def get_btchromas_loudness(h5):
"""
Similar to btchroma, but adds the loudness back.
We use the segments_loudness_max
There is no max value constraint, simply no negative values.
"""
# if string, open and get chromas, if h5, get chromas
if type(h5).__name__ == 'str':
h5 = GETTERS.open_h5_file_read(h5)
chromas = GETTERS.get_segments_pitches(h5)
segstarts = GETTERS.get_segments_start(h5)
btstarts = GETTERS.get_beats_start(h5)
duration = GETTERS.get_duration(h5)
loudnessmax = GETTERS.get_segments_loudness_max(h5)
h5.close()
else:
chromas = GETTERS.get_segments_pitches(h5)
segstarts = GETTERS.get_segments_start(h5)
btstarts = GETTERS.get_beats_start(h5)
duration = GETTERS.get_duration(h5)
loudnessmax = GETTERS.get_segments_loudness_max(h5)
# get the series of starts for segments and beats
segstarts = np.array(segstarts).flatten()
btstarts = np.array(btstarts).flatten()
# add back loudness
chromas = chromas.T * idB(loudnessmax)
# aligned features
btchroma = align_feats(chromas, segstarts, btstarts, duration)
if btchroma is None:
return None
# done (no renormalization)
return btchroma
def get_bttimbre(h5):
"""
Get beat-aligned timbre from a song file of the Million Song Dataset
INPUT:
h5 - filename or open h5 file
RETURN:
bttimbre - beat-aligned timbre, one beat per column
or None if something went wrong (e.g. no beats)
"""
# if string, open and get timbre, if h5, get timbre
if type(h5).__name__ == 'str':
h5 = GETTERS.open_h5_file_read(h5)
timbre = GETTERS.get_segments_timbre(h5)
segstarts = GETTERS.get_segments_start(h5)
btstarts = GETTERS.get_beats_start(h5)
duration = GETTERS.get_duration(h5)
h5.close()
else:
timbre = GETTERS.get_segments_timbre(h5)
segstarts = GETTERS.get_segments_start(h5)
btstarts = GETTERS.get_beats_start(h5)
duration = GETTERS.get_duration(h5)
# get the series of starts for segments and beats
# NOTE: MAYBE USELESS?
# result for track: 'TR0002Q11C3FA8332D'
# segstarts.shape = (708,)
# btstarts.shape = (304,)
segstarts = np.array(segstarts).flatten()
btstarts = np.array(btstarts).flatten()
# aligned features
bttimbre = align_feats(timbre.T, segstarts, btstarts, duration)
if bttimbre is None:
return None
# done (no renormalization)
return bttimbre
def get_btloudnessmax(h5):
"""
Get beat-aligned loudness max from a song file of the Million Song Dataset
INPUT:
h5 - filename or open h5 file
RETURN:
btloudnessmax - beat-aligned loudness max, one beat per column
or None if something went wrong (e.g. no beats)
"""
# if string, open and get max loudness, if h5, get max loudness
if type(h5).__name__ == 'str':
h5 = GETTERS.open_h5_file_read(h5)
loudnessmax = GETTERS.get_segments_loudness_max(h5)
segstarts = GETTERS.get_segments_start(h5)
btstarts = GETTERS.get_beats_start(h5)
duration = GETTERS.get_duration(h5)
h5.close()
else:
loudnessmax = GETTERS.get_segments_loudness_max(h5)
segstarts = GETTERS.get_segments_start(h5)
btstarts = GETTERS.get_beats_start(h5)
duration = GETTERS.get_duration(h5)
# get the series of starts for segments and beats
# NOTE: MAYBE USELESS?
# result for track: 'TR0002Q11C3FA8332D'
# segstarts.shape = (708,)
# btstarts.shape = (304,)
segstarts = np.array(segstarts).flatten()
btstarts = np.array(btstarts).flatten()
# reverse dB
loudnessmax = idB(loudnessmax)
# aligned features
btloudnessmax = align_feats(loudnessmax.reshape(1,
loudnessmax.shape[0]),
segstarts, btstarts, duration)
if btloudnessmax is None:
return None
# set it back to dB
btloudnessmax = dB(btloudnessmax + 1e-10)
# done (no renormalization)
return btloudnessmax
def get_btchromas(h5):
"""
Get beat-aligned chroma from a song file of the Million Song Dataset
INPUT:
h5 - filename or open h5 file
RETURN:
btchromas - beat-aligned chromas, one beat per column
or None if something went wrong (e.g. no beats)
"""
# if string, open and get chromas, if h5, get chromas
if type(h5).__name__ == 'str':
h5 = GETTERS.open_h5_file_read(h5)
chromas = GETTERS.get_segments_pitches(h5)
segstarts = GETTERS.get_segments_start(h5)
btstarts = GETTERS.get_beats_start(h5)
duration = GETTERS.get_duration(h5)
h5.close()
else:
chromas = GETTERS.get_segments_pitches(h5)
segstarts = GETTERS.get_segments_start(h5)
btstarts = GETTERS.get_beats_start(h5)
duration = GETTERS.get_duration(h5)
# get the series of starts for segments and beats
# NOTE: MAYBE USELESS?
# result for track: 'TR0002Q11C3FA8332D'
# segstarts.shape = (708,)
# btstarts.shape = (304,)
segstarts = np.array(segstarts).flatten()
btstarts = np.array(btstarts).flatten()
# aligned features
btchroma = align_feats(chromas.T, segstarts, btstarts, duration)
if btchroma is None:
return None
# Renormalize. Each column max is 1.
maxs = btchroma.max(axis=0)
maxs[np.where(maxs == 0)] = 1.
btchroma = (btchroma / maxs)
# done
return btchroma
def iterate_folder_songs_extracted(root_path, filename_re):
"""Iterate over a collection of HDF5 database files, each one containing
structured data, presumably a song.
Yields a processed record of song data.
Arguments:
root_path -- File path to the root of the file collection.
filename_re -- Regular expression to match HDF5 files
"""
for filepath in iterate_folder_songs(root_path, filename_re):
# Extract the important data from the full song record
filename = os.path.basename(filepath)
ext = os.path.splitext(filename)[1]
if ext == '.h5':
song = hdf5_getters.open_h5_file_read(filepath)
id = hdf5_getters.get_track_id(song)
artist = hdf5_getters.get_artist_name(song)
title = hdf5_getters.get_title(song)
timbre = hdf5_getters.get_segments_timbre(song)
sections_start = hdf5_getters.get_sections_start(song)
sections_conf = hdf5_getters.get_sections_confidence(song)
segments_start = hdf5_getters.get_segments_start(song)
song_end = hdf5_getters.get_duration(song)
song.close()
elif ext == '.analysis':
id = json_anal_getters.get_track_id(filepath)
artist = json_anal_getters.get_artist_name(filepath)
title = json_anal_getters.get_title(filepath)
timbre = json_anal_getters.get_segments_timbre(filepath)
sections_start = json_anal_getters.get_sections_start(filepath)
sections_conf = json_anal_getters.get_sections_confidence(filepath)
segments_start = json_anal_getters.get_segments_start(filepath)
song_end = json_anal_getters.get_duration(filepath)
else:
raise Exception("unrecognized file type: {0}".format(filename))
# Combine into a song record
song_rec = song_record(id, artist, title, timbre, sections_start, sections_conf, segments_start, song_end)
yield song_rec