Online computation

Typically in auditory modelling, we precompute the entire output of each channel of the filterbank (“offline computation”), and then work with that. This is straightforward, but puts a severe limit on the number of channels we can use or the length of time we can work with (otherwise the RAM would be quickly exhausted). Brian hears allows us to use a very large number of channels in filterbanks, but at the cost of only storing the output of the filterbanks for a relatively short period of time (“online computation”). This requires a slight change in the way we use the output of the filterbanks, but is actually not too difficult. For example, suppose we wanted to compute the vector of RMS values for each channel of the output of the filterbank. Traditionally, or if we just use the syntax output = fb.process() in Brian hears, we have an array output of shape (nsamples, nchannels). We could compute the vector of RMS values as:

rms = sqrt(mean(output**2, axis=0))

To do the same thing with online computation, we simply store a vector of the running sum of squares, and update it for each buffered segment as it is computed. At the end of the processing, we divide the sum of squares by the number of samples and take the square root.

The Filterbank.process() method allows us to pass an optional function f(output, running) of two arguments. In this case, process() will first call running = f(output, 0) for the first buffered segment output. It will then call running = f(output, running) for each subsequent segment. In other words, it will “accumulate” the output of f, passing the output of each call to the subsequent call. To compute the vector of RMS values then, we simply do:

def sum_of_squares(input, running):
    return running+sum(input**2, axis=0)

rms = sqrt(fb.process(sum_of_squares)/nsamples)

If the computation you wish to perform is more complicated than can be achieved with the process() method, you can derive a class from Filterbank (see that class’ reference documentation for more details on this).