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test_onset.py
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test_onset.py
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#!/usr/bin/env python
# CREATED:2013-03-11 18:14:30 by Brian McFee <brm2132@columbia.edu>
# unit tests for librosa.onset
from __future__ import print_function
import pytest
# Disable cache
import os
try:
os.environ.pop('LIBROSA_CACHE_DIR')
except:
pass
import warnings
import numpy as np
import librosa
from test_core import srand
__EXAMPLE_FILE = os.path.join('tests', 'data', 'test1_22050.wav')
def test_onset_strength_audio():
def __test(y, sr, feature, n_fft, hop_length, lag, max_size, detrend, center, aggregate):
oenv = librosa.onset.onset_strength(y=y, sr=sr,
S=None,
detrend=detrend,
center=center,
aggregate=aggregate,
feature=feature,
n_fft=n_fft,
hop_length=hop_length,
lag=lag,
max_size=max_size)
assert oenv.ndim == 1
S = librosa.feature.melspectrogram(y=y,
n_fft=n_fft,
hop_length=hop_length)
target_shape = S.shape[-1]
if not detrend:
assert np.all(oenv >= 0)
assert oenv.shape[-1] == target_shape
y, sr = librosa.load(__EXAMPLE_FILE)
for feature in [None,
librosa.feature.melspectrogram,
librosa.feature.chroma_stft]:
for n_fft in [512, 2048]:
for hop_length in [n_fft // 2, n_fft // 4]:
for lag in [0, 1, 2]:
for max_size in [0, 1, 2]:
for detrend in [False, True]:
for center in [False, True]:
for aggregate in [None, np.mean, np.max]:
if lag < 1 or max_size < 1:
tf = pytest.mark.xfail(__test, raises=librosa.ParameterError)
else:
tf = __test
yield (tf, y, sr, feature, n_fft,
hop_length, lag, max_size, detrend, center, aggregate)
tf = pytest.mark.xfail(__test, raises=librosa.ParameterError)
yield (tf, None, sr, feature, n_fft,
hop_length, lag, max_size, detrend, center, aggregate)
def test_onset_strength_spectrogram():
def __test(S, sr, feature, n_fft, hop_length, detrend, center):
oenv = librosa.onset.onset_strength(y=None, sr=sr,
S=S,
detrend=detrend,
center=center,
aggregate=aggregate,
feature=feature,
n_fft=n_fft,
hop_length=hop_length)
assert oenv.ndim == 1
target_shape = S.shape[-1]
if not detrend:
assert np.all(oenv >= 0)
assert oenv.shape[-1] == target_shape
y, sr = librosa.load(__EXAMPLE_FILE)
S = librosa.feature.melspectrogram(y=y, sr=sr)
for feature in [None,
librosa.feature.melspectrogram,
librosa.feature.chroma_stft]:
for n_fft in [512, 2048]:
for hop_length in [n_fft // 2, n_fft // 4]:
for detrend in [False, True]:
for center in [False, True]:
for aggregate in [None, np.mean, np.max]:
yield (__test, S, sr, feature, n_fft,
hop_length, detrend, center)
tf = pytest.mark.xfail(__test, raises=librosa.ParameterError)
yield (tf, None, sr, feature, n_fft,
hop_length, detrend, center)
def test_onset_strength_multi_noagg():
y, sr = librosa.load(__EXAMPLE_FILE)
S = librosa.feature.melspectrogram(y=y, sr=sr)
for lag in [1, 2, 3]:
for max_size in [1]:
# We only test with max_size=1 here to make the sub-band slicing test simple
odf_multi = librosa.onset.onset_strength_multi(S=S,
lag=lag, max_size=1,
aggregate=False)
odf_mean = librosa.onset.onset_strength_multi(S=S,
lag=lag, max_size=1,
aggregate=np.mean)
# With no aggregation, output shape should = input shape
assert odf_multi.shape == S.shape
# Result should average out to the same as mean aggregation
assert np.allclose(odf_mean, np.mean(odf_multi, axis=0))
def test_onset_strength_multi():
y, sr = librosa.load(__EXAMPLE_FILE)
S = librosa.feature.melspectrogram(y=y, sr=sr)
channels = np.linspace(0, S.shape[0], num=5).astype(int)
for lag in [1, 2, 3]:
for max_size in [1]:
# We only test with max_size=1 here to make the sub-band slicing test simple
odf_multi = librosa.onset.onset_strength_multi(S=S,
lag=lag, max_size=1,
channels=channels)
assert len(odf_multi) == len(channels) - 1
for i, (s, t) in enumerate(zip(channels, channels[1:])):
odf_single = librosa.onset.onset_strength(S=S[s:t],
lag=lag,
max_size=1)
assert np.allclose(odf_single, odf_multi[i])
def test_onset_detect_real():
def __test(y, sr, oenv, hop_length, bt):
onsets = librosa.onset.onset_detect(y=y, sr=sr, onset_envelope=oenv,
hop_length=hop_length,
backtrack=bt)
if bt:
assert np.all(onsets >= 0)
else:
assert np.all(onsets > 0)
assert np.all(onsets < len(y) * sr // hop_length)
if oenv is not None:
assert np.all(onsets < len(oenv))
y, sr = librosa.load(__EXAMPLE_FILE)
# Test with no signal
yield pytest.mark.xfail(__test, raises=librosa.ParameterError), None, sr, None, 512, False
for hop_length in [64, 512, 2048]:
oenv = librosa.onset.onset_strength(y=y, sr=sr, hop_length=hop_length)
for bt in [False, True]:
yield __test, y, sr, None, hop_length, bt
yield __test, y, sr, oenv, hop_length, bt
def test_onset_detect_const():
def __test(y, sr, oenv, hop_length):
onsets = librosa.onset.onset_detect(y=y, sr=sr, onset_envelope=oenv,
hop_length=hop_length)
assert len(onsets) == 0
sr = 22050
duration = 3.0
for f in [np.zeros, np.ones]:
y = f(int(duration * sr))
for hop_length in [64, 512, 2048]:
yield __test, y, sr, None, hop_length
yield __test, -y, sr, None, hop_length
oenv = librosa.onset.onset_strength(y=y,
sr=sr,
hop_length=hop_length)
yield __test, y, sr, oenv, hop_length
def test_onset_units():
def __test(units, hop_length, y, sr):
b1 = librosa.onset.onset_detect(y=y, sr=sr, hop_length=hop_length)
b2 = librosa.onset.onset_detect(y=y, sr=sr, hop_length=hop_length,
units=units)
t1 = librosa.frames_to_time(b1, sr=sr, hop_length=hop_length)
if units == 'time':
t2 = b2
elif units == 'samples':
t2 = librosa.samples_to_time(b2, sr=sr)
elif units == 'frames':
t2 = librosa.frames_to_time(b2, sr=sr, hop_length=hop_length)
assert np.allclose(t1, t2)
for sr in [None, 44100]:
y, sr = librosa.load(__EXAMPLE_FILE, sr=sr)
for hop_length in [512, 1024]:
for units in ['frames', 'time', 'samples']:
yield __test, units, hop_length, y, sr
yield pytest.mark.xfail(__test, raises=librosa.ParameterError), 'bad units', hop_length, y, sr
def test_onset_backtrack():
y, sr = librosa.load(__EXAMPLE_FILE)
oenv = librosa.onset.onset_strength(y=y, sr=sr)
onsets = librosa.onset.onset_detect(onset_envelope=oenv, backtrack=False)
def __test(energy):
# Test backtracking
onsets_bt = librosa.onset.onset_backtrack(onsets, energy)
# Make sure there are no negatives
assert np.all(onsets_bt >= 0)
# And that we never roll forward
assert np.all(onsets_bt <= onsets)
# And that the detected peaks are actually minima
assert np.all(energy[onsets_bt] <= energy[np.maximum(0, onsets_bt - 1)])
yield __test, oenv
rms = librosa.feature.rms(y=y)
yield __test, rms
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_onset_strength_noagg():
S = np.zeros((3,3))
librosa.onset.onset_strength(S=S, aggregate=False)
@pytest.mark.xfail(raises=librosa.ParameterError)
def test_onset_strength_badref():
S = np.zeros((3, 3))
librosa.onset.onset_strength(S=S, ref=S[:, :2])
def test_onset_strength_multi_ref():
srand()
# Make a random positive spectrum
S = 1 + np.abs(np.random.randn(1025, 10))
# Test with a null reference
null_ref = np.zeros_like(S)
onsets = librosa.onset.onset_strength_multi(S=S,
ref=null_ref,
aggregate=False,
center=False)
# since the reference is zero everywhere, S - ref = S
# past the setup phase (first frame)
assert np.allclose(onsets[:, 1:], S[:, 1:])