Мобильная версияI have implemented a modified linear prediction coding function like this:
def modified_lpc_process(s: NDArray, p: int, fs: int) -> NDArray: a = lpc_coefficients(s, p) a_hat = modified_pole_coeffs(a, fs=fs) # a_hat = a # debug line s_hat = numpy.zeros(len(s)) for n, sn in enumerate(s): as_accum = 0 for k in range(len(a)): if n - 1 - k < 0: break as_accum += a[k] * s[n - 1 - k] un = sn + as_accum as_hat_accum = 0 for k in range(len(a_hat)): if n - 1 - k < 0: break as_hat_accum += a_hat[k] * s_hat[n - 1 - k] s_hat[n] = -as_hat_accum + un round(s_hat[n], 8) return s_hat Which essentially implements the maths here:
I have noticed that the floating point error in this operation quickly blows up and I end up with sample values too large. In an effort to debug, I've set the modified pole coefficients a_hat to be the same with a, expecting to get the same value for s_hat and s; but the same issue is still there. I've also just realised that in floating point, adding a number and subtracting the same number you end up with slightly different results, which in recursive applications like this can accumulate to large differences.
I wonder what the best practice is in this case to mitigate this error accumulation?
Источник: https://stackoverflow.com/questions/781 ... lculations
