1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
|
# -*- coding: utf-8 -*-
# ######### COPYRIGHT #########
# Credits
# #######
#
# Copyright(c) 2015-2025
# ----------------------
#
# * `LabEx Archimède <http://labex-archimede.univ-amu.fr/>`_
# * `Laboratoire d'Informatique Fondamentale <http://www.lif.univ-mrs.fr/>`_
# (now `Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>`_)
# * `Institut de Mathématiques de Marseille <http://www.i2m.univ-amu.fr/>`_
# * `Université d'Aix-Marseille <http://www.univ-amu.fr/>`_
#
# This software is a port from LTFAT 2.1.0 :
# Copyright (C) 2005-2025 Peter L. Soendergaard <peter@sonderport.dk>.
#
# Contributors
# ------------
#
# * Denis Arrivault <contact.dev_AT_lis-lab.fr>
# * Florent Jaillet <contact.dev_AT_lis-lab.fr>
#
# Description
# -----------
#
# ltfatpy is a partial Python port of the
# `Large Time/Frequency Analysis Toolbox <http://ltfat.sourceforge.net/>`_,
# a MATLAB®/Octave toolbox for working with time-frequency analysis and
# synthesis.
#
# Version
# -------
#
# * ltfatpy version = 1.1.2
# * LTFAT version = 2.1.0
#
# Licence
# -------
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# ######### COPYRIGHT #########
"""Module of greasy signal loading
Ported from ltfat_2.1.0/signals/greasy.m
.. moduleauthor:: Denis Arrivault,
Florent Jaillet
"""
from __future__ import print_function, division
from scipy.io.wavfile import read as wavread
import importlib.resources
import numpy as np
def greasy():
"""Load the 'greasy' test signal
- Usage:
| ``(s, fs) = greasy()``
- Output parameters:
:returns: ``(s, fs)``
:rtype: tuple
:var numpy.ndarray s: 'greasy' signal
:var int fs: sampling frequency in Hz
``greasy`` loads the 'greasy' signal. It is a recording of a woman
pronouncing the word "greasy".
The signal is 5880 samples long and recorded at 16 kHz with around 11
bits of effective quantization.
The signal has been scaled to not produce any clipping when
played. To get integer values use ``numpy.rint(greasy()[0]*2048.)``.
The signal was obtained from Wavelab
(`<http://www-stat.stanford.edu/~wavelab/>`_), it is a part of the first
sentence of the TIMIT speech corpus "She had your dark suit in greasy
wash water all year":
`<http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC93S1>`_.
- Examples:
Plot of 'greasy' in the time-domain:
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from ltfatpy import greasy
>>> _ = plt.plot(np.arange(5880.)/16000., greasy()[0]);
>>> _ = plt.xlabel('Time (seconds)')
>>> _ = plt.ylabel('Amplitude')
>>> plt.show()
Plot of 'greasy' in the time-frequency-domain:
>>> import matplotlib.pyplot as plt
>>> from ltfatpy import greasy, sgram
>>> _ = sgram(greasy()[0], 16000., 90.)
>>> plt.show()
.. image:: images/greasy_1.png
:width: 700px
:alt: time domain image
:align: center
.. image:: images/greasy_2.png
:width: 600px
:alt: spectrogram image
:align: center
- References:
:cite:`mazh93`
"""
# f = resource_stream(__name__, "greasy.wav")
# ref = importlib.resources.files(__name__).joinpath("greasy.wav")
ref = importlib.resources.files(__package__).joinpath("greasy.wav")
with ref.open('rb') as f:
fs, s = wavread(f)
s = s.astype(np.float64) / 2.**15.
return (s, fs)
if __name__ == '__main__': # pragma: no cover
import doctest
doctest.testmod()
|
