LogFiltSpecFlux

#!/usr/bin/env python
# encoding: utf-8
"""
LogFiltSpecFlux onset detection algorithm.

"""

from __future__ import absolute_import, division, print_function

import argparse

from madmom.audio import (FramedSignalProcessor,
                          LogarithmicSpectrogramProcessor, SignalProcessor,
                          SpectrogramDifferenceProcessor)
from madmom.audio.filters import FilterbankProcessor, LogarithmicFilterbank
from madmom.features import (ActivationsProcessor, OnsetPeakPickingProcessor,
                             SpectralOnsetProcessor)
from madmom.io import write_onsets
from madmom.processors import IOProcessor, io_arguments


def main():
    """LogFiltSpecFlux"""

    # define parser
    p = argparse.ArgumentParser(
        formatter_class=argparse.RawDescriptionHelpFormatter, description='''
    The LogFiltSpecFlux program detects all onsets in an audio file according
    to the LogFiltSpecFlux algorithm described in:

    "Evaluating the Online Capabilities of Onset Detection Methods"
    Sebastian Böck, Florian Krebs and Markus Schedl.
    Proceedings of the 13th International Society for Music Information
    Retrieval Conference (ISMIR), 2012.

    This program can be run in 'single' file mode to process a single audio
    file and write the detected onsets to STDOUT or the given output file.

      $ LogFiltSpecFlux single INFILE [-o OUTFILE]

    If multiple audio files should be processed, the program can also be run
    in 'batch' mode to save the detected onsets to files with the given suffix.

    $   LogFiltSpecFlux batch [-o OUTPUT_DIR] [-s OUTPUT_SUFFIX] FILES

    If no output directory is given, the program writes the files with the
    detected onsets to the same location as the audio files.

    The 'pickle' mode can be used to store the used parameters to be able to
    exactly reproduce experiments.

    ''')
    # version
    p.add_argument('--version', action='version',
                   version='LogFiltSpecFlux.2016')
    # add arguments
    io_arguments(p, output_suffix='.onsets.txt')
    ActivationsProcessor.add_arguments(p)
    SignalProcessor.add_arguments(p, norm=False, gain=0)
    FramedSignalProcessor.add_arguments(p, fps=100)
    FilterbankProcessor.add_arguments(p, filterbank=LogarithmicFilterbank,
                                      num_bands=12, fmin=30, fmax=17000,
                                      norm_filters=False)
    LogarithmicSpectrogramProcessor.add_arguments(p, log=True, mul=1, add=1)
    SpectrogramDifferenceProcessor.add_arguments(p, diff_ratio=0.5,
                                                 positive_diffs=True)
    OnsetPeakPickingProcessor.add_arguments(p, threshold=1.6, pre_max=0.01,
                                            post_max=0.05, pre_avg=0.15,
                                            post_avg=0, combine=0.03, delay=0)
    # parse arguments
    args = p.parse_args()

    # print arguments
    if args.verbose:
        print(args)

    # input processor
    if args.load:
        # load the activations from file
        in_processor = ActivationsProcessor(mode='r', **vars(args))
    else:
        # define spectral onset detection function
        in_processor = SpectralOnsetProcessor(onset_method='spectral_flux',
                                              **vars(args))

    # output processor
    if args.save:
        # save the onset activations to file
        out_processor = ActivationsProcessor(mode='w', **vars(args))
    else:
        # detect the onsets and output them
        peak_picking = OnsetPeakPickingProcessor(**vars(args))
        out_processor = [peak_picking, write_onsets]

    # create an IOProcessor
    processor = IOProcessor(in_processor, out_processor)

    # and call the processing function
    args.func(processor, **vars(args))


if __name__ == '__main__':
    main()