model where the transform function is based on the noise model and is responsive to reducing a sum of an output of the transformation function and the received audio signal
where the one or more processors apply the estimate of the secondary path transfer function through an online calculation; and
where an output of the secondary path transfer function is limited to a range of frequencies that lies within an aural range.
9. The head-unit system of claim 8 , where the synchronized engine speed is generated from a tachometer.
10. The head-unit system of claim 8 , where the synchronized engine speed is generated from an engine control unit.
11. The head-unit system of claim 8 , where applying the estimate of the secondary path transfer function to the noise model comprises calculating a plurality of frequency components.
12. The head-unit system of claim 11 , where the estimate of the secondary path transfer function for the plurality frequency components is stored in a non-transitory media.
13. The head-unit system of claim 8 , where the transformation function comprises an adaptive filtering algorithm.
14. The head-unit system of claim 13 , where the adaptive filtering algorithm comprises one of a least-mean-square, a normalized least-mean-square, an affine projection or a recursive least-square.
15. A computer readable medium comprising instructions, stored on a non-transitory medium executable by a processor, for:
obtaining a sync signal synchronized to an engine speed from a noise source reproduced into an acoustic space;
generating a noise model, representing one or more complex tones, responsive to the sync signal;
receiving an audio signal representing a sound field in the acoustic space;
applying an estimate of a secondary path transfer function to the noise model in a frequency domain; and
applying a transformation function to the noise model where the transform function is responsive to reducing a sum of an output of the transformation function and the received audio signal;
where applying the estimate of the secondary path transfer function comprises an online calculation; and
where an output of the secondary path transfer function is limited to a range of frequencies that lies within an aural range.
16. The computer readable medium of claim 15 , where the synchronized engine speed is generated from a tachometer.
17. The computer readable medium of claim 15 , where the synchronized engine speed is generated from an engine control unit.
18. The computer readable medium of claim 15 , where applying the estimate of the secondary path transfer function to the noise model comprises calculating a plurality of frequency components.
19. The computer readable medium of claim 18 , where the estimate of the secondary path transfer function for the plurality frequency components is stored in a non-transitory media.
20. The computer readable medium of claim 15 , where the transformation function comprises an adaptive filtering algorithm.
21. The computer readable medium of claim 20 , where the adaptive filtering algorithm comprises one of a least-mean-square, a normalized least-mean-square, an affine projection or a recursive least-square.