In a machine learning application, a model learns to apply a filter
h(n) via convolution to a 1-dimensional input signal
s(n) (e.g. sampled at
Fs = 16 kHz). The filter size (filter length?) is limited to a fixed number, e.g.
k = 100, so the model cannot learn a filter that exceeds this duration but it is free to insert zeros and make the filter "shorter".
Does this mean that the filter size limits the duration of the "slowest" signal component that falls into the passing bandwidth and hence
Fs/k = 160 Hz is the minimal bandwidth that
h(n) can represent (assuming it's a band pass filter)?