import librosa.display
from pywt import wavedec
wav, sr_ret = librosa.load('E:\PycharmProjects\pythonProject\AudioClassification-Pytorch-master\dataset/audio/fold1/1_001.wav',sr=48000,duration=0.1)
coeffs = wavedec(wav, 'db1', level=3)
cA3, cD3, cD2, cD1 = coeffs
print(coeffs)
print(cA3.shape[0])
print(cD3.shape[0])
print(cD2.shape[0])
print(cD1.shape[0])输出
[array([0.07650445, 0.0833558 , 0.08176605, 0.07457277, 0.08058269,
0.08559845, 0.08848084, 0.08152057, 0.07507973, 0.07418638,
0.07222807, 0.0802957 , 0.07812794, 0.08086747, 0.0813316 ,
0.08133803, 0.08903894, 0.08096947, 0.08364467, 0.0858161 ,
0.08263485, 0.0826166 , 0.084121 , 0.07839198, 0.07731027,
0.07829413, 0.08144377, 0.08425384, 0.07708076, 0.07302544,
0.08425157, 0.07876903, 0.07965968, 0.08055855, 0.07688117,
0.07561374, 0.0776955 , 0.08211555, 0.08077055, 0.07747985,
0.08099629, 0.08394986, 0.08020782, 0.08058906, 0.07841584,
0.07157657, 0.07174112, 0.07586262, 0.07597165, 0.08309121,
0.08112757, 0.08367857, 0.08113486, 0.08471542, 0.08022445,
0.08143417, 0.08199209, 0.08208886, 0.08421066, 0.07655222,
0.08022839, 0.08060424, 0.08251352, 0.07767326, 0.07826051,
0.07334705, 0.07912773, 0.08224639, 0.0790364 , 0.0837498 ,
0.08024669, 0.08002347, 0.08170317, 0.08433206, 0.08640218,
0.07982307, 0.08440088, 0.08177947, 0.08317496, 0.08085726,
0.07737669, 0.07184465, 0.07059529, 0.0732912 , 0.06881827,
0.07532898, 0.07772851, 0.07787324, 0.08319858, 0.08242418,
0.08302195, 0.08178334, 0.08113691, 0.08074863, 0.08416497,
0.07770953, 0.07553872, 0.07905459, 0.07795161, 0.07548623,
0.0763735 , 0.07832793, 0.07339811, 0.07720153, 0.07559691,
0.08141415, 0.07731315, 0.07560685, 0.0770729 , 0.0861042 ,
0.08410327, 0.08318184, 0.08247956, 0.07819069, 0.07677838,
0.08221659, 0.07896061, 0.07779658, 0.08053669, 0.07588732,
0.07578352, 0.0819625 , 0.08047445, 0.08371016, 0.07993497,
0.0777813 , 0.07721636, 0.07812442, 0.08051974, 0.07607076,
0.07781185, 0.08463093, 0.08159737, 0.07877699, 0.07496189,
0.06888194, 0.07555442, 0.07751273, 0.07661042, 0.07716994,
0.08133742, 0.08101633, 0.08267595, 0.08143541, 0.0802163 ,
0.08083557, 0.08178051, 0.08527566, 0.08280802, 0.08180082,
0.07648174, 0.07510398, 0.07431263, 0.08128905, 0.078614 ,
0.07557225, 0.07472385, 0.07460151, 0.07624479, 0.0736261 ,
0.07955411, 0.07606583, 0.07844146, 0.07553253, 0.08258235,
0.08214785, 0.07790203, 0.07635618, 0.07609379, 0.07641994,
0.07296453, 0.07609296, 0.07738647, 0.08275433, 0.0767623 ,
0.07813157, 0.08094644, 0.08080099, 0.07682419, 0.07998861,
0.08367382, 0.08261096, 0.08205467, 0.08700588, 0.08106048,
0.0808828 , 0.08543463, 0.08100224, 0.07617016, 0.07438301,
0.07415715, 0.07884923, 0.0825543 , 0.08242591, 0.07399297,
0.07007709, 0.07432111, 0.07870231, 0.07951258, 0.07747823,
0.08302285, 0.08279146, 0.08541548, 0.08217598, 0.08232942,
0.08017525, 0.08079593, 0.07484783, 0.08008471, 0.07704122,
0.07626027, 0.07872074, 0.07958986, 0.07663406, 0.0798322 ,
0.08069367, 0.08166939, 0.08113493, 0.07984167, 0.08142896,
0.08042534, 0.07943277, 0.07597353, 0.07827045, 0.07882155,
0.07894538, 0.0762165 , 0.07470343, 0.07209414, 0.07398853,
0.07060766, 0.07167587, 0.07709406, 0.07368625, 0.0808139 ,
0.0848256 , 0.08148388, 0.08378291, 0.0797573 , 0.07830805,
0.07792932, 0.08128259, 0.07737546, 0.07406706, 0.07683124,
0.07726574, 0.08051058, 0.08012202, 0.07937583, 0.08373319,
0.08405182, 0.07577369, 0.07758511, 0.07770786, 0.07784066,
0.0797521 , 0.08077393, 0.0782212 , 0.07821926, 0.07224753,
0.06847429, 0.07510112, 0.07935793, 0.08226496, 0.07713239,
0.07336913, 0.07453088, 0.07342835, 0.07915321, 0.0802083 ,
0.0816238 , 0.07868612, 0.07658873, 0.08129548, 0.08166315,
0.08399409, 0.08193853, 0.07621405, 0.07322274, 0.07028052,
0.074819 , 0.07636015, 0.0801906 , 0.08288169, 0.08052365,
0.07812575, 0.0856584 , 0.07943095, 0.0794 , 0.07932052,
0.07406253, 0.0778361 , 0.07833064, 0.08100557, 0.07808447,
0.0744974 , 0.07699095, 0.07259583, 0.07173784, 0.07229984,
0.0718141 , 0.06842783, 0.06690203, 0.07124069, 0.0795822 ,
0.08091439, 0.08040856, 0.08044728, 0.07821813, 0.08208902,
0.08179837, 0.08729526, 0.07951806, 0.08333924, 0.08210346,
0.07523954, 0.06967054, 0.07918971, 0.07891127, 0.07904792,
0.07572553, 0.07884577, 0.0766885 , 0.07705434, 0.07961883,
0.07868335, 0.07378662, 0.07194845, 0.07541746, 0.07771003,
0.07751166, 0.07527715, 0.07342698, 0.07163695, 0.071534 ,
0.07507175, 0.07910451, 0.07556672, 0.07271922, 0.07255846,
0.08069247, 0.07876556, 0.07662736, 0.07938129, 0.07697265,
0.08694211, 0.08396698, 0.09010324, 0.08705542, 0.07861633,
0.08135987, 0.07236537, 0.07304978, 0.07165784, 0.07340549,
0.07421646, 0.0767194 , 0.07675241, 0.08252157, 0.07675698,
0.07544861, 0.07693213, 0.07308666, 0.07410522, 0.07863477,
0.07989947, 0.07596504, 0.07679385, 0.07979421, 0.07945854,
0.07921755, 0.07787776, 0.07914457, 0.07341358, 0.07890388,
0.07645927, 0.07982715, 0.0849042 , 0.08140737, 0.07883157,
0.07596848, 0.07129404, 0.07976403, 0.08318871, 0.0806198 ,
0.08112253, 0.07455175, 0.07499638, 0.07198498, 0.07723052,
0.07590163, 0.07928336, 0.07509609, 0.07649867, 0.07738686,
0.07865521, 0.07855918, 0.07433577, 0.07542983, 0.07663357,
0.0790327 , 0.07832916, 0.07704087, 0.07545093, 0.07496922,
0.07543322, 0.07862443, 0.08919193, 0.08848961, 0.08113268,
0.07875451, 0.07927984, 0.07846828, 0.07718647, 0.07466707,
0.07620907, 0.07991058, 0.0766169 , 0.07648637, 0.0787633 ,
0.08306654, 0.08246756, 0.07767807, 0.07173108, 0.06498012,
0.0659945 , 0.07103009, 0.07433408, 0.06990716, 0.07043816,
0.07639977, 0.07681788, 0.08032517, 0.0806926 , 0.07744077,
0.07543324, 0.07485405, 0.0773479 , 0.07661782, 0.08448278,
0.08356978, 0.07769548, 0.07541443, 0.07788131, 0.08322986,
0.07844289, 0.07219986, 0.0693067 , 0.0746428 , 0.08027782,
0.08220445, 0.08147219, 0.0801007 , 0.08104272, 0.08053236,
0.07884105, 0.07810962, 0.07880224, 0.07743876, 0.07805119,
0.07639103, 0.0650245 , 0.06958014, 0.07624692, 0.07561584,
0.07910293, 0.08245309, 0.07515989, 0.07711318, 0.08244964,
0.08498955, 0.08151393, 0.07814609, 0.07573043, 0.07292885,
0.08006284, 0.0818428 , 0.07845806, 0.07777024, 0.0764415 ,
0.07770479, 0.08139426, 0.07999376, 0.08103448, 0.07086097,
0.06726958, 0.07467616, 0.07264686, 0.07965501, 0.07358599,
0.06837974, 0.07032041, 0.0776692 , 0.07809793, 0.08055476,
0.07907535, 0.07928792, 0.07733329, 0.07562521, 0.07707467,
0.07762201, 0.0792919 , 0.07773963, 0.07735741, 0.07583827,
0.07782334, 0.07556397, 0.07850955, 0.07773505, 0.07798739,
0.07732989, 0.07960375, 0.08103994, 0.08319928, 0.07735961,
0.07601283, 0.07901438, 0.07949287, 0.07512777, 0.08112602,
0.07945028, 0.07675199, 0.07388671, 0.07714041, 0.08099532,
0.07333526, 0.07033597, 0.06997744, 0.0792564 , 0.0786798 ,
0.08057447, 0.08080969, 0.07959443, 0.07612674, 0.07113966,
0.06680212, 0.07484698, 0.08247542, 0.08138932, 0.08040316,
0.07361268, 0.07313529, 0.07641388, 0.08491105, 0.07951498,
0.07505268, 0.06946716, 0.07413299, 0.07489438, 0.07581851,
0.07453717, 0.07234445, 0.07685083, 0.08001874, 0.08391744,
0.08334988, 0.0786819 , 0.07295582, 0.07549617, 0.08413371,
0.08416696, 0.07808357, 0.07391369, 0.06973092, 0.07802516,
0.09084705, 0.08113551, 0.07766015, 0.07585586, 0.0703136 ,
0.07314977, 0.07097851, 0.07535447, 0.0806351 , 0.07829477,
0.07916272, 0.07912883, 0.07459378, 0.07150758, 0.07923067,
0.07881656, 0.07970369, 0.08004165, 0.07624418, 0.08114168,
0.07801101, 0.073632 , 0.07607837, 0.07412928, 0.07651855,
0.08344139, 0.07755301, 0.07544194, 0.07679245, 0.07610674,
0.0775127 , 0.07700332, 0.07513452, 0.06917572, 0.0684009 ],
dtype=float32), array([-9.19412822e-04, -2.77705491e-04, 1.82990357e-03, -5.28894365e-04,
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4.99863178e-04, 4.32031974e-03, -1.91142410e-03, -6.56396151e-05,
2.36150622e-03, -1.17996708e-03, 1.95114315e-03, -1.02481991e-03,
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4.89111990e-04, 2.17217952e-04, 1.28579512e-03, 8.66245478e-04],
dtype=float32), array([ 0.00335322, -0.00148289, -0.00107422, ..., -0.00025483,
-0.00156629, 0.00116636], dtype=float32), array([-2.6028194e-03, -5.1744282e-05, 6.2261336e-04, ...,
-4.1605160e-04, -1.8741619e-03, 7.8527154e-03], dtype=float32)]
600
600
1200
2400离散小波变换为三层，输出的系数为cA3,cD3,cD2,cD1.分别为近似分量和细节分量
import librosa
import matplotlib.pyplot as plt
import numpy as np
import pywt
import librosa.display
from pywt import wavedec
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
wav, sr_ret = librosa.load('E:\PycharmProjects\pythonProject\AudioClassification-Pytorch-master\dataset/audio/fold1/1_001.wav',sr=48000,duration=0.1)
coeffs = wavedec(wav, 'db1', level=3)
cA3, cD3, cD2, cD1 = coeffs
print(coeffs)
print(cA3.shape[0])
print(cD3.shape[0])
print(cD2.shape[0])
print(cD1.shape[0])
plt.figure('cA3')
x1 = range(0,600)
y1 = cD3
#创建图并命名
ax1 = plt.gca()
#设置x轴、y轴名称
ax1.set_xlabel('分解系数')
ax1.set_ylabel('幅度')
ax1.plot(x1, y1, color='b', linewidth=1, alpha=1)
x2 = range(0, 600)
y2 = cD3
#创建图并命名
plt.figure('cD3')
ax2 = plt.gca()
#设置x轴、y轴名称
ax2.set_xlabel('分解系数')
ax2.set_ylabel('幅度')
ax2.plot(x2, y2, color='b', linewidth=1, alpha=1)
x3 = range(0,1200)
y3 = cD2
#创建图并命名
plt.figure('cD2')
ax3 = plt.gca()
#设置x轴、y轴名称
ax3.set_xlabel('分解系数')
ax3.set_ylabel('幅度')
ax3.plot(x3, y3, color='b', linewidth=1, alpha=1)
x4 = range(0,2400)
y4 = cD1
#创建图并命名
plt.figure('cD1')
ax4 = plt.gca()
#设置x轴、y轴名称
ax4.set_xlabel('分解系数')
ax4.set_ylabel('幅度')
ax4.plot(x4, y4, color='b', linewidth=1, alpha=1)
plt.show()二维信号是指两个独立频率变量的函数，可以使用二维小波变换，但在这里我们使用的信号具有相同的采样频率，属于一维信号，只能使用一维小波变换。