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python+离散小波变换+不同系数的波形_librosa 小波

作者：很楠不爱3 | 2024-03-29 16:40:19

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librosa 小波
  1. import librosa.display
  2. from pywt import wavedec
  3. wav, sr_ret = librosa.load('E:\PycharmProjects\pythonProject\AudioClassification-Pytorch-master\dataset/audio/fold1/1_001.wav',sr=48000,duration=0.1)
  4. coeffs = wavedec(wav, 'db1', level=3)
  5. cA3, cD3, cD2, cD1 = coeffs
  6. print(coeffs)
  7. print(cA3.shape[0])
  8. print(cD3.shape[0])
  9. print(cD2.shape[0])
  10. print(cD1.shape[0])

输出

  1. [array([0.07650445, 0.0833558 , 0.08176605, 0.07457277, 0.08058269,
  2.        0.08559845, 0.08848084, 0.08152057, 0.07507973, 0.07418638,
  3.        0.07222807, 0.0802957 , 0.07812794, 0.08086747, 0.0813316 ,
  4.        0.08133803, 0.08903894, 0.08096947, 0.08364467, 0.0858161 ,
  5.        0.08263485, 0.0826166 , 0.084121  , 0.07839198, 0.07731027,
  6.        0.07829413, 0.08144377, 0.08425384, 0.07708076, 0.07302544,
  7.        0.08425157, 0.07876903, 0.07965968, 0.08055855, 0.07688117,
  8.        0.07561374, 0.0776955 , 0.08211555, 0.08077055, 0.07747985,
  9.        0.08099629, 0.08394986, 0.08020782, 0.08058906, 0.07841584,
  10.        0.07157657, 0.07174112, 0.07586262, 0.07597165, 0.08309121,
  11.        0.08112757, 0.08367857, 0.08113486, 0.08471542, 0.08022445,
  12.        0.08143417, 0.08199209, 0.08208886, 0.08421066, 0.07655222,
  13.        0.08022839, 0.08060424, 0.08251352, 0.07767326, 0.07826051,
  14.        0.07334705, 0.07912773, 0.08224639, 0.0790364 , 0.0837498 ,
  15.        0.08024669, 0.08002347, 0.08170317, 0.08433206, 0.08640218,
  16.        0.07982307, 0.08440088, 0.08177947, 0.08317496, 0.08085726,
  17.        0.07737669, 0.07184465, 0.07059529, 0.0732912 , 0.06881827,
  18.        0.07532898, 0.07772851, 0.07787324, 0.08319858, 0.08242418,
  19.        0.08302195, 0.08178334, 0.08113691, 0.08074863, 0.08416497,
  20.        0.07770953, 0.07553872, 0.07905459, 0.07795161, 0.07548623,
  21.        0.0763735 , 0.07832793, 0.07339811, 0.07720153, 0.07559691,
  22.        0.08141415, 0.07731315, 0.07560685, 0.0770729 , 0.0861042 ,
  23.        0.08410327, 0.08318184, 0.08247956, 0.07819069, 0.07677838,
  24.        0.08221659, 0.07896061, 0.07779658, 0.08053669, 0.07588732,
  25.        0.07578352, 0.0819625 , 0.08047445, 0.08371016, 0.07993497,
  26.        0.0777813 , 0.07721636, 0.07812442, 0.08051974, 0.07607076,
  27.        0.07781185, 0.08463093, 0.08159737, 0.07877699, 0.07496189,
  28.        0.06888194, 0.07555442, 0.07751273, 0.07661042, 0.07716994,
  29.        0.08133742, 0.08101633, 0.08267595, 0.08143541, 0.0802163 ,
  30.        0.08083557, 0.08178051, 0.08527566, 0.08280802, 0.08180082,
  31.        0.07648174, 0.07510398, 0.07431263, 0.08128905, 0.078614  ,
  32.        0.07557225, 0.07472385, 0.07460151, 0.07624479, 0.0736261 ,
  33.        0.07955411, 0.07606583, 0.07844146, 0.07553253, 0.08258235,
  34.        0.08214785, 0.07790203, 0.07635618, 0.07609379, 0.07641994,
  35.        0.07296453, 0.07609296, 0.07738647, 0.08275433, 0.0767623 ,
  36.        0.07813157, 0.08094644, 0.08080099, 0.07682419, 0.07998861,
  37.        0.08367382, 0.08261096, 0.08205467, 0.08700588, 0.08106048,
  38.        0.0808828 , 0.08543463, 0.08100224, 0.07617016, 0.07438301,
  39.        0.07415715, 0.07884923, 0.0825543 , 0.08242591, 0.07399297,
  40.        0.07007709, 0.07432111, 0.07870231, 0.07951258, 0.07747823,
  41.        0.08302285, 0.08279146, 0.08541548, 0.08217598, 0.08232942,
  42.        0.08017525, 0.08079593, 0.07484783, 0.08008471, 0.07704122,
  43.        0.07626027, 0.07872074, 0.07958986, 0.07663406, 0.0798322 ,
  44.        0.08069367, 0.08166939, 0.08113493, 0.07984167, 0.08142896,
  45.        0.08042534, 0.07943277, 0.07597353, 0.07827045, 0.07882155,
  46.        0.07894538, 0.0762165 , 0.07470343, 0.07209414, 0.07398853,
  47.        0.07060766, 0.07167587, 0.07709406, 0.07368625, 0.0808139 ,
  48.        0.0848256 , 0.08148388, 0.08378291, 0.0797573 , 0.07830805,
  49.        0.07792932, 0.08128259, 0.07737546, 0.07406706, 0.07683124,
  50.        0.07726574, 0.08051058, 0.08012202, 0.07937583, 0.08373319,
  51.        0.08405182, 0.07577369, 0.07758511, 0.07770786, 0.07784066,
  52.        0.0797521 , 0.08077393, 0.0782212 , 0.07821926, 0.07224753,
  53.        0.06847429, 0.07510112, 0.07935793, 0.08226496, 0.07713239,
  54.        0.07336913, 0.07453088, 0.07342835, 0.07915321, 0.0802083 ,
  55.        0.0816238 , 0.07868612, 0.07658873, 0.08129548, 0.08166315,
  56.        0.08399409, 0.08193853, 0.07621405, 0.07322274, 0.07028052,
  57.        0.074819  , 0.07636015, 0.0801906 , 0.08288169, 0.08052365,
  58.        0.07812575, 0.0856584 , 0.07943095, 0.0794    , 0.07932052,
  59.        0.07406253, 0.0778361 , 0.07833064, 0.08100557, 0.07808447,
  60.        0.0744974 , 0.07699095, 0.07259583, 0.07173784, 0.07229984,
  61.        0.0718141 , 0.06842783, 0.06690203, 0.07124069, 0.0795822 ,
  62.        0.08091439, 0.08040856, 0.08044728, 0.07821813, 0.08208902,
  63.        0.08179837, 0.08729526, 0.07951806, 0.08333924, 0.08210346,
  64.        0.07523954, 0.06967054, 0.07918971, 0.07891127, 0.07904792,
  65.        0.07572553, 0.07884577, 0.0766885 , 0.07705434, 0.07961883,
  66.        0.07868335, 0.07378662, 0.07194845, 0.07541746, 0.07771003,
  67.        0.07751166, 0.07527715, 0.07342698, 0.07163695, 0.071534  ,
  68.        0.07507175, 0.07910451, 0.07556672, 0.07271922, 0.07255846,
  69.        0.08069247, 0.07876556, 0.07662736, 0.07938129, 0.07697265,
  70.        0.08694211, 0.08396698, 0.09010324, 0.08705542, 0.07861633,
  71.        0.08135987, 0.07236537, 0.07304978, 0.07165784, 0.07340549,
  72.        0.07421646, 0.0767194 , 0.07675241, 0.08252157, 0.07675698,
  73.        0.07544861, 0.07693213, 0.07308666, 0.07410522, 0.07863477,
  74.        0.07989947, 0.07596504, 0.07679385, 0.07979421, 0.07945854,
  75.        0.07921755, 0.07787776, 0.07914457, 0.07341358, 0.07890388,
  76.        0.07645927, 0.07982715, 0.0849042 , 0.08140737, 0.07883157,
  77.        0.07596848, 0.07129404, 0.07976403, 0.08318871, 0.0806198 ,
  78.        0.08112253, 0.07455175, 0.07499638, 0.07198498, 0.07723052,
  79.        0.07590163, 0.07928336, 0.07509609, 0.07649867, 0.07738686,
  80.        0.07865521, 0.07855918, 0.07433577, 0.07542983, 0.07663357,
  81.        0.0790327 , 0.07832916, 0.07704087, 0.07545093, 0.07496922,
  82.        0.07543322, 0.07862443, 0.08919193, 0.08848961, 0.08113268,
  83.        0.07875451, 0.07927984, 0.07846828, 0.07718647, 0.07466707,
  84.        0.07620907, 0.07991058, 0.0766169 , 0.07648637, 0.0787633 ,
  85.        0.08306654, 0.08246756, 0.07767807, 0.07173108, 0.06498012,
  86.        0.0659945 , 0.07103009, 0.07433408, 0.06990716, 0.07043816,
  87.        0.07639977, 0.07681788, 0.08032517, 0.0806926 , 0.07744077,
  88.        0.07543324, 0.07485405, 0.0773479 , 0.07661782, 0.08448278,
  89.        0.08356978, 0.07769548, 0.07541443, 0.07788131, 0.08322986,
  90.        0.07844289, 0.07219986, 0.0693067 , 0.0746428 , 0.08027782,
  91.        0.08220445, 0.08147219, 0.0801007 , 0.08104272, 0.08053236,
  92.        0.07884105, 0.07810962, 0.07880224, 0.07743876, 0.07805119,
  93.        0.07639103, 0.0650245 , 0.06958014, 0.07624692, 0.07561584,
  94.        0.07910293, 0.08245309, 0.07515989, 0.07711318, 0.08244964,
  95.        0.08498955, 0.08151393, 0.07814609, 0.07573043, 0.07292885,
  96.        0.08006284, 0.0818428 , 0.07845806, 0.07777024, 0.0764415 ,
  97.        0.07770479, 0.08139426, 0.07999376, 0.08103448, 0.07086097,
  98.        0.06726958, 0.07467616, 0.07264686, 0.07965501, 0.07358599,
  99.        0.06837974, 0.07032041, 0.0776692 , 0.07809793, 0.08055476,
  100.        0.07907535, 0.07928792, 0.07733329, 0.07562521, 0.07707467,
  101.        0.07762201, 0.0792919 , 0.07773963, 0.07735741, 0.07583827,
  102.        0.07782334, 0.07556397, 0.07850955, 0.07773505, 0.07798739,
  103.        0.07732989, 0.07960375, 0.08103994, 0.08319928, 0.07735961,
  104.        0.07601283, 0.07901438, 0.07949287, 0.07512777, 0.08112602,
  105.        0.07945028, 0.07675199, 0.07388671, 0.07714041, 0.08099532,
  106.        0.07333526, 0.07033597, 0.06997744, 0.0792564 , 0.0786798 ,
  107.        0.08057447, 0.08080969, 0.07959443, 0.07612674, 0.07113966,
  108.        0.06680212, 0.07484698, 0.08247542, 0.08138932, 0.08040316,
  109.        0.07361268, 0.07313529, 0.07641388, 0.08491105, 0.07951498,
  110.        0.07505268, 0.06946716, 0.07413299, 0.07489438, 0.07581851,
  111.        0.07453717, 0.07234445, 0.07685083, 0.08001874, 0.08391744,
  112.        0.08334988, 0.0786819 , 0.07295582, 0.07549617, 0.08413371,
  113.        0.08416696, 0.07808357, 0.07391369, 0.06973092, 0.07802516,
  114.        0.09084705, 0.08113551, 0.07766015, 0.07585586, 0.0703136 ,
  115.        0.07314977, 0.07097851, 0.07535447, 0.0806351 , 0.07829477,
  116.        0.07916272, 0.07912883, 0.07459378, 0.07150758, 0.07923067,
  117.        0.07881656, 0.07970369, 0.08004165, 0.07624418, 0.08114168,
  118.        0.07801101, 0.073632  , 0.07607837, 0.07412928, 0.07651855,
  119.        0.08344139, 0.07755301, 0.07544194, 0.07679245, 0.07610674,
  120.        0.0775127 , 0.07700332, 0.07513452, 0.06917572, 0.0684009 ],
  121.       dtype=float32), array([-9.19412822e-04, -2.77705491e-04,  1.82990357e-03, -5.28894365e-04,
  122.        -2.32549757e-03, -2.76560336e-03,  2.87637115e-03,  3.35941091e-03,
  123.        -1.68948621e-03,  9.10822302e-04, -1.40679255e-03, -1.54112652e-03,
  124.         1.80123746e-03, -1.62048638e-03, -8.34085047e-04, -1.13041326e-03,
  125.         4.87755984e-04,  2.64811888e-03, -2.82973051e-04,  6.12594187e-04,
  126.        -4.99051064e-04,  9.62205231e-05,  2.06321478e-04, -1.11363828e-03,
  127.         2.31694430e-03, -1.38759241e-03, -2.08251923e-03,  3.62410396e-03,
  128.         4.18052077e-05, -8.79757106e-04, -4.35471535e-04,  5.41329384e-04,
  129.        -2.53003836e-03,  2.02051923e-03,  2.68366188e-04, -1.16672739e-03,
  130.        -2.71604210e-03,  7.51275569e-04,  1.93547457e-04, -5.85615635e-04,
  131.         1.38495117e-04,  5.32601029e-04,  6.93351030e-05,  4.87066805e-04,
  132.         1.77503750e-03,  8.67754221e-04, -1.52819976e-03, -1.05461478e-03,
  133.        -1.13817677e-03,  3.61729413e-04, -3.43192369e-04, -1.88506022e-03,
  134.         5.49564138e-03,  2.39383429e-03, -2.26896256e-04, -2.39400566e-03,
  135.        -4.40545380e-04, -1.78169087e-03,  2.43815407e-03, -1.97758526e-03,
  136.         4.53829393e-03, -1.89631060e-03, -1.74862146e-03,  3.09866667e-03,
  137.        -3.60884145e-03,  1.11461058e-03, -3.22386622e-03,  2.42048129e-03,
  138.        -6.98234886e-04, -6.80014491e-05,  1.27427280e-03,  2.45563686e-04,
  139.        -3.03193927e-03, -2.92445347e-03,  2.44621560e-03, -4.02562320e-04,
  140.         5.98300248e-04,  1.94914639e-03, -2.08131969e-04,  1.49377808e-03,
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  142.         4.29563224e-05, -6.27275556e-04,  4.11543995e-04, -1.61727518e-03,
  143.         6.54727221e-04,  2.40700319e-03, -9.14633274e-04, -2.44547054e-03,
  144.        -1.60250068e-03,  4.98227775e-04, -3.20073217e-03,  2.57544219e-03,
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  177.         5.43896109e-04, -9.32995230e-04,  2.19732523e-04,  2.27139890e-03,
  178.        -1.15505233e-03,  1.06808171e-03,  6.33094460e-04, -3.02784145e-03,
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  181.         5.48809767e-04,  4.44758683e-04,  6.72250986e-04, -1.63748860e-04,
  182.        -1.94746628e-03,  2.77590007e-04,  2.25482136e-03, -2.32259929e-03,
  183.         6.72016293e-04,  2.99960375e-04,  4.01332974e-04, -2.66224146e-04,
  184.        -1.94330513e-03,  4.36436385e-04,  1.64438039e-04, -1.19584054e-03,
  185.         2.42852420e-03,  3.90414149e-04,  4.23725694e-04,  1.23712420e-03,
  186.         3.19629908e-06, -1.89045444e-03, -1.99901313e-03,  5.03677875e-04,
  187.         2.31100991e-03, -1.41481310e-03,  8.34465027e-04,  1.04177743e-04,
  188.        -2.15237960e-03, -2.14999169e-03,  8.57561827e-04, -6.55081123e-04,
  189.        -2.29173899e-03,  2.07542256e-03, -7.74070621e-04, -1.06385350e-03,
  190.         1.40264630e-03,  4.42564487e-05,  1.82382017e-03, -3.64828855e-04,
  191.        -2.22200900e-03, -3.35648656e-06,  8.33291560e-04, -1.55290589e-03,
  192.         2.77876854e-04, -1.39109790e-03, -2.29655579e-03,  1.76150352e-03,
  193.        -3.22385505e-03,  2.90875882e-03,  5.02839684e-05, -1.14429742e-04,
  194.        -5.31699508e-04, -8.26314092e-04,  2.93190777e-03, -2.11944059e-03,
  195.         1.48001313e-03, -7.52605498e-04, -3.57236713e-04, -5.22516668e-04,
  196.         2.61961669e-03,  3.03197652e-04, -1.64348260e-03, -4.60579991e-04,
  197.        -9.09250230e-04, -5.31461090e-04, -1.46765262e-04,  2.12563574e-03,
  198.        -5.15879691e-03,  1.95128843e-03, -2.75124237e-03,  1.47037208e-04,
  199.         1.79392099e-03, -1.96666270e-03,  1.08301267e-03,  4.37385961e-03,
  200.        -4.82087210e-03,  2.73622572e-04,  3.06653976e-03, -2.21773610e-03,
  201.         1.84828043e-03,  2.64829397e-03, -1.01720542e-03, -2.35607475e-03,
  202.        -9.36273485e-04, -7.86982477e-04,  1.83547661e-03,  1.62300467e-03,
  203.        -1.14835799e-03, -1.26681849e-03,  1.42568350e-03, -3.46705317e-04,
  204.         2.09825858e-03, -2.44937837e-05, -2.90337205e-03, -1.08496472e-03,
  205.         3.05589288e-04,  1.29439682e-03, -2.16550380e-03,  8.50073993e-05,
  206.        -1.33325532e-03,  1.37008354e-03, -1.49320811e-04, -2.12598592e-03,
  207.         4.46293503e-04, -8.99523497e-04,  1.00542605e-03, -1.14213303e-03,
  208.         6.41749799e-03, -2.33733654e-03,  1.65421516e-03,  3.71962786e-04,
  209.        -1.46562234e-03,  2.77005881e-03, -8.79745930e-04, -2.85597146e-03,
  210.         1.24006718e-03,  1.71992928e-04, -1.01399422e-03,  2.18090415e-03,
  211.         1.47879124e-04,  4.05319035e-04,  6.32736832e-04, -1.24635920e-03,
  212.        -1.49516761e-03,  6.43692911e-04, -3.87147069e-04, -4.15816903e-04,
  213.        -4.06179577e-04,  6.77749515e-04, -1.76429749e-04,  3.32884490e-04,
  214.         1.57438964e-03, -1.30302832e-03, -1.12066418e-03,  1.56072900e-03,
  215.        -5.28566539e-04, -8.62061977e-04,  4.80669364e-03,  3.91487032e-04,
  216.         1.07408315e-03,  1.80875883e-03,  1.74738467e-04,  1.01762265e-03,
  217.         1.25486404e-04, -6.29387796e-05, -9.78223979e-04,  9.80060548e-04,
  218.        -3.28290835e-03,  9.39585268e-04, -2.60937959e-04,  1.47106871e-03,
  219.         1.77498162e-03, -1.85396895e-03, -2.31282413e-03, -3.72719020e-04,
  220.         3.00113112e-04, -1.19842589e-04, -1.36920810e-03,  3.01828235e-03,
  221.        -5.10364771e-04,  7.14436173e-04, -4.98756766e-04,  8.36931169e-04,
  222.        -5.62749803e-04,  1.91614032e-03, -2.49107555e-03, -1.74377114e-04,
  223.         5.65234572e-04,  6.11599535e-04, -1.15366653e-03,  1.12172216e-04,
  224.         4.09480184e-04, -6.02588058e-04,  2.46616453e-03, -2.29165331e-03,
  225.         4.99863178e-04,  4.32031974e-03, -1.91142410e-03, -6.56396151e-05,
  226.         2.36150622e-03, -1.17996708e-03,  1.95114315e-03, -1.02481991e-03,
  227.         1.15659833e-03,  2.85096467e-05, -2.71784514e-03, -2.13386118e-03,
  228.         1.55009702e-03, -1.11348554e-03, -1.90751627e-03,  7.24643469e-05,
  229.         1.93396583e-03,  6.35944307e-04, -1.65932253e-03,  1.10460445e-03,
  230.        -2.13388354e-03, -8.78058374e-04,  1.29975379e-04, -2.18156725e-03,
  231.         2.84444913e-03, -1.26660615e-03,  1.69638544e-03, -3.00370902e-03,
  232.         2.15679407e-04, -4.56951559e-04,  4.16825712e-03,  4.66462225e-04,
  233.        -1.35587901e-03,  1.07486174e-03, -8.08518380e-04, -7.95632601e-04,
  234.        -1.31630152e-03,  6.81914389e-05, -1.70946494e-03, -2.34656036e-05,
  235.         4.75030392e-04,  9.45765525e-04,  1.60865486e-04,  2.65451893e-03,
  236.         2.58754194e-03,  1.77779421e-03, -5.28949872e-03, -1.13114342e-03,
  237.         1.82129443e-05, -1.29653513e-03,  1.29612908e-03, -5.70680946e-04,
  238.         7.11333007e-04, -2.22627446e-03, -1.54956058e-03,  1.17816031e-04,
  239.         7.45616853e-04,  1.22104958e-03,  9.33341682e-04, -1.66025013e-03,
  240.        -6.31917268e-04,  1.69041380e-03, -4.95493412e-04, -6.11189753e-04,
  241.        -2.92167068e-03,  1.04411691e-03,  1.22471154e-03,  1.31848827e-03,
  242.        -3.94135714e-06, -4.44556400e-03,  1.96090341e-03, -2.16497853e-03,
  243.        -1.72711909e-04,  2.95315683e-03, -7.64977187e-04, -3.71760502e-03,
  244.        -2.17189267e-03,  7.15553761e-05,  7.80671835e-05, -2.00032070e-03,
  245.        -1.39757991e-03,  6.77466393e-04,  9.14301723e-04, -1.25657767e-03,
  246.         4.27499413e-04,  5.51234931e-04, -1.03604048e-03,  1.28062442e-03,
  247.        -3.05548310e-05,  8.94702971e-05,  1.29890442e-03, -4.81747091e-04,
  248.         7.56133348e-04,  1.55855343e-03,  1.13686547e-03, -8.55661929e-04,
  249.        -2.12424621e-03,  1.23820826e-03,  2.30076164e-03, -1.15603954e-03,
  250.        -1.17508695e-03,  8.90180469e-04, -4.82853502e-04, -1.17086247e-03,
  251.        -8.97310674e-05,  3.44923884e-03, -4.29982692e-03, -2.05725431e-04,
  252.         1.25605613e-03,  8.70946795e-04, -1.21130794e-03,  2.70979106e-03,
  253.        -1.69864669e-03,  6.85799867e-04, -1.38894096e-03,  8.97139311e-04,
  254.        -2.37897038e-04, -3.47964466e-04, -7.21972436e-04, -9.57295299e-04,
  255.        -4.25600633e-03, -2.40505114e-03,  2.66962871e-03, -2.93772668e-04,
  256.         4.76829708e-04, -4.57264483e-04, -4.27111983e-04,  1.11941621e-03,
  257.         1.53720379e-04,  1.01309642e-03,  9.25563276e-04, -1.85696036e-03,
  258.        -1.09097734e-03, -1.42387301e-03,  3.71515751e-04, -8.83851200e-04,
  259.        -4.77556139e-04,  8.95302743e-04, -2.02618167e-03,  1.42284110e-03,
  260.         4.69524413e-04, -5.60089946e-04, -1.09597668e-03,  6.04119152e-04,
  261.         7.17248768e-04, -2.52187252e-04,  2.07419321e-03, -1.79142505e-03,
  262.        -5.71248308e-03,  7.69369304e-04,  1.91691145e-03,  4.37930226e-04,
  263.        -1.33608282e-03,  4.70664352e-04,  8.32937658e-04, -4.21281904e-04,
  264.        -3.07039544e-03, -1.38933212e-03,  2.07934156e-03,  1.01882592e-03,
  265.        -1.72524154e-03,  3.21514904e-03, -1.14127994e-03, -2.61555612e-03,
  266.         2.39065289e-03,  2.31847167e-04,  2.17926130e-03,  2.10806727e-04,
  267.         9.18816775e-04,  7.57198781e-04, -2.36980245e-03,  3.42942774e-04,
  268.        -1.04760006e-03, -1.78744271e-03, -8.74478370e-04,  2.78469175e-03,
  269.        -7.93006271e-04, -2.60777771e-03,  6.65482134e-04, -5.36628067e-04,
  270.         4.89111990e-04,  2.17217952e-04,  1.28579512e-03,  8.66245478e-04],
  271.       dtype=float32), array([ 0.00335322, -0.00148289, -0.00107422, ..., -0.00025483,
  272.        -0.00156629,  0.00116636], dtype=float32), array([-2.6028194e-03, -5.1744282e-05,  6.2261336e-04, ...,
  273.        -4.1605160e-04, -1.8741619e-03,  7.8527154e-03], dtype=float32)]
  274. 600
  275. 600
  276. 1200
  277. 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

 

 

  1. import librosa
  2. import matplotlib.pyplot as plt
  3. import numpy as np
  4. import pywt
  5. import librosa.display
  6. from pywt import wavedec
  7. plt.rcParams['font.sans-serif']=['SimHei']
  8. plt.rcParams['axes.unicode_minus']=False
  9. wav, sr_ret = librosa.load('E:\PycharmProjects\pythonProject\AudioClassification-Pytorch-master\dataset/audio/fold1/1_001.wav',sr=48000,duration=0.1)
  10.  
  11. coeffs = wavedec(wav, 'db1', level=3)
  12. cA3, cD3, cD2, cD1 = coeffs
  13. print(coeffs)
  14. print(cA3.shape[0])
  15. print(cD3.shape[0])
  16. print(cD2.shape[0])
  17. print(cD1.shape[0])
  18.  
  19. plt.figure('cA3')
  20. x1 = range(0,600)
  21. y1 = cD3
  22. #创建图并命名
  23. ax1 = plt.gca()
  24. #设置x轴、y轴名称
  25. ax1.set_xlabel('分解系数')
  26. ax1.set_ylabel('幅度')
  27. ax1.plot(x1, y1, color='b', linewidth=1, alpha=1)
  28.  
  29. x2 = range(0, 600)
  30. y2 = cD3
  31. #创建图并命名
  32. plt.figure('cD3')
  33. ax2 = plt.gca()
  34. #设置x轴、y轴名称
  35. ax2.set_xlabel('分解系数')
  36. ax2.set_ylabel('幅度')
  37. ax2.plot(x2, y2, color='b', linewidth=1, alpha=1)
  38.  
  39. x3 = range(0,1200)
  40. y3 = cD2
  41. #创建图并命名
  42. plt.figure('cD2')
  43. ax3 = plt.gca()
  44. #设置x轴、y轴名称
  45. ax3.set_xlabel('分解系数')
  46. ax3.set_ylabel('幅度')
  47. ax3.plot(x3, y3, color='b', linewidth=1, alpha=1)
  48.  
  49. x4 = range(0,2400)
  50. y4 = cD1
  51. #创建图并命名
  52. plt.figure('cD1')
  53. ax4 = plt.gca()
  54. #设置x轴、y轴名称
  55. ax4.set_xlabel('分解系数')
  56. ax4.set_ylabel('幅度')
  57. ax4.plot(x4, y4, color='b', linewidth=1, alpha=1)
  58. 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。

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