def run(self, musicbrainzid, fname):
pitch = util.docserver_get_json(musicbrainzid,
"pitch",
"pitch",
version="noguessunv")
tonic = util.docserver_get_contents(musicbrainzid,
self.tonicname,
"tonic",
version=self.tonicversion)
tonic = float(tonic)
nppitch = np.array(pitch)
bpo = 64 # 256 pixel high image spanning 4 octaves = 64px/octave
height = 255 # Height of the image
drawpitch = self.normalise_pitch(nppitch[:, 1], tonic, bpo, height)
packed_pitch = StringIO.StringIO()
for p in drawpitch:
packed_pitch.write(struct.pack("B", p))
drawhist = self.get_histogram(drawpitch, 256, 1)
bpo = 120 # 10 cents per bin (original resolution in melody calculation)
max_value = bpo * 4 # 4 octaves
simpitch = self.normalise_pitch(nppitch[:, 1], tonic, bpo, max_value)
simhist = self.get_histogram(simpitch, max_value, 7)
return {
"packedpitch": packed_pitch.getvalue(),
"normalisedpitch": drawpitch,
"drawhistogram": drawhist,
"normalisedhistogram": simhist
}
def obtainPitchTonicForRaga(raganame, outputfolder):
if not os.path.exists(outputfolder):
os.makedirs(outputfolder)
#get raga recording mapping
ragaRecordings = obtainRagaRecordingMapping()
print len(ragaRecordings.keys())
for mbid in ragaRecordings[raganame]:
pitchData = util.docserver_get_json(mbid,'pitch','pitch')
tonic = float(util.docserver_get_contents(mbid,'ctonic','tonic'))
HopSize = 196
filename =util.docserver_get_filename(mbid,'mp3')
shutil.copyfile(filename, outputfolder+ '/'+filename.split('/')[-1])
filename = filename.split('/')[-1].split('.')[0]
#TStamps = np.array(range(0,len(pitchData)))*np.float(HopSize)/44100.0
#dump = np.array([TStamps, pitchData]).transpose()
np.savetxt(outputfolder+ '/'+filename+'.pitch', pitchData, delimiter = "\t")
np.savetxt(outputfolder+ '/'+filename+'.tonic', np.array([tonic]), delimiter = "\t")
def calc_profile(self, raag, recordings, style):
average = compmusic.extractors.similaritylib.raaga.Raaga(raag.name, "")
pitches = []
tonics = []
for r in recordings:
mbid = r.mbid
try:
pitch = util.docserver_get_json(mbid, "pitch", "pitch")
tonic = util.docserver_get_contents(mbid, "votedtonic", "tonic")
tonic = float(tonic)
npp = np.array(pitch)
pitches.append(npp)
tonics.append(tonic)
except util.NoFileException:
pass
average.compute_average_hist_data(pitches, tonics)
if style == "carnatic":
entityname = "raaga"
elif style == "hindustani":
entityname = "raag"
fname = "%s-%s-%s.png" % (style, entityname, raag.common_name.lower().replace(" ", ""))
average.generate_image(fname)
im = data.models.Image()
raag.images.remove()
im.image.save(fname, ContentFile(open(fname, "rb").read()))
raag.images.add(im)
os.unlink(fname)
def calc_profile(self, raag, recordings, style):
average = compmusic.extractors.similaritylib.raaga.Raaga(raag.name, "")
pitches = []
tonics = []
for r in recordings:
mbid = r.mbid
try:
pitch = util.docserver_get_json(mbid, "pitch", "pitch")
tonic = util.docserver_get_contents(mbid, "votedtonic",
"tonic")
tonic = float(tonic)
npp = np.array(pitch)
pitches.append(npp)
tonics.append(tonic)
except util.NoFileException:
pass
average.compute_average_hist_data(pitches, tonics)
if style == "carnatic":
entityname = "raaga"
elif style == "hindustani":
entityname = "raag"
fname = "%s-%s-%s.png" % (style, entityname,
raag.common_name.lower().replace(" ", ""))
average.generate_image(fname)
im = data.models.Image()
im.image.save(fname, ContentFile(open(fname, "rb").read()))
raag.image = im
raag.save()
os.unlink(fname)
def calc_profile(self, raag, recordings):
average = raaga.Raaga(raag.name, "")
pitches = []
tonics = []
for r in recordings:
mbid = r.mbid
try:
pitch = util.docserver_get_json(mbid, "pitch", "pitch")
tonic = util.docserver_get_contents(mbid, "votedtonic", "tonic")
tonic = float(tonic)
npp = np.array(pitch)
pitches.append(npp)
tonics.append(tonic)
except util.NoFileException:
pass
average.compute_average_hist_data(pitches, tonics)
fname = "hindustani-raag-%s.png" % raag.common_name.lower().replace(" ", "")
average.generate_image(fname)
im = data.models.Image()
raag.images.remove()
im.image.save(fname, ContentFile(open(fname, "rb").read()))
raag.images.add(im)
os.unlink(fname)
def run(self, fname):
pitch = util.docserver_get_json(self.musicbrainz_id, "pitch", "pitch", version="noguessunv")
tonic = util.docserver_get_contents(self.musicbrainz_id, "votedtonic", "tonic", version="0.2")
tonic = float(tonic)
nppitch = np.array(pitch)
bpo = 64 # 256 pixel high image spanning 4 octaves = 64px/octave
height = 255 # Height of the image
drawpitch = self.normalise_pitch(nppitch[:,1], tonic, bpo, height)
packed_pitch = StringIO.StringIO()
for p in drawpitch:
packed_pitch.write(struct.pack("B", p))
drawhist = self.get_histogram(drawpitch, 256, 1)
bpo = 120 # 10 cents per bin (original resolution in melody calculation)
max_value = bpo * 4 # 4 octaves
simpitch = self.normalise_pitch(nppitch[:,1], tonic, bpo, max_value)
simhist = self.get_histogram(simpitch, max_value, 7)
return {"packedpitch": packed_pitch.getvalue(),
"normalisedpitch": drawpitch,
"drawhistogram": drawhist,
"normalisedhistogram": simhist}
def calc_profile(self, raag, recordings):
average = compmusic.extractors.similaritylib.raaga.Raaga(raag.name, "")
pitches = []
tonics = []
for r in recordings:
mbid = r.mbid
try:
pitch = util.docserver_get_json(mbid, "pitch", "pitch")
tonic = util.docserver_get_contents(mbid, "votedtonic", "tonic")
try:
tonic = float(tonic)
npp = np.array(pitch)
pitches.append(npp)
tonics.append(tonic)
except ValueError:
pass
except util.NoFileException:
pass
average.compute_average_hist_data(pitches, tonics)
profile = average.average_hist[:, 0]
y = np.concatenate((profile[-50:], profile[:-50]))
y = gaussian_filter(y, 7)
return y.tolist()