当我注意到使用基于 scipys FFT 和使用 numpys 方法得到不同结果时，我试图计算声波的自相关。
我使用的 4 个功能：
def c1(x):
return np.correlate(x,x,'full')
def c2(x):
return np.convolve(x,x[::-1])
def c3(x):
return np.int16(np.rint(signal.fftconvolve(x,x[::-1])))
def c4(x):
return np.int16(np.rint(signal.correlate(x,x,method='fft')))
它对简单数组都有好处，我什至创建了一个检查大型随机 numpy 数组的方法，它们返回相同的结果：
for i in range(1000):
x = np.random.randint(-200,200,100000)
r1,r2,r3,r4 = c1(x),c2(x),c3(x),c4(x)
if not (np.allclose(r1,r2) and np.allclose(r1,r3) and np.allclose(r1,r4)):
return x
它没有失败过一次。但是，当我在真实的声音数据（由 scipy 从 wav 中读取）上尝试它们时，结果发现c1(x)=c2(x)!=c3(x)=c4(x)。我还注意到 c3(x) 和 c4(x) 具有与 c1(x) 和 c2(x) 相同的精确值，除了它们在 c1 的许多地方（尤其是中心）有大块 0-s而c2不。除了那些缺失值之外，它们是相同的。
有人可以告诉我我做错了什么吗？为什么在 c3 和 c4 中有缺失值（0-s），为什么只有在我处理真正的 wav 而不是随机数组时才会发生这种情况？谢谢。
编辑
这是一个示例数组（长度为 600），其函数的结果不匹配：
array([ 1048, 1052, 1122, 1066, 972, 992, 1086, 1072, 1099,
1308, 1373, 1388, 1581, 1749, 1781, 1928, 2122, 2158,
2308, 2539, 2654, 2787, 3071, 3193, 3196, 3328, 3154,
2896, 2771, 2529, 2231, 1952, 1616, 1157, 760, 406,
-31, -518, -897, -1229, -1512, -1769, -2099, -2420, -2755,
-3124, -3486, -3697, -3821, -3965, -3970, -3914, -3932, -3935,
-3952, -3971, -3974, -3913, -3826, -3741, -3621, -3529, -3465,
-3381, -3257, -3097, -2875, -2562, -2299, -2069, -1805, -1598,
-1388, -1197, -931, -645, -355, -10, 307, 610, 840,
989, 1153, 1356, 1471, 1567, 1719, 1809, 1760, 1700,
1649, 1552, 1476, 1407, 1292, 1187, 1049, 886, 741,
529, 299, 94, -107, -302, -454, -613, -752, -909,
-1129, -1288, -1442, -1604, -1689, -1806, -1959, -2042, -2115,
-2223, -2271, -2334, -2380, -2383, -2335, -2243, -2170, -2114,
-2063, -1985, -1927, -1795, -1689, -1567, -1402, -1235, -1034,
-858, -720, -639, -528, -396, -249, -92, 36, 153,
253, 323, 363, 456, 520, 572, 667, 741, 825,
818, 821, 880, 876, 884, 928, 958, 985, 990,
963, 902, 858, 776, 723, 683, 627, 582, 509,
466, 383, 305, 219, 142, 116, 85, 54, 17,
-17, -72, -81, -46, -39, 3, 84, 113, 130,
179, 222, 261, 296, 310, 356, 432, 474, 558,
623, 633, 689, 787, 846, 882, 957, 1027, 1098,
1169, 1202, 1233, 1271, 1291, 1376, 1378, 1370, 1419,
1377, 1359, 1365, 1332, 1301, 1285, 1177, 1128, 1108,
1009, 944, 909, 844, 759, 722, 655, 616, 564,
520, 510, 514, 467, 410, 428, 396, 410, 475,
531, 567, 609, 645, 715, 787, 865, 943, 993,
1111, 1215, 1333, 1416, 1412, 1450, 1669, 1880, 1933,
2038, 2200, 2325, 2417, 2528, 2683, 2854, 3052, 3276,
3504, 3697, 3917, 4163, 4338, 4313, 4098, 3846, 3571,
3100, 2475, 2011, 1621, 1125, 763, 598, 339, -62,
-528, -1038, -1606, -2171, -2748, -3271, -3671, -4002, -4294,
-4436, -4478, -4600, -4672, -4583, -4451, -4454, -4449, -4383,
-4371, -4416, -4513, -4553, -4499, -4378, -4187, -3933, -3642,
-3432, -3192, -2966, -2804, -2696, -2557, -2282, -1996, -1720,
-1396, -1013, -680, -362, 64, 518, 918, 1301, 1580,
1798, 1987, 2071, 2131, 2220, 2305, 2348, 2394, 2472,
2473, 2403, 2327, 2222, 2009, 1745, 1536, 1310, 1068,
813, 540, 289, 87, -87, -261, -439, -597, -747,
-880, -1055, -1246, -1407, -1595, -1748, -1868, -1967, -2043,
-2078, -2112, -2206, -2272, -2316, -2365, -2440, -2486, -2516,
-2531, -2534, -2491, -2393, -2309, -2163, -1981, -1814, -1638,
-1433, -1236, -1102, -976, -824, -656, -472, -253, -34,
111, 297, 456, 543, 620, 748, 834, 859, 890,
915, 925, 942, 962, 972, 982, 1003, 1029, 999,
968, 904, 848, 757, 677, 602, 536, 470, 418,
375, 282, 209, 138, 55, -47, -157, -243, -330,
-429, -468, -477, -493, -517, -509, -510, -467, -357,
-289, -251, -201, -125, -59, -1, 97, 206, 334,
463, 597, 700, 783, 897, 981, 1030, 1030, 1032,
1094, 1153, 1176, 1193, 1187, 1173, 1191, 1223, 1228,
1220, 1222, 1189, 1102, 1046, 1042, 1049, 1007, 982,
952, 885, 877, 907, 839, 746, 716, 652, 638,
628, 540, 483, 533, 474, 434, 461, 448, 469,
420, 410, 514, 527, 444, 450, 456, 461, 546,
653, 778, 962, 1115, 1284, 1369, 1365, 1465, 1588,
1632, 1657, 1737, 1804, 1973, 2118, 2157, 2257, 2365,
2465, 2497, 2626, 2750, 2765, 2696, 2755, 2960, 3020,
3094, 3315, 3632, 3837, 3980, 4042, 3927, 3592, 3067,
2474, 1995, 1582, 1225, 1047, 905, 778, 492, 30,
-486, -1079, -1802, -2394, -2845, -3289, -3617, -3865, -4141,
-4392, -4595, -4756, -4711, -4586, -4440, -4328, -4287, -4335,
-4466, -4543, -4581, -4571, -4445, -4179, -3824, -3514, -3265,
-3093, -2956, -2859, -2796, -2668, -2473, -2224, -2034, -1852,
-1645, -1415, -1166, -778, -338, 36, 443, 788, 1051,
1242, 1355, 1391, 1486, 1661, 1869], dtype=int16)
对其进行简单检查，我们得到：
i: 594 c1(x)[i]: -11191 c4(x)[i]: 0
i: 595 c1(x)[i]: 8216 c4(x)[i]: 0
i: 596 c1(x)[i]: -32326 c4(x)[i]: 0
i: 597 c1(x)[i]: -30734 c4(x)[i]: 0
i: 598 c1(x)[i]: -23815 c4(x)[i]: 0
i: 599 c1(x)[i]: 4296 c4(x)[i]: 0
i: 600 c1(x)[i]: -23815 c4(x)[i]: 0
i: 601 c1(x)[i]: -30734 c4(x)[i]: 0
i: 602 c1(x)[i]: -32326 c4(x)[i]: 0
i: 603 c1(x)[i]: 8216 c4(x)[i]: 0
i: 604 c1(x)[i]: -11191 c4(x)[i]: 0
如您所见，在对称模式的自相关中间，不应该有零。
