algorithms/asa.py
def _break_poorly_matched_fronts(fronts, threshold=0.1, threshold_overlap_samples=3):
    """
    For each onset front, for each frequency in that front, break the onset front if the signals
    between this frequency's onset and the next frequency's onset are not similar enough.

    Specifically:
    If we have the following two frequency channels, and the two O's are part of the same onset front,

    ::

        [ . O . . . . . . . . . . ]
        [ . . . . O . . . . . . . ]

    We compare the signals x and y:

    ::

        [ . x x x x . . . . . . . ]
        [ . y y y y . . . . . . . ]

    And if they are not sufficiently similar (via a DSP correlation algorithm), we break the onset
    front between these two channels.

    Once this is done, remove any onset fronts that are less than 3 channels wide.
    """
    assert threshold_overlap_samples > 0, "Number of samples of overlap must be greater than zero"
    breaks_after = {}
    for front_id in _get_front_ids_one_at_a_time(fronts):
        front = _get_front_idxs_from_id(fronts, front_id)
        for i, (f, s) in enumerate(front):
            if i < len(front) - 1:
                # Get the signal from f, s to f, s+1 and the signal from f+1, s to f+1, s+1
                next_f, next_s = front[i + 1]
                low_s = min(s, next_s)
                high_s = max(s, next_s)
                sig_this_f = fronts[f, low_s:high_s]
                sig_next_f = fronts[next_f, low_s:high_s]
                assert len(sig_next_f) == len(sig_this_f)

                if len(sig_next_f) > threshold_overlap_samples:
                    # If these two signals are not sufficiently close in form, this front should be broken up
                    correlation = signal.correlate(sig_this_f, sig_next_f, mode='same')
                    assert len(correlation) > 0
                    correlation = correlation / max(correlation + 1E-9)
                    similarity = np.sum(correlation) / len(correlation)
                    # TODO: the above stuff probably needs to be figured out
                    if similarity < threshold:
                        if front_id in breaks_after:
                            breaks_after[front_id].append((f, s))
                        else:
                            breaks_after[front_id] = []

    # Now update the fronts matrix by breaking up any fronts at the points we just identified
    # and assign the newly created fronts new IDs
    taken_ids = sorted(np.unique(fronts))
    next_id = taken_ids[-1] + 1
    for id in breaks_after.keys():
        for f, s in breaks_after[id]:
            fidxs, sidxs = np.where(fronts == id)
            idxs_greater_than_f = [fidx for fidx in fidxs if fidx > f]
            start = len(sidxs) - len(idxs_greater_than_f)
            indexes = (idxs_greater_than_f, sidxs[start:])
            fronts[indexes] = next_id
            next_id += 1

    _remove_fronts_that_are_too_small(fronts, 3)
def _break_poorly_matched_fronts(fronts, threshold=0.1, threshold_overlap_samples=3):
    assert threshold_overlap_samples > 0, "Number of samples of overlap must be greater than zero"
    breaks_after = {}
    for front_id in _get_front_ids_one_at_a_time(fronts):
        front = _get_front_idxs_from_id(fronts, front_id)
        for i, (f, s) in enumerate(front):
            if i < len(front) - 1:
                # Get the signal from f, s to f, s+1 and the signal from f+1, s to f+1, s+1
                next_f, next_s = front[i + 1]
                low_s = min(s, next_s)
                high_s = max(s, next_s)
                sig_this_f = fronts[f, low_s:high_s]
                sig_next_f = fronts[next_f, low_s:high_s]
                assert len(sig_next_f) == len(sig_this_f)

                if len(sig_next_f) > threshold_overlap_samples:
                    # If these two signals are not sufficiently close in form, this front should be broken up
                    correlation = signal.correlate(sig_this_f, sig_next_f, mode='same')
                    assert len(correlation) > 0
                    correlation = correlation / max(correlation + 1E-9)
                    similarity = np.sum(correlation) / len(correlation)
                    # TODO: the above stuff probably needs to be figured out
                    if similarity < threshold:
                        if front_id in breaks_after:
                            breaks_after[front_id].append((f, s))
                        else:
                            breaks_after[front_id] = []

    # Now update the fronts matrix by breaking up any fronts at the points we just identified
    # and assign the newly created fronts new IDs
    taken_ids = sorted(np.unique(fronts))
    next_id = taken_ids[-1] + 1
    for id in breaks_after.keys():
        for f, s in breaks_after[id]:
            fidxs, sidxs = np.where(fronts == id)
            idxs_greater_than_f = [fidx for fidx in fidxs if fidx > f]
            start = len(sidxs) - len(idxs_greater_than_f)
            indexes = (idxs_greater_than_f, sidxs[start:])
            fronts[indexes] = next_id
            next_id += 1

    _remove_fronts_that_are_too_small(fronts, 3)
def _break_poorly_matched_fronts ( fronts , threshold = 0.1 , threshold_overlap_samples = 3 ) : assert threshold_overlap_samples > 0 , "Number of samples of overlap must be greater than zero" breaks_after = { } for front_id in _get_front_ids_one_at_a_time ( fronts ) : front = _get_front_idxs_from_id ( fronts , front_id ) for i , ( f , s ) in enumerate ( front ) : if i < len ( front ) - 1 : # Get the signal from f, s to f, s+1 and the signal from f+1, s to f+1, s+1 next_f , next_s = front [ i + 1 ] low_s = min ( s , next_s ) high_s = max ( s , next_s ) sig_this_f = fronts [ f , low_s : high_s ] sig_next_f = fronts [ next_f , low_s : high_s ] assert len ( sig_next_f ) == len ( sig_this_f ) if len ( sig_next_f ) > threshold_overlap_samples : # If these two signals are not sufficiently close in form, this front should be broken up correlation = signal . correlate ( sig_this_f , sig_next_f , mode = 'same' ) assert len ( correlation ) > 0 correlation = correlation / max ( correlation + 1E-9 ) similarity = np . sum ( correlation ) / len ( correlation ) # TODO: the above stuff probably needs to be figured out if similarity < threshold : if front_id in breaks_after : breaks_after [ front_id ] . append ( ( f , s ) ) else : breaks_after [ front_id ] = [ ] # Now update the fronts matrix by breaking up any fronts at the points we just identified # and assign the newly created fronts new IDs taken_ids = sorted ( np . unique ( fronts ) ) next_id = taken_ids [ - 1 ] + 1 for id in breaks_after . keys ( ) : for f , s in breaks_after [ id ] : fidxs , sidxs = np . where ( fronts == id ) idxs_greater_than_f = [ fidx for fidx in fidxs if fidx > f ] start = len ( sidxs ) - len ( idxs_greater_than_f ) indexes = ( idxs_greater_than_f , sidxs [ start : ] ) fronts [ indexes ] = next_id next_id += 1 _remove_fronts_that_are_too_small ( fronts , 3 )
For each onset front, for each frequency in that front, break the onset front if the signals
    between this frequency's onset and the next frequency's onset are not similar enough.

    Specifically:
    If we have the following two frequency channels, and the two O's are part of the same onset front,

    ::

        [ . O . . . . . . . . . . ]
        [ . . . . O . . . . . . . ]

    We compare the signals x and y:

    ::

        [ . x x x x . . . . . . . ]
        [ . y y y y . . . . . . . ]

    And if they are not sufficiently similar (via a DSP correlation algorithm), we break the onset
    front between these two channels.

    Once this is done, remove any onset fronts that are less than 3 channels wide.
For each onset front for each frequency in that front break the onset front if the signals between this frequency s onset and the next frequency s onset are not similar enough .