def test_spectral_bandwidth():
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'])
for infile in files(os.path.join('data', 'met-bandwidth-*.mat')):
yield __test, infile
def test_spectral_bandwidth():
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'])
for infile in files('data/met-bandwidth-*.mat'):
yield __test, infile
def test_spectral_rolloff():
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)
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'])
for infile in files(os.path.join('tests', 'data', 'met-rolloff-*.mat')):
yield __test, infile
def test_tempo():
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]))
for infile in files('data/beat-tempo-*.mat'):
yield (__test, infile)
def test_tempo():
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]))
for infile in files('data/beat-tempo-*.mat'):
yield (__test, infile)
def deprecated_test_beat():
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'])
for infile in files('data/beat-beat-*.mat'):
yield (__test, infile)
def deprecated_test_beat():
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'])
for infile in files(os.path.join('data', 'beat-beat-*.mat')):
yield (__test, infile)
def test_onset_strength():
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
for infile in files('data/beat-onset-*.mat'):
yield (__test, infile)
def test_onset_strength():
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
for infile in files('data/beat-onset-*.mat'):
yield (__test, infile)
def test_onset_strength():
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
for infile in files(os.path.join('data', 'beat-onset-*.mat')):
yield (__test, infile)
def test_spectral_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'])
for infile in files(os.path.join('data', 'met-centroid-*.mat')):
yield __test, infile
def test_spectral_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'])
for infile in files('data/met-centroid-*.mat'):
yield __test, infile
def test_onset_strength():
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
for infile in files(os.path.join('data','beat-onset-*.mat')):
yield (__test, infile)
def test_spectral_contrast():
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)
for infile in files('data/met-contrast-*.mat'):
yield __test, infile
def test_spectral_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'])
for infile in files('data/met-rolloff-*.mat'):
yield __test, infile
def test_spectral_contrast():
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)
for infile in files(os.path.join('data', 'met-contrast-*.mat')):
yield __test, infile
except:
pass
import pytest
import numpy as np
import scipy.stats
import librosa
from test_core import files, load
__EXAMPLE_FILE = os.path.join('tests', 'data', 'test1_22050.wav')
@pytest.mark.parametrize('infile',
files(os.path.join('data', 'beat-onset-*.mat')))
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])
import numpy as np
import scipy.stats
import librosa
from test_core import files, load
__EXAMPLE_FILE = os.path.join("tests", "data", "test1_22050.wav")
@pytest.fixture(scope="module", params=[22050, 44100])
def ysr(request):
return librosa.load(__EXAMPLE_FILE, sr=request.param)
@pytest.mark.parametrize("infile",
files(os.path.join("data", "beat-onset-*.mat")))
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,
)
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=scipy.signal.hamming,
center=False,
))
if normalize:
S = S / (S[0] + np.sum(2 * S[1:], axis=0))
return S
@pytest.mark.parametrize("infile",
files(
os.path.join("tests", "data",
"met-centroid-*.mat")))
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,
import numpy as np
import scipy.stats
import librosa
from test_core import files, load
__EXAMPLE_FILE = os.path.join("tests", "data", "test1_22050.wav")
@pytest.fixture(scope="module", params=[22050, 44100])
def ysr(request):
return librosa.load(__EXAMPLE_FILE, sr=request.param)
@pytest.mark.parametrize("infile", files(os.path.join("data", "beat-onset-*.mat")))
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])
@pytest.mark.parametrize("tempo", [60, 80, 110, 160])
@pytest.mark.parametrize("sr", [22050, 44100])
@pytest.mark.parametrize("hop_length", [512, 1024])