/
test_features.py
806 lines (586 loc) · 26.8 KB
/
test_features.py
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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from __future__ import print_function
import warnings
import numpy as np
import pytest
import librosa
from test_core import load, srand
# Disable cache
import os
try:
os.environ.pop("LIBROSA_CACHE_DIR")
except KeyError:
pass
__EXAMPLE_FILE = os.path.join("tests", "data", "test1_22050.wav")
warnings.resetwarnings()
warnings.simplefilter("always")
warnings.filterwarnings("module", ".*", FutureWarning, "scipy.*")
# utils submodule
@pytest.mark.parametrize("slope", np.linspace(-2, 2, num=6))
@pytest.mark.parametrize("xin", [np.vstack([np.arange(100.0)] * 3)])
@pytest.mark.parametrize("order", [1])
@pytest.mark.parametrize("width, axis", [(3, 0), (3, 1), (5, 1), (7, 1)])
@pytest.mark.parametrize("bias", [-10, 0, 10])
def test_delta(xin, width, slope, order, axis, bias):
x = slope * xin + bias
# Note: this test currently only checks first-order differences
# if width < 3 or np.mod(width, 2) != 1 or width > x.shape[axis]:
# pytest.raises(librosa.ParameterError)
delta = librosa.feature.delta(x, width=width, order=order, axis=axis)
# Check that trimming matches the expected shape
assert x.shape == delta.shape
# Once we're sufficiently far into the signal (ie beyond half_len)
# (x + delta)[t] should approximate x[t+1] if x is actually linear
slice_orig = [slice(None)] * x.ndim
slice_out = [slice(None)] * delta.ndim
slice_orig[axis] = slice(width // 2 + 1, -width // 2 + 1)
slice_out[axis] = slice(width // 2, -width // 2)
assert np.allclose((x + delta)[tuple(slice_out)], x[tuple(slice_orig)])
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_delta_badorder():
x = np.ones((10, 10))
librosa.feature.delta(x, order=0)
@pytest.mark.xfail(raises=librosa.ParameterError)
@pytest.mark.parametrize("x", [np.ones((3, 100))])
@pytest.mark.parametrize(
"width, axis",
[(-1, 0), (-1, 1), (0, 0), (0, 1), (1, 0), (1, 1), (2, 0), (2, 1), (4, 0), (4, 1), (5, 0), (6, 0), (6, 1), (7, 0)],
)
def test_delta_badwidthaxis(x, width, axis):
librosa.feature.delta(x, width=width, axis=axis)
@pytest.mark.parametrize("data", [np.arange(5.), np.remainder(np.arange(10000), 24)])
@pytest.mark.parametrize("delay", [-4, -2, -1, 1, 2, 4])
@pytest.mark.parametrize("n_steps", [1, 2, 3, 300])
def test_stack_memory(data, n_steps, delay):
data_stack = librosa.feature.stack_memory(data, n_steps=n_steps, delay=delay)
# If we're one-dimensional, reshape for testing
if data.ndim == 1:
data = data.reshape((1, -1))
d, t = data.shape
assert data_stack.shape[0] == n_steps * d
assert data_stack.shape[1] == t
assert np.allclose(data_stack[0], data[0])
for i in range(d):
for step in range(1, n_steps):
if delay > 0:
assert np.allclose(data[i, : -step * delay], data_stack[step * d + i, step * delay:])
else:
assert np.allclose(data[i, -step * delay:], data_stack[step * d + i, : step * delay])
assert np.max(data) + 1e-7 >= np.max(data_stack)
assert np.min(data) - 1e-7 <= np.min(data_stack)
@pytest.mark.parametrize("n_steps,delay", [(0, 1), (-1, 1), (1, 0)])
@pytest.mark.parametrize("data", [np.zeros((2, 2))])
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_stack_memory_fail(data, n_steps, delay):
librosa.feature.stack_memory(data, n_steps=n_steps, delay=delay)
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_stack_memory_ndim_toobig():
librosa.feature.stack_memory(np.zeros((2,2,2)), n_steps=3, delay=1)
@pytest.mark.parametrize('data', [np.zeros((2, 0))])
@pytest.mark.xfail(raises=librosa.ParameterError)
@pytest.mark.parametrize('delay', [-2, -1, 1, 2])
@pytest.mark.parametrize('n_steps', [1, 2])
def test_stack_memory_ndim_badshape(data, delay, n_steps):
librosa.feature.stack_memory(data, n_steps=n_steps, delay=delay)
@pytest.fixture(scope="module")
def S_ideal():
# An idealized spectrum with all zero energy except at one DFT band
S = np.zeros((513, 3))
S[5, :] = 1.0
return S
# spectral submodule
@pytest.mark.parametrize(
"freq",
[
None,
librosa.fft_frequencies(sr=22050, n_fft=1024),
3 * librosa.fft_frequencies(sr=22050, n_fft=1024),
np.random.randn(513, 3),
],
)
def test_spectral_centroid_synthetic(S_ideal, freq):
n_fft = 2 * (S_ideal.shape[0] - 1)
cent = librosa.feature.spectral_centroid(S=S_ideal, freq=freq)
if freq is None:
freq = librosa.fft_frequencies(sr=22050, n_fft=n_fft)
assert np.allclose(cent, freq[5])
@pytest.mark.parametrize("S", [-np.ones((9, 3)), -np.ones((9, 3)) * 1.0j])
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_spectral_centroid_errors(S):
librosa.feature.spectral_centroid(S=S)
@pytest.mark.parametrize("sr", [22050])
@pytest.mark.parametrize("y,S", [(np.zeros(3 * 22050), None), (None, np.zeros((1025, 10)))])
def test_spectral_centroid_empty(y, sr, S):
cent = librosa.feature.spectral_centroid(y=y, sr=sr, S=S)
assert not np.any(cent)
@pytest.mark.parametrize(
"freq",
[
None,
librosa.fft_frequencies(sr=22050, n_fft=1024),
3 * librosa.fft_frequencies(sr=22050, n_fft=1024),
np.random.randn(513, 3),
],
)
@pytest.mark.parametrize("norm", [False, True])
@pytest.mark.parametrize("p", [1, 2])
def test_spectral_bandwidth_synthetic(S_ideal, freq, norm, p):
# This test ensures that a signal confined to a single frequency bin
# always achieves 0 bandwidth
bw = librosa.feature.spectral_bandwidth(S=S_ideal, freq=freq, norm=norm, p=p)
assert not np.any(bw)
@pytest.mark.parametrize(
"freq",
[
None,
librosa.fft_frequencies(sr=22050, n_fft=1024),
3 * librosa.fft_frequencies(sr=22050, n_fft=1024),
np.random.randn(513, 1),
],
)
def test_spectral_bandwidth_onecol(S_ideal, freq):
# This test checks for issue https://github.com/librosa/librosa/issues/552
# failure when the spectrogram has a single column
bw = librosa.feature.spectral_bandwidth(S=S_ideal[:, :1], freq=freq)
assert bw.shape == (1, 1)
@pytest.mark.xfail(raises=librosa.ParameterError)
@pytest.mark.parametrize("S", [-np.ones((17, 2)), -np.ones((17, 2)) * 1.0j])
def test_spectral_bandwidth_errors(S):
librosa.feature.spectral_bandwidth(S=S)
@pytest.mark.parametrize("S", [np.ones((1025, 3))])
@pytest.mark.parametrize(
"freq", [None, librosa.fft_frequencies(sr=22050, n_fft=2048), np.cumsum(np.abs(np.random.randn(1025, 3)), axis=0)]
)
@pytest.mark.parametrize("pct", [0.25, 0.5, 0.95])
def test_spectral_rolloff_synthetic(S, freq, pct):
sr = 22050
rolloff = librosa.feature.spectral_rolloff(S=S, sr=sr, freq=freq, roll_percent=pct)
n_fft = 2 * (S.shape[0] - 1)
if freq is None:
freq = librosa.fft_frequencies(sr=sr, n_fft=n_fft)
idx = np.floor(pct * freq.shape[0]).astype(int)
assert np.allclose(rolloff, freq[idx])
@pytest.mark.xfail(raises=librosa.ParameterError)
@pytest.mark.parametrize(
"S,pct",
[(-np.ones((513, 3)), 0.95), (-np.ones((513, 3)) * 1.0j, 0.95), (np.ones((513, 3)), -1), (np.ones((513, 3)), 2)],
)
def test_spectral_rolloff_errors(S, pct):
librosa.feature.spectral_rolloff(S=S, roll_percent=pct)
@pytest.fixture(scope="module")
def y_ex():
return librosa.load(os.path.join("tests", "data", "test1_22050.wav"))
def test_spectral_contrast_log(y_ex):
# We already have a regression test for linear energy difference
# This test just does a sanity-check on the log-scaled version
y, sr = y_ex
contrast = librosa.feature.spectral_contrast(y=y, sr=sr, linear=False)
assert not np.any(contrast < 0)
@pytest.mark.parametrize("S", [np.ones((1025, 10))])
@pytest.mark.parametrize(
"freq,fmin,n_bands,quantile",
[
(0, 200, 6, 0.02),
(np.zeros(1 + 1025), 200, 6, 0.02),
(np.zeros((1025, 10)), 200, 6, 0.02),
(None, -1, 6, 0.02),
(None, 0, 6, 0.02),
(None, 200, -1, 0.02),
(None, 200, 6, -1),
(None, 200, 6, 2),
(None, 200, 7, 0.02),
],
)
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_spectral_contrast_errors(S, freq, fmin, n_bands, quantile):
librosa.feature.spectral_contrast(S=S, freq=freq, fmin=fmin, n_bands=n_bands, quantile=quantile)
@pytest.mark.parametrize(
"S,flatness_ref",
[
(np.array([[1, 3], [2, 1], [1, 2]]), np.array([[0.7937005259, 0.7075558390]])),
(np.ones((1025, 2)), np.ones((1, 2))),
(np.zeros((1025, 2)), np.ones((1, 2))),
],
)
def test_spectral_flatness_synthetic(S, flatness_ref):
flatness = librosa.feature.spectral_flatness(S=S)
assert np.allclose(flatness, flatness_ref)
@pytest.mark.parametrize("S", [np.ones((1025, 2))])
@pytest.mark.parametrize("amin", [0, -1])
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_spectral_flatness_errors(S, amin):
librosa.feature.spectral_flatness(S=S, amin=amin)
@pytest.mark.parametrize("S", [-np.ones((1025, 2)), -np.ones((1025, 2)) * 1.0j])
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_spectral_flatness_badtype(S):
librosa.feature.spectral_flatness(S=S)
@pytest.mark.parametrize("n", range(10, 100, 10))
def test_rms_const(n):
S = np.ones((n, 5))
# RMSE of an all-ones band is 1
frame_length = 2 * (n - 1)
rms = librosa.feature.rms(S=S, frame_length=frame_length)
assert np.allclose(rms, np.ones_like(rms) / np.sqrt(frame_length), atol=1e-2)
@pytest.mark.parametrize("frame_length", [2048, 2049, 4096, 4097])
@pytest.mark.parametrize("hop_length", [128, 512, 1024])
@pytest.mark.parametrize("center", [False, True])
@pytest.mark.parametrize("y2", [np.random.randn(100000)])
def test_rms(y_ex, y2, frame_length, hop_length, center):
y1, sr = y_ex
# Ensure audio is divisible into frame size.
y1 = librosa.util.fix_length(y1, y1.size - y1.size % frame_length)
y2 = librosa.util.fix_length(y2, y2.size - y2.size % frame_length)
assert y1.size % frame_length == 0
assert y2.size % frame_length == 0
# STFT magnitudes with a constant windowing function and no centering.
S1 = librosa.magphase(librosa.stft(y1, n_fft=frame_length,
hop_length=hop_length, window=np.ones, center=center))[0]
S2 = librosa.magphase(librosa.stft(y2, n_fft=frame_length,
hop_length=hop_length, window=np.ones, center=center))[0]
# Try both RMS methods.
rms1 = librosa.feature.rms(S=S1, frame_length=frame_length, hop_length=hop_length)
rms2 = librosa.feature.rms(y=y1, frame_length=frame_length, hop_length=hop_length, center=center)
rms3 = librosa.feature.rms(S=S2, frame_length=frame_length, hop_length=hop_length)
rms4 = librosa.feature.rms(y=y2, frame_length=frame_length, hop_length=hop_length, center=center)
assert rms1.shape == rms2.shape
assert rms3.shape == rms4.shape
# Ensure results are similar.
np.testing.assert_allclose(rms1, rms2, atol=5e-4)
np.testing.assert_allclose(rms3, rms4, atol=5e-4)
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_rms_noinput():
librosa.feature.rms(y=None, S=None)
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_rms_badshape():
S = np.empty((100, 3))
librosa.feature.rms(S=S, frame_length=100)
@pytest.fixture(params=[32, 16, 8, 4, 2], scope="module")
def y_zcr(request):
sr = 16384
period = request.param
y = np.ones(sr)
y[::period] = -1
rate = 2.0 / period
return y, sr, rate
@pytest.mark.parametrize("frame_length", [513, 2049])
@pytest.mark.parametrize("hop_length", [128, 256])
@pytest.mark.parametrize("center", [False, True])
def test_zcr_synthetic(y_zcr, frame_length, hop_length, center):
y, sr, rate = y_zcr
zcr = librosa.feature.zero_crossing_rate(y, frame_length=frame_length, hop_length=hop_length, center=center)
# We don't care too much about the edges if there's padding
if center:
zcr = zcr[:, frame_length // 2: -frame_length // 2]
# We'll allow 1% relative error
assert np.allclose(zcr, rate, rtol=1e-2)
@pytest.fixture(scope="module", params=[1, 2])
def poly_order(request):
return request.param
@pytest.fixture(scope="module")
def poly_coeffs(poly_order):
return np.atleast_1d(np.arange(1, 1 + poly_order))
@pytest.fixture(scope="module", params=[None, 1, 2, -1, "varying"])
def poly_freq(request):
srand()
freq = librosa.fft_frequencies()
if request.param in (1, 2):
return freq ** request.param
elif request.param == -1:
return np.cumsum(np.abs(np.random.randn(1 + 2048 // 2)), axis=0)
elif request.param == "varying":
return np.cumsum(np.abs(np.random.randn(1 + 2048 // 2, 5)), axis=0)
else:
return None
@pytest.fixture(scope="module")
def poly_S(poly_coeffs, poly_freq):
if poly_freq is None:
poly_freq = librosa.fft_frequencies()
S = np.zeros_like(poly_freq)
for i, c in enumerate(poly_coeffs):
S += c * poly_freq ** i
return S.reshape((poly_freq.shape[0], -1))
def test_poly_features_synthetic(poly_S, poly_coeffs, poly_freq):
sr = 22050
n_fft = 2048
order = poly_coeffs.shape[0] - 1
p = librosa.feature.poly_features(S=poly_S, sr=sr, n_fft=n_fft, order=order, freq=poly_freq)
for i in range(poly_S.shape[-1]):
assert np.allclose(poly_coeffs, p[::-1, i].squeeze())
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_tonnetz_fail_empty():
librosa.feature.tonnetz(y=None, chroma=None)
def test_tonnetz_audio(y_ex):
y, sr = y_ex
tonnetz = librosa.feature.tonnetz(y=y, sr=sr)
assert tonnetz.shape[0] == 6
def test_tonnetz_cqt(y_ex):
y, sr = y_ex
chroma_cqt = librosa.feature.chroma_cqt(y=y, sr=sr, n_chroma=24)
tonnetz = librosa.feature.tonnetz(chroma=chroma_cqt, sr=sr)
assert tonnetz.shape[1] == chroma_cqt.shape[1]
assert tonnetz.shape[0] == 6
def test_tonnetz_msaf():
# Use pre-computed chroma
tonnetz_chroma = np.load(os.path.join("tests", "data", "feature-tonnetz-chroma.npy"))
tonnetz_msaf = np.load(os.path.join("tests", "data", "feature-tonnetz-msaf.npy"))
tonnetz = librosa.feature.tonnetz(chroma=tonnetz_chroma)
assert tonnetz.shape[1] == tonnetz_chroma.shape[1]
assert tonnetz.shape[0] == 6
assert np.allclose(tonnetz_msaf, tonnetz)
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_tempogram_fail_noinput():
librosa.feature.tempogram(y=None, onset_envelope=None)
@pytest.mark.parametrize("y", [np.zeros(10 * 1000)])
@pytest.mark.parametrize("sr", [1000])
@pytest.mark.parametrize("win_length,window", [(-384, "hann"), (0, "hann"), (384, np.ones(3))])
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_tempogram_fail_badwin(y, sr, win_length, window):
librosa.feature.tempogram(y=y, sr=sr, win_length=win_length, window=window)
@pytest.mark.parametrize("hop_length", [512, 1024])
def test_tempogram_audio(y_ex, hop_length):
y, sr = y_ex
oenv = librosa.onset.onset_strength(y=y, sr=sr, hop_length=hop_length)
# Get the tempogram from audio
t1 = librosa.feature.tempogram(y=y, sr=sr, onset_envelope=None, hop_length=hop_length)
# Get the tempogram from oenv
t2 = librosa.feature.tempogram(y=None, sr=sr, onset_envelope=oenv, hop_length=hop_length)
# Make sure it works when both are provided
t3 = librosa.feature.tempogram(y=y, sr=sr, onset_envelope=oenv, hop_length=hop_length)
# And that oenv overrides y
t4 = librosa.feature.tempogram(y=0 * y, sr=sr, onset_envelope=oenv, hop_length=hop_length)
assert np.allclose(t1, t2)
assert np.allclose(t1, t3)
assert np.allclose(t1, t4)
@pytest.mark.parametrize("tempo", [60, 120, 200])
@pytest.mark.parametrize("center", [False, True])
def test_tempogram_odf_equiv(tempo, center):
sr = 22050
hop_length = 512
duration = 8
odf = np.zeros(duration * sr // hop_length)
spacing = sr * 60.0 // (hop_length * tempo)
odf[:: int(spacing)] = 1
odf_ac = librosa.autocorrelate(odf)
tempogram = librosa.feature.tempogram(
onset_envelope=odf, sr=sr, hop_length=hop_length, win_length=len(odf), window=np.ones, center=center, norm=None
)
idx = 0
if center:
idx = len(odf) // 2
assert np.allclose(odf_ac, tempogram[:, idx])
@pytest.mark.parametrize("tempo", [60, 90, 200])
@pytest.mark.parametrize("win_length", [192, 384])
@pytest.mark.parametrize("window", ["hann", np.ones])
@pytest.mark.parametrize("norm", [None, 1, 2, np.inf])
def test_tempogram_odf_peak(tempo, win_length, window, norm):
sr = 22050
hop_length = 512
duration = 8
# Generate an evenly-spaced pulse train
odf = np.zeros(duration * sr // hop_length)
spacing = sr * 60.0 // (hop_length * tempo)
odf[:: int(spacing)] = 1
tempogram = librosa.feature.tempogram(
onset_envelope=odf, sr=sr, hop_length=hop_length, win_length=win_length, window=window, norm=norm
)
# Check the shape of the output
assert tempogram.shape[0] == win_length
assert tempogram.shape[1] == len(odf)
# Mean over time to wash over the boundary padding effects
idx = np.where(librosa.util.localmax(tempogram.max(axis=1)))[0]
# Indices should all be non-zero integer multiples of spacing
assert np.allclose(idx, spacing * np.arange(1, 1 + len(idx)))
@pytest.mark.parametrize("center", [False, True])
@pytest.mark.parametrize("win_length", [192, 384])
@pytest.mark.parametrize("window", ["hann", np.ones])
@pytest.mark.parametrize("norm", [None, 1, 2, np.inf])
def test_tempogram_odf_multi(center, win_length, window, norm):
sr = 22050
hop_length = 512
duration = 8
# Generate an evenly-spaced pulse train
odf = np.zeros((10, duration * sr // hop_length))
for i in range(10):
spacing = sr * 60.0 // (hop_length * (60 + 12 * i))
odf[i, :: int(spacing)] = 1
tempogram = librosa.feature.tempogram(
onset_envelope=odf, sr=sr, hop_length=hop_length, win_length=win_length, window=window, norm=norm
)
for i in range(10):
tg_local = librosa.feature.tempogram(
onset_envelope=odf[i], sr=sr, hop_length=hop_length, win_length=win_length, window=window, norm=norm
)
assert np.allclose(tempogram[i], tg_local)
@pytest.mark.parametrize("y", [np.zeros(10 * 1000)])
@pytest.mark.parametrize("sr", [1000])
@pytest.mark.parametrize("win_length,window", [(-384, "hann"), (0, "hann"), (384, np.ones(3))])
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_fourier_tempogram_fail_badwin(y, sr, win_length, window):
librosa.feature.fourier_tempogram(y=y, sr=sr, win_length=win_length, window=window)
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_fourier_tempogram_fail_noinput():
librosa.feature.fourier_tempogram(y=None, onset_envelope=None)
@pytest.mark.parametrize("hop_length", [512, 1024])
def test_fourier_tempogram_audio(y_ex, hop_length):
y, sr = y_ex
oenv = librosa.onset.onset_strength(y=y, sr=sr, hop_length=hop_length)
# Get the tempogram from audio
t1 = librosa.feature.fourier_tempogram(y=y, sr=sr, onset_envelope=None, hop_length=hop_length)
# Get the tempogram from oenv
t2 = librosa.feature.fourier_tempogram(y=None, sr=sr, onset_envelope=oenv, hop_length=hop_length)
# Make sure it works when both are provided
t3 = librosa.feature.fourier_tempogram(y=y, sr=sr, onset_envelope=oenv, hop_length=hop_length)
# And that oenv overrides y
t4 = librosa.feature.fourier_tempogram(y=0 * y, sr=sr, onset_envelope=oenv, hop_length=hop_length)
assert np.iscomplexobj(t1)
assert np.allclose(t1, t2)
assert np.allclose(t1, t3)
assert np.allclose(t1, t4)
@pytest.mark.parametrize("sr", [22050])
@pytest.mark.parametrize("hop_length", [512])
@pytest.mark.parametrize("win_length", [192, 384])
@pytest.mark.parametrize("center", [False, True])
@pytest.mark.parametrize("window", ["hann", np.ones])
def test_fourier_tempogram_invert(sr, hop_length, win_length, center, window):
duration = 16
tempo = 100
odf = np.zeros(duration * sr // hop_length, dtype=np.float32)
spacing = sr * 60.0 // (hop_length * tempo)
odf[:: int(spacing)] = 1
tempogram = librosa.feature.fourier_tempogram(
onset_envelope=odf, sr=sr, hop_length=hop_length, win_length=win_length, window=window, center=center
)
if center:
sl = slice(None)
else:
sl = slice(win_length // 2, -win_length // 2)
odf_inv = librosa.istft(tempogram, hop_length=1, center=center, window=window, length=len(odf))
assert np.allclose(odf_inv[sl], odf[sl], atol=1e-6)
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
@pytest.mark.xfail(raises=librosa.ParameterError)
@pytest.mark.parametrize("win_len_smooth", [-1, 0, 1.5, "foo"])
def test_cens_fail(y_ex, win_len_smooth):
y, sr = y_ex
librosa.feature.chroma_cens(y=y, sr=sr, win_len_smooth=win_len_smooth)
@pytest.mark.parametrize("S", [librosa.power_to_db(np.random.randn(128, 1) ** 2, ref=np.max)])
@pytest.mark.parametrize("dct_type", [1, 2, 3])
@pytest.mark.parametrize("norm", [None, "ortho"])
@pytest.mark.parametrize("n_mfcc", [13, 20])
@pytest.mark.parametrize("lifter", [0, 13])
def test_mfcc(S, dct_type, norm, n_mfcc, lifter):
E_total = np.sum(S, axis=0)
mfcc = librosa.feature.mfcc(S=S, dct_type=dct_type, norm=norm, n_mfcc=n_mfcc, lifter=lifter)
assert mfcc.shape[0] == n_mfcc
assert mfcc.shape[1] == S.shape[1]
# In type-2 mode, DC component should be constant over all frames
if dct_type == 2:
assert np.var(mfcc[0] / E_total) <= 1e-29
# This test is no longer relevant since scipy 1.2.0
# @pytest.mark.xfail(raises=NotImplementedError)
# def test_mfcc_dct1_ortho():
# S = np.ones((65, 3))
# librosa.feature.mfcc(S=S, dct_type=1, norm='ortho')
@pytest.mark.xfail(raises=librosa.ParameterError)
@pytest.mark.parametrize("lifter", [-1, np.nan])
def test_mfcc_badlifter(lifter):
S = np.random.randn(128, 100) ** 2
librosa.feature.mfcc(S=S, lifter=lifter)
# -- feature inversion tests
@pytest.mark.parametrize("power", [1, 2])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("n_fft", [1024, 2048])
def test_mel_to_stft(power, dtype, n_fft):
srand()
# Make a random mel spectrum, 4 frames
mel_basis = librosa.filters.mel(22050, n_fft, n_mels=128, dtype=dtype)
stft_orig = np.random.randn(n_fft // 2 + 1, 4) ** power
mels = mel_basis.dot(stft_orig.astype(dtype))
stft = librosa.feature.inverse.mel_to_stft(mels, power=power, n_fft=n_fft)
# Check precision
assert stft.dtype == dtype
# Check for non-negative spectrum
assert np.all(stft >= 0)
# Check that the shape is good
assert stft.shape[0] == 1 + n_fft // 2
# Check that the approximation is good in RMSE terms
assert np.sqrt(np.mean((mel_basis.dot(stft ** power) - mels) ** 2)) <= 5e-2
def test_mel_to_audio():
y = librosa.tone(440.0, sr=22050, duration=1)
M = librosa.feature.melspectrogram(y=y, sr=22050)
y_inv = librosa.feature.inverse.mel_to_audio(M, sr=22050, length=len(y))
# Sanity check the length
assert len(y) == len(y_inv)
# And that it's valid audio
assert librosa.util.valid_audio(y_inv)
@pytest.mark.parametrize("n_mfcc", [13, 20])
@pytest.mark.parametrize("n_mels", [64, 128])
@pytest.mark.parametrize("dct_type", [2, 3])
@pytest.mark.parametrize("lifter", [-1, 0, 1, 2, 3])
@pytest.mark.parametrize("y", [librosa.tone(440.0, sr=22050, duration=1)])
def test_mfcc_to_mel(y, n_mfcc, n_mels, dct_type, lifter):
mfcc = librosa.feature.mfcc(y=y, sr=22050, n_mels=n_mels, n_mfcc=n_mfcc, dct_type=dct_type)
# check lifter parameter error
if lifter < 0:
with pytest.raises(librosa.ParameterError):
librosa.feature.inverse.mfcc_to_mel(mfcc * 10 ** 3, n_mels=n_mels, dct_type=dct_type, lifter=lifter)
# check no lifter computations
elif lifter == 0:
melspec = librosa.feature.melspectrogram(y=y, sr=22050, n_mels=n_mels)
mel_recover = librosa.feature.inverse.mfcc_to_mel(mfcc, n_mels=n_mels, dct_type=dct_type)
# Quick shape check
assert melspec.shape == mel_recover.shape
# Check non-negativity
assert np.all(mel_recover >= 0)
# check that runtime warnings are triggered when appropriate
elif lifter == 2:
with pytest.warns(UserWarning):
librosa.feature.inverse.mfcc_to_mel(mfcc * 10 ** 3, n_mels=n_mels, dct_type=dct_type, lifter=lifter)
# check if mfcc_to_mel works correctly with lifter
else:
ones = np.ones(mfcc.shape, dtype=mfcc.dtype)
n_mfcc = mfcc.shape[0]
idx = np.arange(1, 1 + n_mfcc, dtype=mfcc.dtype)
lifter_sine = 1 + lifter * 0.5 * np.sin(np.pi * idx / lifter)[:, np.newaxis]
# compute the recovered mel
mel_recover = librosa.feature.inverse.mfcc_to_mel(
ones * lifter_sine, n_mels=n_mels, dct_type=dct_type, lifter=lifter
)
# compute the expected mel
mel_expected = librosa.feature.inverse.mfcc_to_mel(ones, n_mels=n_mels, dct_type=dct_type, lifter=0)
# assert equality of expected and recovered mels
np.testing.assert_almost_equal(mel_recover, mel_expected, 3)
@pytest.mark.parametrize("n_mfcc", [13, 20])
@pytest.mark.parametrize("n_mels", [64, 128])
@pytest.mark.parametrize("dct_type", [2, 3])
@pytest.mark.parametrize("lifter", [0, 3])
@pytest.mark.parametrize("y", [librosa.tone(440.0, sr=22050, duration=1)])
def test_mfcc_to_audio(y, n_mfcc, n_mels, dct_type, lifter):
mfcc = librosa.feature.mfcc(y=y, sr=22050, n_mels=n_mels, n_mfcc=n_mfcc, dct_type=dct_type)
y_inv = librosa.feature.inverse.mfcc_to_audio(mfcc, n_mels=n_mels, dct_type=dct_type, lifter=lifter, length=len(y))
# Sanity check the length
assert len(y) == len(y_inv)
# And that it's valid audio
assert librosa.util.valid_audio(y_inv)