def compute_ssm(wav_file, h, ssm_read_pk, is_ismir=False, tonnetz=False):
"""Computes the self similarity matrix from an audio file.
Parameters
----------
wav_file: str
Path to the wav file to be read.
h : float
Hop size.
ssm_read_pk : bool
Whether to read the ssm from a pickle file or not (note: this function
utomatically saves the ssm in a pickle file).
is_ismir : bool
Produce the plots that appear on the ISMIR paper.
tonnetz : bool
Compute tonnetz instead of Chroma features.
Returns
-------
X : np.array((N, N))
Self-similarity matrix
"""
if not ssm_read_pk:
# Read WAV file
logging.info("Reading the WAV file...")
C = utils.compute_audio_chromagram(wav_file, h)
C = utils.median_filter(C, L=9)
if is_ismir:
ismir.plot_chroma(C)
# Compute Tonnetz if needed
F = C
if tonnetz:
F = utils.chroma_to_tonnetz(C)
# Compute the self similarity matrix
logging.info("Computing key-invariant self-similarity matrix...")
X = utils.compute_key_inv_ssm(F, h)
#plt.imshow(X, interpolation="nearest", aspect="auto")
#plt.show()
utils.write_cPickle(wav_file + "-audio-ssm.pk", X)
else:
X = utils.read_cPickle(wav_file + "-audio-ssm.pk")
if is_ismir:
#X = X**2.5
ismir.plot_ssm(X)
ismir.plot_score_examples(X)
return X
def process(wav_file, outfile, csv_file=None, bpm=None, tol=0.35,
ssm_read_pk=False, read_pk=False, rho=2, is_ismir=False,
tonnetz=False, sonify=False):
"""Main process to find the patterns in a polyphonic audio file.
Parameters
----------
wav_file : str
Path to the wav file to be analyzed.
csv_file : str
Path to the csv containing the midi_score of the input audio file
(needed to produce a result that can be read for JKU dataset).
outfile : str
Path to file to save the results.
bpm : int
Beats per minute of the piece. If None, bpms are read from the JKU.
tol : float
Tolerance to find the segments in the SSM.
ssm_read_pk : bool
Whether to read the SSM from a pickle file.
read_pk : bool
Whether to read the segments from a pickle file.
rho : int
Positive integer to compute the score of the segments.
is_ismir : bool
Produce the plots that appear on the ISMIR paper.
tonnetz : bool
Whether to use Tonnetz or Chromas.
sonify : bool
Whether to sonify the patterns or not.
"""
# Get the correct bpm if needed
if bpm is None:
bpm = get_bpm(wav_file)
# Algorithm parameters
min_notes = 8
max_diff_notes = 4
h = bpm / 60. / 8. # Hop size /8 works better than /4, but it takes longer
# to process
# Obtain the Self Similarity Matrix
X = compute_ssm(wav_file, h, ssm_read_pk, is_ismir, tonnetz)
# Read CSV file
if csv_file is not None:
logging.info("Reading the CSV file for MIDI pitches...")
midi_score = utils.read_csv(csv_file)
patterns = []
csv_patterns = []
while patterns == [] or csv_patterns == []:
# Find the segments inside the self similarity matrix
logging.info("Finding segments in the self-similarity matrix...")
max_diff = int(max_diff_notes / float(h))
min_dur = int(np.ceil(min_notes / float(h)))
#print min_dur, min_notes, h, max_diff
if not read_pk:
segments = []
while segments == []:
logging.info("\ttrying tolerance %.2f" % tol)
segments = utils.find_segments(X, min_dur, th=tol, rho=rho)
tol -= 0.05
utils.write_cPickle(wav_file + "-audio.pk", segments)
else:
segments = utils.read_cPickle(wav_file + "-audio.pk")
# Obtain the patterns from the segments and split them if needed
logging.info("Obtaining the patterns from the segments...")
patterns = obtain_patterns(segments, max_diff)
# Decrease tolerance in case we couldn't find any patterns
tol -= 0.05
# Get the csv patterns if they exist
if csv_file is not None:
csv_patterns = patterns_to_csv(patterns, midi_score, h)
else:
csv_patterns = [0]
# Sonify patterns if needed
if sonify:
logging.info("Sonifying Patterns...")
utils.sonify_patterns(wav_file, patterns, h)
# Formatting csv patterns and save results
if csv_file is not None:
logging.info("Writting results into %s" % outfile)
utils.save_results(csv_patterns, outfile=outfile)
else:
# If not csv, just print the results on the screen
print_patterns(patterns, h)
if is_ismir:
ismir.plot_segments(X, segments)
# Alright, we're done :D
logging.info("Algorithm finished.")