def test_cens():
# load CQT data from Chroma Toolbox
ct_cqt = load(os.path.join('data', 'features-CT-cqt.mat'))
fn_ct_chroma_cens = [
'features-CT-CENS_9-2.mat', 'features-CT-CENS_21-5.mat',
'features-CT-CENS_41-1.mat'
]
cens_params = [(9, 2), (21, 5), (41, 1)]
for cur_test_case, cur_fn_ct_chroma_cens in enumerate(fn_ct_chroma_cens):
win_len_smooth = cens_params[cur_test_case][0]
downsample_smooth = cens_params[cur_test_case][1]
# plug into librosa cens computation
lr_chroma_cens = librosa.feature.chroma_cens(
C=ct_cqt['f_cqt'],
win_len_smooth=win_len_smooth,
fmin=librosa.core.midi_to_hz(1),
bins_per_octave=12,
n_octaves=10)
# leaving out frames to match chroma toolbox behaviour
# lr_chroma_cens = librosa.resample(lr_chroma_cens, orig_sr=1, target_sr=1/downsample_smooth)
lr_chroma_cens = lr_chroma_cens[:, ::downsample_smooth]
# load CENS-41-1 features
ct_chroma_cens = load(os.path.join('data', cur_fn_ct_chroma_cens))
assert np.allclose(ct_chroma_cens['f_CENS'],
lr_chroma_cens,
rtol=1e-15,
atol=1e-15)
def test_cens():
# load CQT data from Chroma Toolbox
ct_cqt = load(os.path.join('tests', 'data', 'features-CT-cqt.mat'))
fn_ct_chroma_cens = ['features-CT-CENS_9-2.mat',
'features-CT-CENS_21-5.mat',
'features-CT-CENS_41-1.mat']
cens_params = [(9, 2), (21, 5), (41, 1)]
for cur_test_case, cur_fn_ct_chroma_cens in enumerate(fn_ct_chroma_cens):
win_len_smooth = cens_params[cur_test_case][0]
downsample_smooth = cens_params[cur_test_case][1]
# plug into librosa cens computation
lr_chroma_cens = librosa.feature.chroma_cens(C=ct_cqt['f_cqt'],
win_len_smooth=win_len_smooth,
fmin=librosa.core.midi_to_hz(1),
bins_per_octave=12,
n_octaves=10)
# leaving out frames to match chroma toolbox behaviour
# lr_chroma_cens = librosa.resample(lr_chroma_cens, orig_sr=1, target_sr=1/downsample_smooth)
lr_chroma_cens = lr_chroma_cens[:, ::downsample_smooth]
# load CENS-41-1 features
ct_chroma_cens = load(os.path.join('tests', 'data', cur_fn_ct_chroma_cens))
maxdev = np.abs(ct_chroma_cens['f_CENS'] - lr_chroma_cens)
assert np.allclose(ct_chroma_cens['f_CENS'], lr_chroma_cens, rtol=1e-15, atol=1e-15), maxdev
def __test(infile):
DATA = load(infile)
y, sr = librosa.load(os.path.join('tests', DATA['wavfile'][0]),
sr=None,
mono=True)
n_fft = DATA['nfft'][0, 0].astype(int)
hop_length = DATA['hop_length'][0, 0].astype(int)
S = DATA['S']
# normalization is disabled here, since the precomputed S is already
# normalized
# metlab uses p=1, other folks use p=2
bw = librosa.feature.spectral_bandwidth(S=S,
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
centroid=DATA['centroid'],
norm=False,
p=1)
# METlab implementation takes the mean, not the sum
assert np.allclose(bw, S.shape[0] * DATA['bw'])
def test_spectral_bandwidth(infile):
DATA = load(infile)
y, sr = librosa.load(os.path.join("tests", DATA["wavfile"][0]),
sr=None,
mono=True)
n_fft = DATA["nfft"][0, 0].astype(int)
hop_length = DATA["hop_length"][0, 0].astype(int)
S = DATA["S"]
# normalization is disabled here, since the precomputed S is already
# normalized
# metlab uses p=1, other folks use p=2
bw = librosa.feature.spectral_bandwidth(
S=S,
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
centroid=DATA["centroid"],
norm=False,
p=1,
)
# METlab implementation takes the mean, not the sum
assert np.allclose(bw, S.shape[0] * DATA["bw"])
def deprecated_test_beat(infile):
DATA = load(infile)
(bpm, beats) = librosa.beat.beat_track(y=None, sr=8000, hop_length=32, onset_envelope=DATA["onsetenv"][0])
beat_times = librosa.frames_to_time(beats, sr=8000, hop_length=32)
assert np.allclose(beat_times, DATA["beats"])
def test_onset_strength(infile):
DATA = load(infile)
# Compute onset envelope using the same spectrogram
onsets = librosa.onset.onset_strength(
y=None, sr=8000, S=DATA["D"], lag=1, max_size=1, center=False, detrend=True, aggregate=np.mean
)
assert np.allclose(onsets[1:], DATA["onsetenv"][0])
def __test(infile):
DATA = load(infile)
(bpm, beats) = librosa.beat.beat_track(y=None,
sr=8000,
hop_length=32,
onset_envelope=DATA['onsetenv'][0])
beat_times = librosa.frames_to_time(beats, sr=8000, hop_length=32)
assert np.allclose(beat_times, DATA['beats'])
def __test(infile):
DATA = load(infile)
(bpm, beats) = librosa.beat.beat_track(y=None,
sr=8000,
hop_length=32,
onset_envelope=DATA['onsetenv'][0])
beat_times = librosa.frames_to_time(beats, sr=8000, hop_length=32)
assert np.allclose(beat_times, DATA['beats'])
def __test(infile):
DATA = load(infile)
# Estimate tempo from the given onset envelope
tempo = librosa.beat.estimate_tempo(DATA['onsetenv'][0],
sr=8000,
hop_length=32,
start_bpm=120.0)
assert (np.allclose(tempo, DATA['t'][0, 0])
or np.allclose(tempo, DATA['t'][0, 1]))
def __test(infile):
DATA = load(infile)
# Estimate tempo from the given onset envelope
tempo = librosa.beat.estimate_tempo(DATA['onsetenv'][0],
sr=8000,
hop_length=32,
start_bpm=120.0)
assert (np.allclose(tempo, DATA['t'][0, 0]) or
np.allclose(tempo, DATA['t'][0, 1]))
def __test(infile):
DATA = load(infile)
# Compute onset envelope using the same spectrogram
onsets = librosa.onset.onset_strength(y=None,
sr=8000,
S=DATA['D'],
centering=False,
detrend=True)
assert np.allclose(onsets[1:], DATA['onsetenv'][0])
pass
def __test(infile):
DATA = load(infile)
# Compute onset envelope using the same spectrogram
onsets = librosa.onset.onset_strength(y=None,
sr=8000,
S=DATA['D'],
centering=False,
detrend=True)
assert np.allclose(onsets[1:], DATA['onsetenv'][0])
pass
def __test(infile):
DATA = load(infile)
# Compute onset envelope using the same spectrogram
onsets = librosa.onset.onset_strength(y=None,
sr=8000,
S=DATA['D'],
lag=1,
max_size=1,
center=False,
detrend=True,
aggregate=np.mean)
assert np.allclose(onsets[1:], DATA['onsetenv'][0])
pass
def __test(infile):
DATA = load(infile)
y, sr = librosa.load(DATA['wavfile'][0], sr=None, mono=True)
n_fft = DATA['nfft'][0, 0].astype(int)
hop_length = DATA['hop_length'][0, 0].astype(int)
# spectralCentroid uses normalized spectra
S = met_stft(y, n_fft, hop_length, n_fft, True)
centroid = librosa.feature.spectral_centroid(S=S,
sr=sr,
n_fft=n_fft,
hop_length=hop_length)
assert np.allclose(centroid, DATA['centroid'])
def __test(infile):
DATA = load(infile)
y, sr = librosa.load(DATA['wavfile'][0], sr=None, mono=True)
n_fft = DATA['nfft'][0, 0].astype(int)
hop_length = DATA['hop_length'][0, 0].astype(int)
# spectralCentroid uses normalized spectra
S = met_stft(y, n_fft, hop_length, n_fft, True)
centroid = librosa.feature.spectral_centroid(S=S,
sr=sr,
n_fft=n_fft,
hop_length=hop_length)
assert np.allclose(centroid, DATA['centroid'])
def __test(infile):
DATA = load(infile)
y, sr = librosa.load(DATA['wavfile'][0], sr=None, mono=True)
n_fft = DATA['nfft'][0, 0].astype(int)
hop_length = DATA['hop_length'][0, 0].astype(int)
# spectralContrast uses normalized spectra
S = met_stft(y, n_fft, hop_length, n_fft, True)
contrast = librosa.feature.spectral_contrast(S=S, sr=sr,
n_fft=n_fft,
hop_length=hop_length,
linear=True)
assert np.allclose(contrast, DATA['contrast'], rtol=1e-3, atol=1e-2)
def __test(infile):
DATA = load(infile)
y, sr = librosa.load(DATA['wavfile'][0], sr=None, mono=True)
n_fft = DATA['nfft'][0, 0].astype(int)
hop_length = DATA['hop_length'][0, 0].astype(int)
# spectralContrast uses normalized spectra
S = met_stft(y, n_fft, hop_length, n_fft, True)
contrast = librosa.feature.spectral_contrast(S=S, sr=sr,
n_fft=n_fft,
hop_length=hop_length,
linear=True)
assert np.allclose(contrast, DATA['contrast'], rtol=1e-3, atol=1e-2)
def __test(infile):
DATA = load(infile)
y, sr = librosa.load(DATA['wavfile'][0], sr=None, mono=True)
n_fft = DATA['nfft'][0, 0].astype(int)
hop_length = DATA['hop_length'][0, 0].astype(int)
pct = DATA['pct'][0, 0]
# spectralRolloff uses normalized spectra
S = met_stft(y, n_fft, hop_length, n_fft, True)
rolloff = librosa.feature.spectral_rolloff(S=S, sr=sr,
n_fft=n_fft,
hop_length=hop_length,
roll_percent=pct)
assert np.allclose(rolloff, DATA['rolloff'])
def __test(infile):
DATA = load(infile)
y, sr = librosa.load(DATA['wavfile'][0], sr=None, mono=True)
n_fft = DATA['nfft'][0, 0].astype(int)
hop_length = DATA['hop_length'][0, 0].astype(int)
pct = DATA['pct'][0, 0]
# spectralRolloff uses normalized spectra
S = met_stft(y, n_fft, hop_length, n_fft, True)
rolloff = librosa.feature.spectral_rolloff(S=S, sr=sr,
n_fft=n_fft,
hop_length=hop_length,
roll_percent=pct)
assert np.allclose(rolloff, DATA['rolloff'])
def test_spectral_centroid(infile):
DATA = load(infile)
y, sr = librosa.load(os.path.join("tests", DATA["wavfile"][0]),
sr=None,
mono=True)
n_fft = DATA["nfft"][0, 0].astype(int)
hop_length = DATA["hop_length"][0, 0].astype(int)
# spectralCentroid uses normalized spectra
S = met_stft(y, n_fft, hop_length, n_fft, True)
centroid = librosa.feature.spectral_centroid(S=S,
sr=sr,
n_fft=n_fft,
hop_length=hop_length)
assert np.allclose(centroid, DATA["centroid"])
def test_spectral_contrast(infile):
DATA = load(infile)
y, sr = librosa.load(os.path.join("tests", DATA["wavfile"][0]),
sr=None,
mono=True)
n_fft = DATA["nfft"][0, 0].astype(int)
hop_length = DATA["hop_length"][0, 0].astype(int)
# spectralContrast uses normalized spectra
S = met_stft(y, n_fft, hop_length, n_fft, True)
contrast = librosa.feature.spectral_contrast(S=S,
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
linear=True)
assert np.allclose(contrast, DATA["contrast"], rtol=1e-3, atol=1e-2)
def test_spectral_rolloff(infile):
DATA = load(infile)
y, sr = librosa.load(os.path.join("tests", DATA["wavfile"][0]),
sr=None,
mono=True)
n_fft = DATA["nfft"][0, 0].astype(int)
hop_length = DATA["hop_length"][0, 0].astype(int)
pct = DATA["pct"][0, 0]
# spectralRolloff uses normalized spectra
S = met_stft(y, n_fft, hop_length, n_fft, True)
rolloff = librosa.feature.spectral_rolloff(S=S,
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
roll_percent=pct)
assert np.allclose(rolloff, DATA["rolloff"])
def __test(infile):
DATA = load(infile)
y, sr = librosa.load(DATA['wavfile'][0], sr=None, mono=True)
n_fft = DATA['nfft'][0, 0].astype(int)
hop_length = DATA['hop_length'][0, 0].astype(int)
S = DATA['S']
# normalization is disabled here, since the precomputed S is already
# normalized
# metlab uses p=1, other folks use p=2
bw = librosa.feature.spectral_bandwidth(S=S, sr=sr,
n_fft=n_fft,
hop_length=hop_length,
centroid=DATA['centroid'],
norm=False,
p=1)
# METlab implementation takes the mean, not the sum
assert np.allclose(bw, S.shape[0] * DATA['bw'])
