def testRegressionStreaming(self):
# In Streaming mode the duration of the file is not known by the algorithm so it works by concatenating fragments.
# About 30 seconds of processing are required to show results consistant with the Standard mode.
loader = es.MonoLoader(filename=join(testdata.audio_dir, 'recorded', 'mozart_c_major_30sec.wav'))
cp = es.Chromaprinter(analysisTime=30, concatenate=True)
pool = Pool()
loader.audio >> cp.signal
cp.fingerprint >> (pool, 'chromaprint')
es.essentia.run(loader)
self.assertEqualVector(self.expected, pool['chromaprint'][0])
def chromaprint(self, analysisTime=30):
"""
This algorithm computes the fingerprint of the input signal using Chromaprint algorithm.
It is a wrapper of the Chromaprint library
Returns: The chromaprints are returned as base64-encoded strings.
"""
vec_input = ess.VectorInput(self.audio_vector)
chromaprinter = ess.Chromaprinter(analysisTime=analysisTime, sampleRate=self.fs)
pool = Pool()
vec_input.data >> chromaprinter.signal
chromaprinter.fingerprint >> (pool, 'chromaprint')
run(vec_input)
return pool['chromaprint']
import essentia.standard as es
import essentia.streaming as ess
import matplotlib.pyplot as plt
loader = ess.MonoLoader(filename = '/home/lib/audio/work/arglaaa-mini/22.wav')
fps = ess.Chromaprinter(analysisTime=20, concatenate=True)
pool = ess.essentia.Pool()
# Conecting the algorithms
loader.audio >> fps.signal
fps.fingerprint >> (pool, 'chromaprint')
ess.essentia.run(loader)
fp = pool['chromaprint'][0]
print(('fp = {0}'.format(fp)))
import acoustid as ai
# import acoustid.chromaprint
import codecs
import numpy as np
print('type(fp) = ', type(fp))
fpbytes = bytes(fp, 'utf-8')
print('fpbytes = {0}'.format(fpbytes))
fp_int = ai.chromaprint.decode_fingerprint(fpbytes)[0]