Find large number of consecutive values fulfilling condition in a numpy array

Question

I have some audio data loaded in a numpy array and I wish to segment the data by finding silent parts, i.e. parts where the audio amplitude is below a certain threshold over a a period in time.

An extremely simple way to do this is something like this:

values = ''.join(("1" if (abs(x) < SILENCE_THRESHOLD) else "0" for x in samples)) pattern = re.compile('1{%d,}'%int(MIN_SILENCE))                                                                            for match in pattern.finditer(values):    # code goes here 

The code above finds parts where there are at least MIN_SILENCE consecutive elements smaller than SILENCE_THRESHOLD.

Now, obviously, the above code is horribly inefficient and a terrible abuse of regular expressions. Is there some other method that is more efficient, but still results in equally simple and short code?

Answer 1

Here's a numpy-based solution.

I think (?) it should be faster than the other options. Hopefully it's fairly clear.

However, it does require a twice as much memory as the various generator-based solutions. As long as you can hold a single temporary copy of your data in memory (for the diff), and a boolean array of the same length as your data (1-bit-per-element), it should be pretty efficient...

import numpy as np  def main():     # Generate some random data     x = np.cumsum(np.random.random(1000) - 0.5)     condition = np.abs(x) < 1          # Print the start and stop indices of each region where the absolute      # values of x are below 1, and the min and max of each of these regions     for start, stop in contiguous_regions(condition):         segment = x[start:stop]         print start, stop         print segment.min(), segment.max()  def contiguous_regions(condition):     """Finds contiguous True regions of the boolean array "condition". Returns     a 2D array where the first column is the start index of the region and the     second column is the end index."""      # Find the indicies of changes in "condition"     d = np.diff(condition)     idx, = d.nonzero()       # We need to start things after the change in "condition". Therefore,      # we'll shift the index by 1 to the right.     idx += 1      if condition[0]:         # If the start of condition is True prepend a 0         idx = np.r_[0, idx]      if condition[-1]:         # If the end of condition is True, append the length of the array         idx = np.r_[idx, condition.size] # Edit      # Reshape the result into two columns     idx.shape = (-1,2)     return idx  main()