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test_audio_signal.py
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test_audio_signal.py
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# encoding: utf-8
# pylint: skip-file
"""
This file contains tests for the madmom.audio.signal module.
"""
from __future__ import absolute_import, division, print_function
import os
import sys
import tempfile
import unittest
from os.path import join as pj
from madmom.audio.signal import *
from . import AUDIO_PATH
from .test_audio_comb_filters import sig_1d, sig_2d
sample_file = pj(AUDIO_PATH, 'sample.wav')
sample_file_22k = pj(AUDIO_PATH, 'sample_22050.wav')
stereo_sample_file = pj(AUDIO_PATH, 'stereo_sample.wav')
tmp_file = tempfile.NamedTemporaryFile(delete=False).name
# test signal functions
class TestSmoothFunction(unittest.TestCase):
def test_types(self):
# mono signals
result = smooth(sig_1d, None)
self.assertTrue(type(result) == type(sig_1d))
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == sig_1d.shape)
result = smooth(sig_1d, 3)
self.assertTrue(type(result) == type(sig_1d))
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == sig_1d.shape)
# multi-channel signals
result = smooth(sig_2d, None)
self.assertTrue(type(result) == type(sig_2d))
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_2d.shape)
result = smooth(sig_2d, 3)
self.assertTrue(type(result) == type(sig_2d))
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_2d.shape)
def test_errors(self):
with self.assertRaises(ValueError):
smooth(np.zeros(9).reshape(3, 3), -1)
with self.assertRaises(ValueError):
smooth(np.zeros(9).reshape(3, 3), 'bla')
with self.assertRaises(ValueError):
smooth(np.zeros(18).reshape(3, 3, 2), 4)
def test_values(self):
# mono signals
result = smooth(sig_1d, None)
self.assertTrue(np.allclose(result, sig_1d))
result = smooth(sig_1d, 0)
self.assertTrue(np.allclose(result, sig_1d))
result = smooth(sig_1d, 3)
result_3 = [0, 0.08, 1, 0.08, 0.08, 1, 0.08, 0.08, 1]
self.assertTrue(np.allclose(result, result_3))
result = smooth(sig_1d, 5)
result_5 = [0.08, 0.54, 1, 0.62, 0.62, 1, 0.62, 0.62, 1]
self.assertTrue(np.allclose(result, result_5))
result = smooth(sig_1d, 7)
result_7 = [0.31, 0.77, 1.08, 1.08, 1.08, 1.16, 1.08, 1.08, 1.08]
self.assertTrue(np.allclose(result, result_7))
result = smooth(sig_1d, np.ones(3))
result_3_ones = [0, 1, 1, 1, 1, 1, 1, 1, 1]
self.assertTrue(np.allclose(result, result_3_ones))
result = smooth(sig_1d, np.ones(4))
result_4_ones = [0, 1, 1, 1, 2, 1, 1, 2, 1]
self.assertTrue(np.allclose(result, result_4_ones))
# multi-channel signals
result = smooth(sig_2d, None)
self.assertTrue(np.allclose(result, sig_2d))
result = smooth(sig_2d, 3)
result_3 = [[0, 0.08, 1, 0.08, 0.08, 1, 0.08, 0.08, 1],
[1, 0.16, 1, 0.16, 1, 0.16, 1, 0.16, 1]]
self.assertTrue(np.allclose(result, np.asarray(result_3).T))
result = smooth(sig_2d, 5)
result_5 = [[0.08, 0.54, 1, 0.62, 0.62, 1, 0.62, 0.62, 1],
[1.08, 1.08, 1.16, 1.08, 1.16, 1.08, 1.16, 1.08, 1.08]]
self.assertTrue(np.allclose(result, np.asarray(result_5).T))
result = smooth(sig_2d, 7)
result_7 = [[0.31, 0.77, 1.08, 1.08, 1.08, 1.16, 1.08, 1.08, 1.08],
[1.31, 1.62, 1.62, 1.7, 1.62, 1.7, 1.62, 1.62, 1.31]]
self.assertTrue(np.allclose(result, np.asarray(result_7).T))
class TestAdjustGainFunction(unittest.TestCase):
def test_types(self):
# mono signals
result = adjust_gain(sig_1d, 0)
self.assertTrue(type(result) == type(sig_1d))
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == sig_1d.shape)
self.assertTrue(result.dtype == sig_1d.dtype)
# same with int16 dtype
result = adjust_gain(sig_1d.astype(np.int16), 0)
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == sig_1d.shape)
self.assertTrue(result.dtype == np.int16)
# from file
signal = Signal(sample_file)
result = adjust_gain(signal, 0)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == signal.dtype)
# multi-channel signals
result = adjust_gain(sig_2d, 0)
self.assertTrue(type(result) == type(sig_2d))
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_2d.shape)
self.assertTrue(result.dtype == sig_2d.dtype)
# same with int dtype
result = adjust_gain(sig_2d.astype(int), 0)
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_2d.shape)
self.assertTrue(result.dtype == int)
def test_values(self):
# mono signals
result = adjust_gain(sig_1d, 0)
self.assertTrue(np.allclose(result, sig_1d))
result = adjust_gain(sig_1d, -10)
self.assertTrue(np.allclose(result, 0.31622777 * sig_1d))
result = adjust_gain(sig_1d, 10)
self.assertTrue(np.allclose(result, 3.1622777 * sig_1d))
# same with int dtype
result = adjust_gain(sig_1d.astype(int), 0)
self.assertTrue(np.allclose(result, sig_1d.astype(int)))
result = adjust_gain(sig_1d.astype(int), -5)
self.assertTrue(np.allclose(result, 0 * sig_1d))
# multi-channel signals
result = adjust_gain(sig_2d, 0)
self.assertTrue(np.allclose(result, sig_2d))
result = adjust_gain(sig_2d, -3)
self.assertTrue(np.allclose(result, 0.70794578 * sig_2d))
# same with int16 dtype
result = adjust_gain(sig_2d.astype(np.int16), 0)
self.assertTrue(np.allclose(result, sig_2d))
result = adjust_gain(sig_2d.astype(np.int16), -1)
self.assertTrue(np.allclose(result, 0 * sig_2d))
def test_errors(self):
with self.assertRaises(ValueError):
adjust_gain(sig_2d.astype(np.int16), +60)
class TestAttenuateFunction(unittest.TestCase):
def test_types(self):
# mono signals
result = attenuate(sig_1d, 0)
self.assertTrue(type(result) == type(sig_1d))
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == sig_1d.shape)
self.assertTrue(result.dtype == sig_1d.dtype)
# same as int16 dtype
result = attenuate(sig_1d.astype(np.int16), 0)
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == sig_1d.shape)
self.assertTrue(result.dtype == np.int16)
# from file
signal = Signal(sample_file)
result = attenuate(signal, 0)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == np.int16)
# multi-channel signals
result = attenuate(sig_2d, 0)
self.assertTrue(type(result) == type(sig_2d))
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_2d.shape)
self.assertTrue(result.dtype == sig_2d.dtype)
# same as int dtype
result = attenuate(sig_2d.astype(int), 0)
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_2d.shape)
self.assertTrue(result.dtype == int)
def test_values(self):
# mono signals
result = attenuate(sig_1d, 0)
self.assertTrue(np.allclose(result, sig_1d))
result = attenuate(sig_1d, 10)
self.assertTrue(np.allclose(result, 0.31622777 * sig_1d))
result = attenuate(sig_1d, -10)
self.assertTrue(np.allclose(result, 3.1622777 * sig_1d))
# same with int dtype
result = attenuate(sig_1d.astype(int), 0)
self.assertTrue(np.allclose(result, sig_1d.astype(int)))
result = attenuate(sig_1d.astype(int), 5)
self.assertTrue(np.allclose(result, 0 * sig_1d))
# multi-channel signals
result = attenuate(sig_2d, 0)
self.assertTrue(np.allclose(result, sig_2d))
result = attenuate(sig_2d, 3)
self.assertTrue(np.allclose(result, 0.70794578 * sig_2d))
# same with int16 dtype
result = attenuate(sig_2d.astype(np.int16), 0)
self.assertTrue(np.allclose(result, sig_2d))
result = attenuate(sig_2d.astype(np.int16), 1)
self.assertTrue(np.allclose(result, 0 * sig_2d))
def test_errors(self):
with self.assertRaises(ValueError):
attenuate(sig_2d.astype(np.int16), -10)
class TestNormalizeFunction(unittest.TestCase):
def test_types(self):
# mono signals
result = normalize(sig_1d)
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == sig_1d.shape)
self.assertTrue(result.dtype == sig_1d.dtype)
# same as int16 dtype
result = normalize(sig_1d.astype(np.int16))
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == sig_1d.shape)
self.assertTrue(result.dtype == np.int16)
# from file
signal = Signal(sample_file)
result = normalize(signal)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == np.int16)
# multi-channel signals
result = normalize(sig_2d)
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_2d.shape)
self.assertTrue(result.dtype == sig_2d.dtype)
# same as int32 dtype
result = normalize(sig_2d.astype(np.int32))
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_2d.shape)
self.assertTrue(result.dtype == np.int32)
def test_values(self):
# mono signals
result = normalize(sig_1d)
self.assertTrue(np.allclose(result, sig_1d))
result = normalize(sig_1d * 0.5)
self.assertTrue(np.allclose(result, sig_1d))
self.assertTrue(np.max(result) == 1)
# same as int16 dtype
result = normalize(10 * sig_1d.astype(np.int16))
self.assertTrue(np.allclose(result, sig_1d * 32767))
self.assertTrue(np.max(result) == 32767)
# multi-channel signals
result = normalize(sig_2d)
self.assertTrue(np.allclose(result, sig_2d))
self.assertTrue(np.max(result) == 1)
# negative values
result = normalize(sig_2d * 4 - 2)
self.assertTrue(np.allclose(result, sig_2d * 2 - 1))
self.assertTrue(result.max() == 1)
self.assertTrue(result.min() == -1)
# same as int32 dtype
result = normalize(3 * sig_2d.astype(np.int32))
self.assertTrue(np.allclose(result, sig_2d * 2147483647))
self.assertTrue(np.max(result) == 2147483647)
def test_errors(self):
with self.assertRaises(ValueError):
normalize(sig_2d.astype(np.int64))
class TestMixFunction(unittest.TestCase):
mono_2d = np.asarray([0.5, 0, 1, 0, 0.5, 0.5, 0.5, 0, 1], dtype=float)
def test_types(self):
# mono signals
result = remix(sig_1d, 1)
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == sig_1d.shape)
self.assertTrue(result.dtype == sig_1d.dtype)
result = remix(sig_1d, 2)
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == (len(sig_1d), 2))
self.assertTrue(result.dtype == sig_1d.dtype)
result = remix(sig_1d, 3)
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == (len(sig_1d), 3))
self.assertTrue(result.dtype == sig_1d.dtype)
# same as int dtype
result = remix(sig_1d.astype(int), 1)
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == sig_1d.shape)
self.assertTrue(result.dtype == int)
result = remix(sig_1d.astype(int), 2)
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == (len(sig_1d), 2))
self.assertTrue(result.dtype == int)
# from file
signal = Signal(sample_file)
result = remix(signal, 1)
self.assertTrue(isinstance(result, Signal))
self.assertTrue(isinstance(result, np.ndarray))
self.assertTrue(result.dtype == np.int16)
# multi-channel signals
result = remix(sig_2d, 1)
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_1d.shape)
self.assertTrue(result.dtype == sig_2d.dtype)
result = remix(sig_2d, 2)
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_2d.shape)
self.assertTrue(result.dtype == sig_2d.dtype)
# from file
signal = Signal(stereo_sample_file)
result = remix(signal, 1)
self.assertTrue(isinstance(result, Signal))
self.assertTrue(isinstance(result, np.ndarray))
self.assertTrue(result.dtype == np.int16)
result = remix(signal, 2)
self.assertTrue(isinstance(result, Signal))
self.assertTrue(isinstance(result, np.ndarray))
self.assertTrue(result.dtype == np.int16)
with self.assertRaises(NotImplementedError):
remix(sig_2d, 3)
# same as int dtype
result = remix(sig_2d.astype(int), 1)
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_1d.shape)
self.assertTrue(result.dtype == int)
result = remix(sig_2d.astype(int), 2)
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_2d.shape)
self.assertTrue(result.dtype == int)
def test_channel_selection(self):
result = remix(sig_2d, 1, channel=0)
self.assertEqual(result.shape, sig_1d.shape)
self.assertTrue(np.array_equal(result, sig_1d))
result = remix(sig_2d, 1, channel=1)
self.assertTrue(np.array_equal(result, sig_2d[:, 1]), 0)
def test_values(self):
# mono signals
result = remix(sig_1d, 1)
self.assertTrue(np.allclose(result, sig_1d))
# same as int dtype
result = remix(sig_2d.astype(int), 1)
self.assertTrue(np.allclose(result, self.mono_2d.astype(int)))
# multi-channel signals
result = remix(sig_2d, 1)
self.assertTrue(np.allclose(result, self.mono_2d))
# same as int dtype
result = remix(2 * sig_2d.astype(int), 1)
self.assertTrue(np.allclose(result, 2 * self.mono_2d))
class TestResampleFunction(unittest.TestCase):
def setUp(self):
self.signal = Signal(sample_file)
self.signal_22k = Signal(sample_file_22k)
self.signal_float = Signal(sample_file, dtype=np.float32)
self.stereo_signal = Signal(stereo_sample_file)
self.float_target = np.array([-0.07537885, -0.077897, -0.08440731,
-0.07527363, -0.06685895, -0.05827513])
def test_types(self):
# mono signal
result = resample(self.signal, 22050)
self.assertTrue(isinstance(result, Signal))
self.assertTrue(isinstance(result, np.ndarray))
self.assertTrue(result.dtype == self.signal.dtype)
# stereo signal
result = resample(self.stereo_signal, 22050)
self.assertTrue(isinstance(result, Signal))
self.assertTrue(isinstance(result, np.ndarray))
self.assertTrue(result.dtype == self.stereo_signal.dtype)
def test_values_mono(self):
result = resample(self.signal, 22050)
self.assertEqual(result.sample_rate, 22050)
self.assertEqual(result.num_samples, 61741)
self.assertEqual(result.dtype, self.signal.dtype)
self.assertEqual(result.num_channels, self.signal.num_channels)
self.assertTrue(np.allclose(result.length, self.signal.length))
self.assertTrue(np.allclose(result, self.signal_22k))
def test_values_mono_float(self):
result = resample(self.signal_float, 22050)
self.assertEqual(result.sample_rate, 22050)
self.assertEqual(result.num_samples, 61741)
self.assertEqual(result.dtype, self.signal_float.dtype)
self.assertEqual(result.num_channels, self.signal_float.num_channels)
self.assertTrue(np.allclose(result.length, self.signal_float.length))
self.assertTrue(np.allclose(result[:6], self.float_target))
def test_values_dtype(self):
result = resample(self.signal, 22050, dtype=np.float32)
self.assertEqual(result.sample_rate, 22050)
self.assertEqual(result.num_samples, 61741)
self.assertEqual(result.dtype, np.float32)
self.assertEqual(result.num_channels, self.signal_float.num_channels)
self.assertTrue(np.allclose(result.length, self.signal_float.length))
self.assertTrue(np.allclose(result[:6], self.float_target))
def test_values_stereo(self):
result = resample(self.stereo_signal, 22050)
self.assertEqual(result.sample_rate, 22050)
self.assertEqual(result.num_samples, 91460)
self.assertEqual(result.dtype, self.stereo_signal.dtype)
self.assertEqual(result.num_channels, self.stereo_signal.num_channels)
self.assertTrue(np.allclose(result.length, self.stereo_signal.length))
self.assertTrue(np.allclose(result[:6],
[[34, 38], [32, 33], [37, 31],
[35, 35], [32, 34], [33, 34]]))
def test_values_upmixing(self):
result = resample(self.signal, 22050, num_channels=2)
self.assertEqual(result.sample_rate, 22050)
self.assertEqual(result.num_samples, 61741)
self.assertEqual(result.dtype, self.signal.dtype)
self.assertEqual(result.num_channels, 2)
self.assertTrue(np.allclose(result.length, self.signal.length))
stereo = np.vstack((self.signal_22k, self.signal_22k)).T / np.sqrt(2)
self.assertTrue(np.allclose(result, stereo, atol=np.sqrt(2)))
def test_values_downmixing(self):
result = resample(self.stereo_signal, 22050, num_channels=1)
self.assertEqual(result.sample_rate, 22050)
self.assertEqual(result.num_samples, 91460)
self.assertEqual(result.dtype, self.stereo_signal.dtype)
self.assertEqual(result.num_channels, 1)
self.assertTrue(np.allclose(result.length, self.stereo_signal.length))
self.assertTrue(np.allclose(result[:6], [36, 33, 34, 35, 33, 34]))
def test_errors(self):
with self.assertRaises(ValueError):
resample(sig_1d, 2)
class TestRescaleFunction(unittest.TestCase):
def test_types(self):
# mono signals
result = rescale(sig_1d, float)
self.assertTrue(len(result) == len(sig_1d))
self.assertTrue(result.shape == sig_1d.shape)
self.assertTrue(result.dtype == float)
# from file
signal = Signal(sample_file)
result = rescale(signal)
self.assertTrue(isinstance(result, Signal))
self.assertTrue(isinstance(result, np.ndarray))
self.assertTrue(result.dtype == np.float32)
# multi-channel signals
result = rescale(sig_2d, np.float16)
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.shape == sig_2d.shape)
self.assertTrue(result.dtype == np.float16)
# from file
signal = Signal(stereo_sample_file)
result = rescale(signal, float)
self.assertTrue(isinstance(result, Signal))
self.assertTrue(isinstance(result, np.ndarray))
self.assertTrue(result.dtype == float)
def test_errors(self):
with self.assertRaises(ValueError):
rescale(sig_2d, complex)
with self.assertRaises(ValueError):
rescale(sig_2d, int)
with self.assertRaises(ValueError):
rescale(np.ones(10, dtype=complex))
def test_values(self):
# mono signals
result = rescale(sig_1d, float)
self.assertTrue(np.allclose(result, sig_1d))
# from file
signal = Signal(sample_file)
result = rescale(signal)
self.assertTrue(np.allclose(result[:6],
[-0.07611316, -0.07660146, -0.07580798,
-0.08172857, -0.08645894, -0.08212531]))
# multi-channel signals
result = rescale(sig_2d, np.float16)
self.assertTrue(np.allclose(result, sig_2d))
# from file
signal = Signal(stereo_sample_file)
result = rescale(signal, float)
self.assertTrue(np.allclose(result[:6], [[0.00100711, 0.0011597],
[0.00106815, 0.00109867],
[0.00088504, 0.00103763],
[0.00109867, 0.00094607],
[0.00112918, 0.00091556],
[0.00109867, 0.00103763]]))
class TestTrimFunction(unittest.TestCase):
def test_types(self):
# mono signals
result = trim(sig_1d)
self.assertTrue(type(result) == type(sig_1d))
self.assertTrue(result.ndim == sig_1d.ndim)
self.assertTrue(result.dtype == sig_1d.dtype)
signal = Signal(sample_file)
result = trim(signal)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.ndim == signal.ndim)
self.assertTrue(result.dtype == np.int16)
# multi-channel signals
result = trim(sig_2d)
self.assertTrue(type(result) == type(sig_2d))
self.assertTrue(len(result) == len(sig_2d))
self.assertTrue(result.ndim == sig_2d.ndim)
self.assertTrue(result.dtype == sig_2d.dtype)
signal = Signal(stereo_sample_file)
result = trim(signal)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.ndim == signal.ndim)
self.assertTrue(result.dtype == np.int16)
def test_values(self):
# mono signals
result = trim(sig_1d)
trimmed_1d = [1, 0, 0, 1, 0, 0, 1]
self.assertTrue(np.allclose(result, trimmed_1d))
self.assertTrue(len(result) == len(sig_1d) - 2)
# multi-channel signals
# signal has leading zeros only in one channel
result = trim(sig_2d)
self.assertTrue(result.shape == sig_2d.shape)
signal = Signal(stereo_sample_file)
result = trim(signal)
self.assertTrue(result.shape == signal.shape)
# signal with leading zeros only in both channels
signal = np.tile(np.arange(5), 2).reshape(2, 5).T
result = trim(signal)
self.assertTrue(result.shape == (4, 2))
self.assertTrue(np.allclose(result[:, 0], np.arange(1, 5)))
self.assertTrue(np.allclose(result[:, 1], np.arange(1, 5)))
# zeros at the end
result = trim(np.arange(5, 0, -1))
self.assertTrue(np.allclose(result, [5, 4, 3, 2, 1]))
class TestEnergyFunction(unittest.TestCase):
def test_types(self):
# mono signals
result = energy(sig_1d)
self.assertIsInstance(result, float)
# multi-channel signals
result = energy(sig_2d)
self.assertIsInstance(result, float)
def test_errors(self):
with self.assertRaises(TypeError):
energy(None)
def test_values(self):
# mono signals
result = energy(sig_1d)
self.assertTrue(np.allclose(result, 3))
result = energy(np.zeros(100))
self.assertTrue(np.allclose(result, 0))
# multi-channel signals
result = energy(sig_2d)
self.assertTrue(np.allclose(result, 8))
result = energy(np.zeros(100).reshape(-1, 2))
self.assertTrue(np.allclose(result, 0))
def test_frames(self):
# mono signals
frames = FramedSignal(sig_1d, frame_size=4, hop_size=2)
result = energy(frames)
self.assertTrue(np.allclose(result, [0, 1, 2, 1, 1]))
result = energy(np.zeros(100))
self.assertTrue(np.allclose(result, 0))
# multi-channel signals
frames = FramedSignal(sig_2d, frame_size=4, hop_size=2)
result = energy(frames)
self.assertTrue(np.allclose(result, [1, 3, 4, 3, 3]))
result = energy(np.zeros(100).reshape(-1, 2))
self.assertTrue(np.allclose(result, 0))
class TestRootMeanSquareFunction(unittest.TestCase):
def test_types(self):
# mono signals
result = root_mean_square(sig_1d)
self.assertIsInstance(result, float)
# multi-channel signals
result = root_mean_square(sig_2d)
self.assertIsInstance(result, float)
def test_values(self):
# mono signals
result = root_mean_square(sig_1d)
self.assertTrue(np.allclose(result, 0.57735026919))
result = root_mean_square(np.zeros(100))
self.assertTrue(np.allclose(result, 0))
# multi-channel signals
result = root_mean_square(sig_2d)
self.assertTrue(np.allclose(result, 2. / 3))
result = root_mean_square(np.zeros(100).reshape(-1, 2))
self.assertTrue(np.allclose(result, 0))
def test_frames(self):
# mono signals
frames = FramedSignal(sig_1d, frame_size=4, hop_size=2)
result = root_mean_square(frames)
self.assertTrue(np.allclose(result, [0, 0.5, 0.70710678, 0.5, 0.5]))
result = root_mean_square(np.zeros(100))
self.assertTrue(np.allclose(result, 0))
# multi-channel signals
frames = FramedSignal(sig_2d, frame_size=4, hop_size=2)
result = root_mean_square(frames)
self.assertTrue(np.allclose(result, [0.35355339, 0.61237244,
0.70710678, 0.61237244,
0.61237244]))
result = root_mean_square(np.zeros(100).reshape(-1, 2))
self.assertTrue(np.allclose(result, 0))
class TestSoundPressureLevelFunction(unittest.TestCase):
def test_types(self):
# mono signals
result = sound_pressure_level(sig_1d)
self.assertIsInstance(result, float)
# multi-channel signals
result = sound_pressure_level(sig_2d)
self.assertIsInstance(result, float)
def test_values(self):
# mono signals
result = sound_pressure_level(sig_1d)
self.assertTrue(np.allclose(result, -4.7712125472))
# silence
result = sound_pressure_level(np.zeros(100))
self.assertTrue(np.allclose(result, -np.finfo(float).max))
# maximum float amplitude, alternating between -1 and 1
sinus = np.cos(np.linspace(0, 2 * np.pi * 100, 2 * 100 + 1))
result = sound_pressure_level(sinus)
self.assertTrue(np.allclose(result, 0.))
# maximum int16 amplitude, alternating between -1 and 1
sinus_int16 = (sinus * np.iinfo(np.int16).max).astype(np.int16)
result = sound_pressure_level(sinus_int16)
self.assertTrue(np.allclose(result, 0., atol=1e-3))
# multi-channel signals
result = sound_pressure_level(sig_2d)
self.assertTrue(np.allclose(result, -3.52182518111))
# silence
result = sound_pressure_level(np.zeros(100).reshape(-1, 2))
self.assertTrue(np.allclose(result, -np.finfo(float).max))
# maximum float amplitude, alternating between -1 and 1
sig = remix(sinus, 2)
result = sound_pressure_level(sig)
self.assertTrue(np.allclose(result, 0.))
# maximum int16 amplitude, alternating between -1 and 1
sig = remix(sinus_int16, 2)
result = sound_pressure_level(sig)
self.assertTrue(np.allclose(result, 0., atol=1e-3))
def test_frames(self):
# mono signals
frames = FramedSignal(sig_1d, frame_size=4, hop_size=2)
result = sound_pressure_level(frames)
self.assertTrue(np.allclose(result, [-np.finfo(float).max, -6.0206,
-3.0103, -6.0206, -6.0206]))
result = sound_pressure_level(np.zeros(100))
self.assertTrue(np.allclose(result, -np.finfo(float).max))
# multi-channel signals
frames = FramedSignal(sig_2d, frame_size=4, hop_size=2)
result = sound_pressure_level(frames)
self.assertTrue(np.allclose(result, [-9.03089987, -4.25968732,
-3.01029996, -4.25968732,
-4.25968732]))
result = sound_pressure_level(np.zeros(100).reshape(-1, 2))
self.assertTrue(np.allclose(result, -np.finfo(float).max))
# signal classes
class TestSignalClass(unittest.TestCase):
def test_types_array(self):
result = Signal(sig_1d)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == float)
self.assertIsInstance(result.start, type(None))
self.assertIsInstance(result.stop, type(None))
self.assertIsInstance(result.num_samples, int)
self.assertIsInstance(result.sample_rate, type(None))
self.assertIsInstance(result.num_channels, int)
self.assertIsInstance(result.length, type(None))
self.assertIsInstance(result.ndim, int)
def test_types_array_with_sample_rate(self):
result = Signal(sig_1d, 1)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == float)
self.assertIsInstance(result.start, type(None))
self.assertIsInstance(result.stop, type(None))
self.assertIsInstance(result.num_samples, int)
self.assertIsInstance(result.sample_rate, int)
self.assertIsInstance(result.num_channels, int)
self.assertIsInstance(result.length, float)
self.assertIsInstance(result.ndim, int)
def test_types_file(self):
result = Signal(sample_file)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == np.int16)
def test_values_1d(self):
result = Signal(sig_1d, 1)
self.assertTrue(np.allclose(result, sig_1d))
self.assertTrue(len(result) == 9)
self.assertTrue(result.num_samples == 9)
self.assertTrue(result.sample_rate == 1)
self.assertTrue(result.num_channels == 1)
self.assertTrue(result.length == 9)
def test_values_1d_no_sample_rate(self):
result = Signal(sig_1d)
self.assertTrue(np.allclose(result, sig_1d))
self.assertTrue(len(result) == 9)
self.assertTrue(result.num_samples == 9)
self.assertTrue(result.sample_rate is None)
self.assertTrue(result.num_channels == 1)
self.assertTrue(result.length is None)
def test_values_2d(self):
result = Signal(sig_2d, 12.3)
self.assertTrue(np.allclose(result, sig_2d))
self.assertTrue(len(result) == 9)
self.assertTrue(result.num_samples == 9)
# not officially supported, but Signal can handle float sample rates
self.assertTrue(result.sample_rate == 12.3)
self.assertTrue(result.num_channels == 2)
self.assertTrue(result.length == 9 / 12.3)
self.assertTrue(result.ndim == 2)
def test_num_channels(self):
result = Signal(sig_2d, sample_rate=1, num_channels=1)
self.assertTrue(result.shape == (9, ))
self.assertTrue(np.allclose(result,
[0.5, 0, 1, 0, 0.5, 0.5, 0.5, 0, 1]))
def test_values_file(self):
result = Signal(sample_file)
self.assertTrue(np.allclose(result[:5],
[-2494, -2510, -2484, -2678, -2833]))
self.assertTrue(len(result) == 123481)
self.assertTrue(result.num_samples == 123481)
self.assertTrue(result.sample_rate == 44100)
self.assertTrue(result.num_channels == 1)
self.assertTrue(result.ndim == 1)
self.assertTrue(np.allclose(result.length, 2.8))
def test_write_method(self):
orig = Signal(sample_file)
orig.write(tmp_file)
result = Signal(tmp_file)
self.assertTrue(np.allclose(orig, result))
def test_methods(self):
# mono signals
signal = Signal(sig_1d)
self.assertTrue(np.allclose(signal.energy(), 3))
self.assertTrue(np.allclose(signal.rms(), 0.57735026919))
self.assertTrue(np.allclose(signal.spl(), -4.7712125472))
# multi-channel signals
signal = Signal(sig_2d)
self.assertTrue(np.allclose(signal.energy(), 8))
self.assertTrue(np.allclose(signal.root_mean_square(), 2. / 3))
self.assertTrue(np.allclose(signal.sound_pressure_level(),
-3.52182518111))
class TestSignalProcessorClass(unittest.TestCase):
def setUp(self):
self.processor = SignalProcessor()
def test_types(self):
self.assertIsInstance(self.processor, SignalProcessor)
self.assertIsInstance(self.processor, Processor)
# attributes
self.assertTrue(self.processor.sample_rate is None)
self.assertTrue(self.processor.num_channels is None)
self.assertTrue(self.processor.start is None)
self.assertTrue(self.processor.stop is None)
self.assertIsInstance(self.processor.norm, bool)
self.assertIsInstance(self.processor.gain, float)
def test_values(self):
# attributes
self.assertTrue(self.processor.sample_rate is None)
self.assertTrue(self.processor.num_channels is None)
self.assertTrue(self.processor.start is None)
self.assertTrue(self.processor.stop is None)
self.assertTrue(self.processor.norm is False)
self.assertTrue(self.processor.gain == 0)
def test_process(self):
result = self.processor.process(sample_file)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == np.int16)
self.assertTrue(np.allclose(result[:5],
[-2494, -2510, -2484, -2678, -2833]))
self.assertTrue(len(result) == 123481)
self.assertTrue(result.min() == -20603)
self.assertTrue(result.max() == 17977)
self.assertTrue(result.mean() == -172.88385257650975)
# attributes
self.assertTrue(result.sample_rate == 44100)
# properties
self.assertTrue(result.num_samples == 123481)
self.assertTrue(result.num_channels == 1)
self.assertTrue(np.allclose(result.length, 2.8))
def test_process_stereo(self):
self.processor.num_channels = 1
self.assertTrue(self.processor.num_channels == 1)
result = self.processor.process(stereo_sample_file)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == np.int16)
def test_process_norm(self):
self.processor.norm = True
self.assertTrue(self.processor.norm is True)
result = self.processor.process(sample_file)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == np.int16)
self.assertTrue(np.allclose(result[:5],
[-3966, -3991, -3950, -4259, -4505]))
self.assertTrue(len(result) == 123481)
self.assertTrue(result.min() == -32767)
self.assertTrue(result.max() == 28590)
self.assertTrue(result.mean() == -274.92599671204476)
# attributes
self.assertTrue(result.sample_rate == 44100)
# properties
self.assertTrue(result.num_samples == 123481)
self.assertTrue(result.num_channels == 1)
self.assertTrue(np.allclose(result.length, 2.8))
def test_process_gain(self):
self.processor.gain = -10
self.assertTrue(self.processor.gain == -10.)
result = self.processor.process(sample_file)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == np.int16)
self.assertTrue(np.allclose(result[:5],
[-788, -793, -785, -846, -895]))
self.assertTrue(len(result) == 123481)
# attributes
self.assertTrue(result.sample_rate == 44100)
# properties
self.assertTrue(result.num_samples == 123481)
self.assertTrue(result.num_channels == 1)
self.assertTrue(np.allclose(result.length, 2.8))
def test_pickle(self):
self.processor.dump(tmp_file)
processor = SignalProcessor.load(tmp_file)
self.assertEqual(type(self.processor), type(processor))
self.assertEqual(self.processor.sample_rate,
processor.sample_rate)
self.assertEqual(self.processor.num_channels,
processor.num_channels)
self.assertEqual(self.processor.start, processor.start)
self.assertEqual(self.processor.stop, processor.stop)
self.assertEqual(self.processor.norm, processor.norm)
self.assertEqual(self.processor.gain, processor.gain)
@unittest.skipIf(sys.version_info < (3, 2), 'assertWarns needs Python 3.2')
def test_multiprocessing(self):
from concurrent.futures import ProcessPoolExecutor
sig = Signal(sample_file)
pool = ProcessPoolExecutor(max_workers=2)
pool.submit(self.processor, sig).result()
# framing functions
class TestSignalFrameFunction(unittest.TestCase):
def test_types(self):
result = signal_frame(np.arange(10), 0, 4, 2)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == int)
result = signal_frame(np.arange(10, dtype=float), 0, 4, 2)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == float)
signal = Signal(sample_file)
result = signal_frame(signal, 0, 4, 2)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == np.int16)
result = signal_frame(signal, 2000, 400, 200)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == np.int16)
result = signal_frame(signal, -10, 400, 200)
self.assertIsInstance(result, Signal)
self.assertIsInstance(result, np.ndarray)
self.assertTrue(result.dtype == np.int16)
def test_short_input_length(self):
result = signal_frame(np.arange(4), 0, 10, 5)
self.assertTrue(np.allclose(result, [0, 0, 0, 0, 0, 0, 1, 2, 3, 0]))
result = signal_frame(np.arange(4), 1, 10, 5)
self.assertTrue(np.allclose(result, [0, 1, 2, 3, 0, 0, 0, 0, 0, 0]))
result = signal_frame(np.arange(4), 2, 10, 5)
self.assertTrue(np.allclose(result, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]))
result = signal_frame(np.arange(4), -2, 10, 5)
self.assertTrue(np.allclose(result, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]))
def test_values(self):
result = signal_frame(np.arange(10), -1, 4, 2)
self.assertTrue(np.allclose(result, [0, 0, 0, 0]))
result = signal_frame(np.arange(10), 0, 4, 2)
self.assertTrue(np.allclose(result, [0, 0, 0, 1]))
result = signal_frame(np.arange(10), 1, 4, 2)
self.assertTrue(np.allclose(result, [0, 1, 2, 3]))
result = signal_frame(np.arange(10), 2, 4, 2)
self.assertTrue(np.allclose(result, [2, 3, 4, 5]))
result = signal_frame(np.arange(10), 3, 4, 2)
self.assertTrue(np.allclose(result, [4, 5, 6, 7]))
result = signal_frame(np.arange(10), 4, 4, 2)
self.assertTrue(np.allclose(result, [6, 7, 8, 9]))
result = signal_frame(np.arange(10), 5, 4, 2)
self.assertTrue(np.allclose(result, [8, 9, 0, 0]))
def test_stereo_values(self):
signal = np.tile(np.arange(10)[:, np.newaxis], 2)
result = signal_frame(signal, 0, 4, 2)
self.assertTrue(np.allclose(result, [[0, 0], [0, 0], [0, 0], [1, 1]]))
result = signal_frame(signal, 1, 4, 2)
self.assertTrue(np.allclose(result, [[0, 0], [1, 1], [2, 2], [3, 3]]))
result = signal_frame(signal, 2, 4, 2)
self.assertTrue(np.allclose(result, [[2, 2], [3, 3], [4, 4], [5, 5]]))
result = signal_frame(signal, 3, 4, 2)
self.assertTrue(np.allclose(result, [[4, 4], [5, 5], [6, 6], [7, 7]]))
result = signal_frame(signal, 4, 4, 2)
self.assertTrue(np.allclose(result, [[6, 6], [7, 7], [8, 8], [9, 9]]))
result = signal_frame(signal, 5, 4, 2)
self.assertTrue(np.allclose(result, [[8, 8], [9, 9], [0, 0], [0, 0]]))
result = signal_frame(signal, 6, 4, 2)
self.assertTrue(np.allclose(result, [[0, 0], [0, 0], [0, 0], [0, 0]]))
def test_float_hop_size(self):
result = signal_frame(np.arange(10), 0, 3.5, 2)
self.assertTrue(np.allclose(result, [0, 0, 1]))
result = signal_frame(np.arange(10), 1, 3.5, 2)
self.assertTrue(np.allclose(result, [1, 2, 3]))
result = signal_frame(np.arange(10), 2, 3.5, 2)
self.assertTrue(np.allclose(result, [3, 4, 5]))
def test_origin(self):
result = signal_frame(np.arange(10), 0, 4, 2)
self.assertTrue(np.allclose(result, [0, 0, 0, 1]))
# positive values shift to the left
result = signal_frame(np.arange(10), 0, 4, 2, 1)
self.assertTrue(np.allclose(result, [0, 0, 0, 0]))
# negative values shift to the right
result = signal_frame(np.arange(10), 0, 4, 2, -1)
self.assertTrue(np.allclose(result, [0, 0, 1, 2]))
result = signal_frame(np.arange(10), 0, 4, 2, -2)
self.assertTrue(np.allclose(result, [0, 1, 2, 3]))
result = signal_frame(np.arange(10), 0, 4, 2, -4)
self.assertTrue(np.allclose(result, [2, 3, 4, 5]))
# test with float origin with half the size of the frame size
result = signal_frame(np.arange(10), 0, 5, 2, -2.5)
self.assertTrue(np.allclose(result, [0, 1, 2, 3, 4]))
def test_pad(self):
# 1D signal
x = np.arange(10, 20)
# pad with a fixed value