Acoustic signal enhancement system
View Patent ↗A signal enhancement system improves the quality of a noisy input signal. The system finds a low noise signal model which best matches the noisy input signal. Noisy portions of the input signal are replaced with portions of the low noise signal models. As the input signal increases in noise content, the output signal includes an increasing amount of the low noise signal model. The system thereby produces an output signal with very low noise which corresponds to the input signal.
1. A method for enhancing an input signal, the method comprising:
determining a time-frequency representation of a noisy input signal;
estimating a background noise level and a signal-to-noise ratio;
determining a matching low noise signal template for the time-frequency representation; and
replacing a portion of the time-frequency representation with a mix of the time-frequency representation and the matching low noise signal template, the mix weighted by the signal-to-noise ratio.
2. The method of claim 1 , where determining comprises:
determining a matching low noise spectrogram.
3. The method of claim 1 , where determining comprises:
determining a smallest root mean square difference between the time-frequency representation and multiple low noise signal templates, including the matching low noise signal template, in a signal model.
4. The method of claim 3 , where the multiple low noise signal templates comprise low noise spectrograms.
5. The method of claim 1 , further comprising:
collecting multiple low noise signal templates, including the matching low noise signal template, into a signal model.
6. The method of claim 5 , further comprising:
training the signal model.
7. The method of claim 6 , further comprising:
determining whether a learning mode is active or inactive;
and where replacing further comprises:
replacing a portion of the digitized input signal with a signal-to-noise ratio weighted mix of the time-frequency representation and the matching low noise signal template, when the learning mode is inactive.
8. The method of claim 7 , where training comprises:
updating the matching low noise signal template with the time-frequency representation, when the learning mode is active.
9. The method of claim 7 , where training comprises:
adding the time-frequency representation as a new low noise signal template into the signal model, when the learning mode is active.
10. A system for enhancing an input signal, the system comprising:
means for transforming the input signal to a time-frequency representation;
means for determining a matching low noise signal template for the time-frequency representation; and
means for replacing a portion of the time-frequency representation with a signal-to-noise ratio weighted mix of the time-frequency representation and the matching low noise signal template.
11. The system of claim 10 , further comprising:
means for estimating a background noise level and a signal-to-noise ratio.
12. The system of claim 10 , further comprising:
means for detecting a speech utterance in the input signal, and where the portion is the speech utterance.
13. The system of claim 10 , further comprising:
means for training a signal model comprising the matching low noise signal template by updating the matching low noise signal template or adding a new low noise signal template comprising the time-frequency representation.
14. A signal enhancement system comprising:
a processor;
memory coupled to the processor, the memory comprising instructions which cause the processor to:
establish a signal model comprising multiple low noise signal templates;
obtain an input signal;
determine a matching low noise signal template in the signal model for the input signal; and
replace a portion of the input signal with a signal-to-noise ratio weighted mix of the input signal and the matching low noise signal template.
15. The system of claim 14 , where the memory further comprises instructions which cause the processor to:
determine an input signal spectrogram of the input signal; where:
the instructions which cause the processor to determine a matching low noise signal template cause the processor to determine a matching low noise spectrogram template; and where:
the instructions which cause the processor to replace a portion of the input signal cause the processor to generate a low noise spectrogram by replacing a portion of the input signal spectrogram with a signal-to-noise ratio weighted mix of the input signal spectrum and the matching low noise spectrogram template.
16. The system of claim 15 , where the memory further comprises instructions which cause the processor to:
synthesize a low noise output time series from the low noise spectrogram.
17. The system of claim 14 , where the instructions which cause the processor to determine a matching low noise signal template cause the processor to:
determine a signal-to-noise ratio weighted distance between the input signal and each of the low noise signal templates,
whereby frequency bands in the input signal contribute to the signal-to-noise ratio weighted distance in proportion to their signal-to-noise ratios.
18. The system of claim 14 , where the memory further comprises instructions which cause the processor to train the signal model.
19. The system of claim 18 , where the instructions which cause the processor to train the signal model comprise instructions which cause the processor to update at least one of the low noise signal templates in the signal model.
20. The system of claim 18 , where the instructions which cause the processor to train the signal model comprise instructions which cause the processor to add the input signal as a new low noise signal template to the signal model.
21. A product comprising:
a computer readable medium; and
instructions on the computer readable medium which cause a processor to:
determine a matching low noise signal template for a noisy input signal from a signal model comprising multiple low noise signal templates; and
replace a portion of the input signal with a signal-to-noise ratio weighted mix of the input signal and the matching low noise signal template.
22. The product of claim 21 , where the instructions further cause the processor to:
determine an input signal spectrogram of the noisy input signal; and where the instructions which determine a matching low noise signal template cause the processor to:
determine a signal-to-noise ratio weighted distance between the input signal spectrogram and each of the low noise signal templates; and
select, as the matching low noise signal template, the multiple low noise signal template in the signal model with the smallest signal-to-noise ratio weighted distance,
whereby frequency bands in the noisy input signal contribute to the signal-to-noise ratio weighted distance in proportion to their signal-to-noise ratios.
23. The product of claim 21 , where the medium further stores instructions which cause the processor to:
detect a transient in the noisy input signal prior to determining the matching low noise signal template.
24. The product of claim 23 , where the transient is a voice transient.
25. The product of claim 21 , where the medium further stores instructions which cause the processor to:
search for a transient in the noisy input signal;
update a background noise estimate when the transient is not present; and
determine the matching low noise signal template and replace the portion of the input signal upon detection of the transient.