def en_timbre(a_track, b_track, distance='cos'): ''' computes timbral distance using EN features distance = ['cos'|'euc'|'man'|'all'] default is cos distance, can do all ''' a_bits = track.track_from_url(a_track["streaming"]) b_bits = track.track_from_url(b_track["streaming"]) a_timbre = np.zeros((len(a_bits.segments), 12)) for idx, seg in enumerate(a_bits.segments): a_timbre[idx] = seg['timbre'] a_stack = np.hstack((a_timbre.mean(axis=0),a_timbre.std(axis=0))) b_timbre = np.zeros((len(b_bits.segments), 12)) for idx, seg in enumerate(b_bits.segments): b_timbre[idx] = seg['timbre'] b_stack = np.hstack((b_timbre.mean(axis=0),b_timbre.std(axis=0))) if distance == 'cos': return spatial.distance.cosine(a_stack, b_stack) elif distance == 'euc': return spatial.distance.euclidean(a_stack, b_stack) elif distance == 'man': return spatial.distance.cityblock(a_stack, b_stack)#as in manhatten distance elif distance == 'all': euc = spatial.distance.euclidean(a_stack, b_stack) cos = spatial.distance.cosine(a_stack, b_stack) man = spatial.distance.cityblock(a_stack, b_stack)#as in manhatten distance return euc, cos, man
def main():
if DROPBOX_APP_KEY == '' or DROPBOX_APP_SECRET == '':
exit("You need to set your DROPBOX_APP_KEY and DROPBOX_APP_SECRET!")
dbclient = Dropbox(DROPBOX_APP_KEY, DROPBOX_APP_SECRET)
all_files = dbclient.all_files()
import random
random.shuffle(all_files)
for f in all_files:
fnew = DEFAULT + f["path"] + ".json"
if os.path.exists(fnew): continue # Skip things we've analyzed
try:
print >> sys.stderr, "%s" % f["path"]
fmedia = dbclient.api_client.media(f["path"])
t = track.track_from_url(fmedia["url"])
# print t.__dict__
r = requests.get(t.analysis_url)
if r.status_code == 200 and r.headers[
'content-type'] == "application/json":
d = os.path.split(fnew)
mkdir_p(d[0])
open(fnew, "wt").write(r.text)
# dbclient.api_client.put_file(fnew, r.text)
print >> sys.stderr, "... %s" % fnew
# print f["path"] + ".json"
# print r.json()
else:
print >> sys.stderr, "Error on %s, %s, %s" % (
f["path"], r.status_code, r.headers['content-type'])
# break
except Exception, e:
print >> sys.stderr, type(e), e, f["path"]
def main():
if DROPBOX_APP_KEY == '' or DROPBOX_APP_SECRET == '':
exit("You need to set your DROPBOX_APP_KEY and DROPBOX_APP_SECRET!")
dbclient = Dropbox(DROPBOX_APP_KEY, DROPBOX_APP_SECRET)
all_files = dbclient.all_files()
import random
random.shuffle(all_files)
for f in all_files:
fnew = DEFAULT + f["path"] + ".json"
if os.path.exists(fnew): continue # Skip things we've analyzed
try:
print >> sys.stderr, "%s" % f["path"]
fmedia = dbclient.api_client.media(f["path"])
t = track.track_from_url(fmedia["url"])
# print t.__dict__
r = requests.get(t.analysis_url)
if r.status_code == 200 and r.headers['content-type'] == "application/json":
d = os.path.split(fnew)
mkdir_p(d[0])
open(fnew, "wt").write(r.text)
# dbclient.api_client.put_file(fnew, r.text)
print >> sys.stderr, "... %s" % fnew
# print f["path"] + ".json"
# print r.json()
else:
print >> sys.stderr, "Error on %s, %s, %s" % (f["path"], r.status_code, r.headers['content-type'])
# break
except Exception, e:
print >> sys.stderr, type(e), e, f["path"]
def default(self, track_a, track_b):
"""
takes in two track ids from sc and grabs some analyze from EN. Uses this to make a similarity assertion between the two tracks.
Note, doing everything in approximately the stupidist way possible, so ymmv.
"""
root = init_scope()
a_track = root.tracks(int(track_a))
b_track = root.tracks(int(track_b))
a_bits = track.track_from_url(a_track.stream_url)
b_bits = track.track_from_url(b_track.stream_url)
a_timbre = np.zeros((len(a_bits.segments), 12))
for idx, seg in enumerate(a_bits.segments):
a_timbre[idx] = seg['timbre']
a_stack = np.hstack((a_timbre.mean(axis=0),a_timbre.std(axis=0)))
b_timbre = np.zeros((len(b_bits.segments), 12))
for idx, seg in enumerate(b_bits.segments):
b_timbre[idx] = seg['timbre']
b_stack = np.hstack((b_timbre.mean(axis=0),b_timbre.std(axis=0)))
euc = spatial.distance.euclidean(a_stack, b_stack)
cos = spatial.distance.cosine(a_stack, b_stack)
man = spatial.distance.cityblock(a_stack, b_stack)#as in manhatten distance
return """
timbral distance between <a href='{0}'>{1}</a> and <a href='{2}'>{3}</a> in various ways
<ul>
<li> Euclidean distance: {4}</li>
<li> Cosine distance: {5}</li>
<li> City block distance {6}</li>
</ul>
(<a href="../">back</a>)
""".format(
a_track.permalink_url,
"'"+a_track.title+"' by "+a_track.user.username,
b_track.permalink_url,
"'"+b_track.title+"' by "+b_track.user.username,
euc,
cos,
man)
def echonest_audio(tid):
url = 'https://api.soundcloud.com/tracks/%d/stream?consumer_key=17089d3c7d05cb2cfdffe46c2e486ff0' %tid
print url
attributes = {}
try:
t = track.track_from_url(url)
wait = t.get_analysis()
att = dir(t)
eid = t.id
attributes['tid'] = tid
attributes['eid'] = eid
key_attribute_list = ['instrumentalness', 'valence', 'loudness', 'duration', 'key', 'mode', 'danceability', 'acousticness', 'speechiness', 'tempo', 'liveness', 'energy']
segment_attr_list = ['pitches', 'timbre']
for a in att:
if a in key_attribute_list:
ta = getattr(t,a)
attributes[a] = ta
segment_data = t.segments
# average loudness over all segments
feature = 'loudness_max'
subs = []
for i in segment_data:
subs.append(i[feature])
attributes[feature+"_mean"] = np.mean(subs)
attributes[feature+"_std"] = np.std(subs)
attributes[feature+"_kurt"]= stats.kurtosis(subs)
attributes[feature+"_skew"] =stats.skew(subs)
# pitch and timbre have multiple levels
for feature in segment_attr_list:
for j in range(len(segment_data[0][feature])):
subs = []
if j<9:
featurestr = feature +"0"
else:
featurestr = feature
for i in segment_data:
subs.append(i[feature][j])
attributes[featurestr+str(j+1)+"_mean"] = np.mean(subs)
attributes[featurestr+str(j+1)+"_std"] = np.std(subs)
attributes[featurestr+str(j+1)+"_kurt"] = stats.kurtosis(subs)
attributes[featurestr+str(j+1)+"_skew"] = stats.skew(subs)
import pdb; pdb.set_trace()
except:
pass
return attributes
def _api_call(url):
track = etrack.track_from_url(url)
track.get_analysis()
return track
