I'm currently working with audio signals and have a problem:
C = A*B + N, where
C = recorded signal from microphone consisting of:
A = known music file data played on speakers next to microphone
B = some convolution on the recorded A-sound due to speaker->mic roundtrip
(I mean the recorded signal won't be 100% the same as the audio data from the file before it is played to the speaker and recorded by mic. (Is this an impulse response?))
N = some additional noise sounds recorded by microphone
My goal: an approximate estimation as to whether there is a signal N and how loud it is.
I don't have a need for accurate data!
I'm working with Apple's vDSP API. I have cross correlated the signals A and C, so I have the time window in which the signals overlap.
In the overlapping window, I have both signals in the time and frequency domain.
Currently I'm helpless if, for example, a Wiener filter is the right approach and if I'm capable to apply one with my known parameters(Is a known noise required? or the impulse response of the environment?). I tried to apply a Wiener deconvolution by dividing C/A in the frequency domain with no success.
Once more: I don't need accurate data, just a rough guess how much N is there in the signal C. Actually a SNR like measure would be sufficient.