{"id":65037,"date":"2026-01-24T13:04:38","date_gmt":"2026-01-24T05:04:38","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/65037.html"},"modified":"2026-01-24T13:04:38","modified_gmt":"2026-01-24T05:04:38","slug":"%e5%9f%ba%e4%ba%8e%e5%8f%af%e5%ad%a6%e4%b9%a0morlet%e5%b0%8f%e6%b3%a2%e5%8c%b9%e9%85%8d%e6%bb%a4%e6%b3%a2%e5%92%8c%e7%bb%9f%e8%ae%a1%e7%89%b9%e5%be%81%e8%9e%8d%e5%90%88%e7%9a%84%e5%bc%95%e5%8a%9b","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/65037.html","title":{"rendered":"\u57fa\u4e8e\u53ef\u5b66\u4e60Morlet\u5c0f\u6ce2\u5339\u914d\u6ee4\u6ce2\u548c\u7edf\u8ba1\u7279\u5f81\u878d\u5408\u7684\u5f15\u529b\u6ce2\u4fe1\u53f7\u68c0\u6d4b\u7b97\u6cd5\uff08\u7b97\u6cd5\u5b8c\u5584\u4e2d\uff0cPython\uff09"},"content":{"rendered":"

\u7b97\u6cd5\u7528\u4e8e\u5f15\u529b\u6ce2\u4fe1\u53f7\u68c0\u6d4b\u4efb\u52a1，\u7ed3\u5408\u4e86\u4f20\u7edf\u5339\u914d\u6ee4\u6ce2\u7684\u7269\u7406\u53ef\u89e3\u91ca\u6027\u548c\u6df1\u5ea6\u5b66\u4e60\u7684\u5b66\u4e60\u80fd\u529b\u3002\u9996\u5148，\u7b97\u6cd5\u5b9a\u4e49\u4e86\u53ef\u5b66\u4e60\u7684Morlet\u5c0f\u6ce2\u57fa\u51fd\u6570，\u8fd9\u4e9b\u5c0f\u6ce2\u7684\u9891\u7387\u3001\u5c3a\u5ea6\u7b49\u53c2\u6570\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u53ef\u4ee5\u4f18\u5316\u8c03\u6574，\u80fd\u591f\u81ea\u9002\u5e94\u5730\u63d0\u53d6\u5f15\u529b\u6ce2\u4fe1\u53f7\u7684\u7279\u5f81\u3002\u7136\u540e，\u901a\u8fc7\u5339\u914d\u6ee4\u6ce2\u5668\u7ec4\u5bf9\u5de6\u53f3\u63a2\u6d4b\u5668\u7684\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u8fdb\u884c\u5377\u79ef\u64cd\u4f5c，\u63d0\u53d6\u4fe1\u53f7\u7684\u5e45\u5ea6\u548c\u4f4d\u7f6e\u7279\u5f81\u3002\u4e3a\u4e86\u63d0\u9ad8\u7279\u5f81\u7684\u8868\u5f81\u80fd\u529b，\u7b97\u6cd5\u91c7\u7528\u4e86\u5206\u6876\u6700\u5927\u6c60\u5316\u7b56\u7565，\u5c06\u5377\u79ef\u54cd\u5e94\u5206\u6210\u591a\u4e2a\u65f6\u95f4\u6876，\u5e76\u63d0\u53d6\u6bcf\u4e2a\u6876\u4e2d\u7684\u6700\u5927\u54cd\u5e94\u503c\u53ca\u5176\u76f8\u5bf9\u4f4d\u7f6e，\u8fd9\u6837\u65e2\u4fdd\u7559\u4e86\u5173\u952e\u7684\u65f6\u95f4\u4fe1\u606f，\u53c8\u51cf\u5c11\u4e86\u6570\u636e\u7ef4\u5ea6\u3002\u63a5\u7740，\u5c06\u8fd9\u4e9b\u4ece\u5c0f\u6ce2\u6ee4\u6ce2\u5668\u63d0\u53d6\u7684\u7279\u5f81\u4e0e\u9884\u8ba1\u7b97\u7684\u7edf\u8ba1\u7279\u5f81（\u5982PSD\u6f02\u79fb\u3001\u4fe1\u566a\u6bd4\u7b49）\u8fdb\u884c\u62fc\u63a5，\u5f62\u6210\u4e00\u4e2a\u7efc\u5408\u7684\u7279\u5f81\u5411\u91cf\u3002\u6700\u540e，\u901a\u8fc7\u4e00\u4e2a\u591a\u5c42\u611f\u77e5\u673a\u5206\u7c7b\u5668\u5bf9\u8fd9\u4e2a\u7efc\u5408\u7279\u5f81\u5411\u91cf\u8fdb\u884c\u5904\u7406，\u8f93\u51fa\u4fe1\u53f7\u5b58\u5728\u7684\u6982\u7387\u3002\u6574\u4e2a\u6a21\u578b\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4f7f\u7528\u4e86\u4e8c\u5143\u4ea4\u53c9\u71b5\u635f\u5931\u51fd\u6570，\u5e76\u91c7\u7528\u4e86\u5b66\u4e60\u7387\u8c03\u5ea6\u548c\u65e9\u505c\u7b56\u7565\u6765\u4f18\u5316\u8bad\u7ec3\u8fc7\u7a0b，\u786e\u4fdd\u6a21\u578b\u80fd\u591f\u6709\u6548\u5730\u5b66\u4e60\u533a\u5206\u5f15\u529b\u6ce2\u4fe1\u53f7\u548c\u566a\u58f0\u3002<\/p>\n<\/p>\n

\"\"<\/p>\n

\u7b97\u6cd5\u6b65\u9aa4<\/p>\n

\u6570\u636e\u51c6\u5907\u9636\u6bb5：\u52a0\u8f7d\u5de6\u53f3\u63a2\u6d4b\u5668\u7684\u5f15\u529b\u6ce2\u5e94\u53d8\u6570\u636e，\u540c\u65f6\u52a0\u8f7d\u9884\u8ba1\u7b97\u7684\u7edf\u8ba1\u7279\u5f81，\u5982\u8ddd\u79bb\u6570\u636e\u5b54\u7684\u6700\u8fd1\u65f6\u95f4\u3001PSD\u6f02\u79fb\u503c\u3001\u5f52\u4e00\u5316\u56e0\u5b50\u7b49，\u5e76\u5c06\u6570\u636e\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u9a8c\u8bc1\u96c6\u3002<\/p>\n

\u6a21\u578b\u521d\u59cb\u5316\u9636\u6bb5：\u6784\u5efa\u5305\u542b6\u4e2a\u4e0d\u540c\u4e2d\u5fc3\u9891\u7387（15Hz\u300120Hz\u300130Hz\u300140Hz\u300150Hz\u300160Hz）\u7684\u53ef\u5b66\u4e60Morlet\u5c0f\u6ce2\u5e93，\u6bcf\u4e2a\u5c0f\u6ce2\u7684\u6301\u7eed\u65f6\u95f4\u4e3a0.75\u79d2，\u521d\u59cb\u5c3a\u5ea6\u4e3a0.05\u3002<\/p>\n

\u7279\u5f81\u63d0\u53d6\u9636\u6bb5：\u5c06\u5de6\u53f3\u63a2\u6d4b\u5668\u7684\u6570\u636e\u5206\u522b\u4e0e\u6bcf\u4e2a\u5c0f\u6ce2\u8fdb\u884c\u590d\u6570\u5377\u79ef\u8fd0\u7b97，\u8ba1\u7b97\u5b9e\u90e8\u548c\u865a\u90e8\u7684\u54cd\u5e94，\u7136\u540e\u8fdb\u884c\u76f8\u4f4d\u6821\u6b63\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u4fe1\u53f7\u5bf9\u9f50\u6548\u679c\u3002\u5bf9\u5de6\u53f3\u63a2\u6d4b\u5668\u7684\u6821\u6b63\u540e\u54cd\u5e94\u53d6\u6700\u5927\u503c，\u4ee5\u589e\u5f3a\u4fe1\u53f7\u5e76\u6291\u5236\u566a\u58f0\u3002<\/p>\n

\u7279\u5f81\u964d\u7ef4\u9636\u6bb5：\u5c06\u5377\u79ef\u54cd\u5e94\u6309\u65f6\u95f4\u5212\u5206\u4e3a\u5927\u5c0f\u4e3a26\u7684\u6876，\u5bf9\u6bcf\u4e2a\u6876\u63d0\u53d6\u7edd\u5bf9\u503c\u6700\u5927\u7684\u54cd\u5e94\u503c\u53ca\u5176\u5728\u6876\u5185\u7684\u4f4d\u7f6e\u7d22\u5f15，\u7136\u540e\u4ece\u6240\u6709\u6876\u4e2d\u9009\u53d6\u524d3\u4e2a\u6700\u5927\u7684\u54cd\u5e94\u503c\u53ca\u5176\u5f52\u4e00\u5316\u4f4d\u7f6e，\u5f62\u6210\u6bcf\u4e2a\u5c0f\u6ce2\u76846\u7ef4\u7279\u5f81\u3002<\/p>\n

\u7279\u5f81\u878d\u5408\u9636\u6bb5：\u5c06\u6240\u67096\u4e2a\u5c0f\u6ce2\u63d0\u53d6\u7684\u7279\u5f81（\u517136\u7ef4）\u4e0e\u9884\u8ba1\u7b97\u768420\u7ef4\u7edf\u8ba1\u7279\u5f81\u8fdb\u884c\u62fc\u63a5，\u5f62\u6210\u4e00\u4e2a56\u7ef4\u7684\u7efc\u5408\u7279\u5f81\u5411\u91cf\u3002<\/p>\n

\u5206\u7c7b\u51b3\u7b56\u9636\u6bb5：\u5c06\u7efc\u5408\u7279\u5f81\u5411\u91cf\u8f93\u5165\u5230\u4e00\u4e2a\u4e24\u5c42\u5168\u8fde\u63a5\u795e\u7ecf\u7f51\u7edc\u5206\u7c7b\u5668\u4e2d，\u7b2c\u4e00\u5c42\u670964\u4e2a\u795e\u7ecf\u5143\u5e76\u4f7f\u7528ReLU\u6fc0\u6d3b\u51fd\u6570，\u7b2c\u4e8c\u5c42\u8f93\u51fa\u4e00\u4e2a\u6807\u91cf\u503c，\u7ecf\u8fc7Sigmoid\u51fd\u6570\u8f6c\u6362\u4e3a\u4fe1\u53f7\u5b58\u5728\u7684\u6982\u7387\u3002<\/p>\n

\u8bad\u7ec3\u4f18\u5316\u9636\u6bb5：\u4f7f\u7528\u4e8c\u5143\u4ea4\u53c9\u71b5\u635f\u5931\u51fd\u6570\u548cAdam\u4f18\u5316\u5668\u8fdb\u884c\u8bad\u7ec3，\u91c7\u7528ReduceLROnPlateau\u5b66\u4e60\u7387\u8c03\u5ea6\u7b56\u7565\u5728\u9a8c\u8bc1\u635f\u5931\u505c\u6ede\u65f6\u964d\u4f4e\u5b66\u4e60\u7387，\u5e76\u8bbe\u7f6e\u65e9\u505c\u673a\u5236\u5728\u8fde\u7eed\u591a\u4e2aepoch\u9a8c\u8bc1\u635f\u5931\u65e0\u6539\u5584\u65f6\u7ec8\u6b62\u8bad\u7ec3\u3002<\/p>\n

\u6a21\u578b\u8bc4\u4f30\u9636\u6bb5：\u5728\u9a8c\u8bc1\u96c6\u4e0a\u8ba1\u7b97\u6a21\u578b\u7684\u9884\u6d4b\u6982\u7387，\u5206\u6790\u4fe1\u53f7\u548c\u566a\u58f0\u7684\u5206\u5e03\u5dee\u5f02，\u4f7f\u7528\u8d1d\u53f6\u65af\u6821\u51c6\u65b9\u6cd5\u5bf9\u8f93\u51fa\u6982\u7387\u8fdb\u884c\u6821\u51c6，\u5e76\u8ba1\u7b97\u9006\u8bef\u62a5\u7387(IFAR)\u7b49\u68c0\u6d4b\u6027\u80fd\u6307\u6807\u3002<\/p>\n

\u7ed3\u679c\u53ef\u89c6\u5316\u9636\u6bb5：\u7ed8\u5236\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u635f\u5931\u66f2\u7ebf，\u53ef\u89c6\u5316\u5b66\u4e60\u5230\u7684\u5c0f\u6ce2\u5f62\u72b6，\u5206\u6790\u5355\u4e2a\u6837\u672c\u7684\u65f6\u9891\u56fe\u548c\u5c0f\u6ce2\u54cd\u5e94，\u6bd4\u8f83\u65b0\u65e7IFAR\u503c\u4ee5\u8bc4\u4f30\u7b97\u6cd5\u6539\u8fdb\u6548\u679c\u3002<\/p>\n

import torch.nn as nn
\nimport torch.nn.functional as F
\nimport torch<\/p>\n

# \u5b9a\u4e49\u53ef\u5b66\u4e60\u7684Morlet\u5c0f\u6ce2\u7c7b
\nclass LearnableMorlet(nn.Module):
\n def __init__(self, duration=1.0, sample_rate=256, frequency=1.0, scale=0.2):
\n super().__init__()
\n self.duration = duration # \u5c0f\u6ce2\u6301\u7eed\u65f6\u95f4（\u79d2）
\n self.sample_rate = sample_rate # \u91c7\u6837\u7387（Hz）
\n self.freq = frequency # \u5c0f\u6ce2\u4e2d\u5fc3\u9891\u7387（Hz）
\n # \u4f7f\u7528\u5bf9\u6570\u5c3a\u5ea6\u4f5c\u4e3a\u53ef\u8bad\u7ec3\u53c2\u6570，\u9632\u6b62\u6536\u655b\u5230\u96f6
\n self.log_scale = nn.Parameter(torch.tensor(torch.log(torch.tensor(scale)), dtype=torch.float32))<\/p>\n

@property
\n def scale(self):
\n """\u8fd4\u56de\u5b9e\u9645\u5c3a\u5ea6\u503c（log_scale\u7684\u6307\u6570）"""
\n return torch.exp(self.log_scale)<\/p>\n

def forward(self, return_t=False):
\n # \u83b7\u53d6\u53c2\u6570\u8bbe\u5907\u4fe1\u606f
\n dev = self.log_scale.device
\n dt = 1.0 \/ self.sample_rate
\n # \u751f\u6210\u65f6\u95f4\u5e8f\u5217
\n t = torch.arange(
\n -self.duration \/ 2,
\n self.duration \/ 2,
\n step=dt,
\n device=dev,
\n dtype=torch.float32,
\n )
\n # \u83b7\u53d6\u5b9e\u9645\u5c3a\u5ea6\u503c
\n actual_scale = self.scale
\n # \u9ad8\u65af\u5305\u7edc\u51fd\u6570
\n gauss = torch.exp(-0.5 * (t \/ actual_scale) ** 2)
\n # \u6b63\u5f26\u632f\u8361\u51fd\u6570
\n sinusoid = torch.exp(1j * 2 * torch.pi * self.freq * t)
\n # \u7ec4\u5408\u6210\u590d\u6570\u5c0f\u6ce2
\n wavelet = gauss * sinusoid<\/p>\n

# \u5f52\u4e00\u5316\u5904\u7406
\n norm = wavelet.norm(p=2)
\n if norm > 0:
\n wavelet = wavelet \/ norm<\/p>\n

return wavelet if not return_t else (wavelet, t)<\/p>\n

# \u5b9a\u4e49\u5339\u914d\u6ee4\u6ce2\u5668\u7ec4
\nclass MatchedFilterBank(nn.Module):
\n def __init__(self, wavelet_properties):
\n super().__init__()
\n # \u521b\u5efa\u591a\u4e2a\u53ef\u5b66\u4e60Morlet\u5c0f\u6ce2
\n self.wavelets = nn.ModuleList([
\n LearnableMorlet(
\n duration=wp["duration"],
\n sample_rate=wp.get("sample_rate", 256),
\n frequency=wp["frequency"],
\n scale=wp["scale"],
\n ) for wp in wavelet_properties
\n ])
\n # \u6bcf\u4e2a\u5c0f\u6ce2\u63d0\u53d66\u4e2a\u7279\u5f81（3\u4e2a\u6700\u5927\u503c+3\u4e2a\u4f4d\u7f6e\u7d22\u5f15）
\n self.features_per_wavelet = 6<\/p>\n

def forward(self, x_left, x_right):
\n B = x_left.size(0) # \u6279\u5927\u5c0f
\n device = x_left.device
\n all_feats = [] # \u5b58\u50a8\u6240\u6709\u7279\u5f81<\/p>\n

# \u5bf9\u6bcf\u4e2a\u5c0f\u6ce2\u8fdb\u884c\u5904\u7406
\n for idx, wavelet in enumerate(self.wavelets):
\n # \u751f\u6210\u590d\u6570\u6838\u51fd\u6570
\n kernel = wavelet() # \u5f62\u72b6(L,)
\n real_k = kernel.real.view(1, 1, -1).to(device) # \u5b9e\u90e8\u6838
\n imag_k = kernel.imag.view(1, 1, -1).to(device) # \u865a\u90e8\u6838<\/p>\n

# \u5904\u7406\u5de6\u53f3\u63a2\u6d4b\u5668\u901a\u9053
\n combined_responses = []
\n for x_channel in [x_left, x_right]:
\n # \u5377\u79ef\u8fd0\u7b97
\n real_out = F.conv1d(x_channel, real_k, padding=0) # \u5b9e\u90e8\u5377\u79ef
\n imag_out = F.conv1d(x_channel, imag_k, padding=0) # \u865a\u90e8\u5377\u79ef<\/p>\n

# \u76f8\u4f4d\u6821\u6b63
\n phases = torch.atan2(imag_out, real_out) # \u8ba1\u7b97\u76f8\u4f4d
\n real_r = real_out * torch.cos(phases) + imag_out * torch.sin(phases) # \u6821\u6b63\u540e\u7684\u5b9e\u90e8
\n combined_responses.append(real_r)<\/p>\n

# \u5bf9\u5de6\u53f3\u63a2\u6d4b\u5668\u54cd\u5e94\u53d6\u6700\u5927\u503c
\n combined_response = torch.max(combined_responses[0], combined_responses[1])<\/p>\n

# \u5206\u6876\u5904\u7406：\u5c06\u4fe1\u53f7\u5206\u6210\u591a\u4e2a\u6876，\u63d0\u53d6\u6bcf\u4e2a\u6876\u7684\u6700\u5927\u503c
\n bucket_size = 26 # \u6876\u5927\u5c0f
\n L_out = combined_response.size(2)
\n num_buckets = L_out \/\/ bucket_size<\/p>\n

# \u91cd\u5851\u4e3a\u6876\u7684\u5f62\u72b6
\n rb = combined_response[…, : num_buckets * bucket_size].reshape(
\n B, 1, num_buckets, bucket_size
\n )
\n # \u8ba1\u7b97\u6bcf\u4e2a\u6876\u7684\u6700\u5927\u503c\u548c\u4f4d\u7f6e
\n bucket_max, bucket_idx = rb.abs().max(dim=3)
\n # \u63d0\u53d6\u524d3\u4e2a\u6700\u5927\u503c\u53ca\u5176\u4f4d\u7f6e
\n top_vals, top_idxs = torch.topk(bucket_max, k=3, dim=2)<\/p>\n

# \u6241\u5e73\u5316\u5904\u7406
\n vals_flat = top_vals.view(B, -1)
\n idxs_norm = top_idxs.view(B, -1).float() \/ num_buckets # \u5f52\u4e00\u5316\u4f4d\u7f6e<\/p>\n

# \u62fc\u63a5\u503c\u7279\u5f81\u548c\u4f4d\u7f6e\u7279\u5f81
\n wavelet_feat = torch.cat([vals_flat, idxs_norm], dim=1) # \u5f62\u72b6(B,6)
\n all_feats.append(wavelet_feat)<\/p>\n

# \u62fc\u63a5\u6240\u6709\u5c0f\u6ce2\u7279\u5f81
\n return torch.cat(all_feats, dim=1)<\/p>\n

# \u5b9a\u4e49\u5b8c\u6574\u6a21\u578b
\nclass FullModel(nn.Module):
\n def __init__(self, wavelet_props, stats_dim=20):
\n super().__init__()
\n self.filterbank = MatchedFilterBank(wavelet_props) # \u5339\u914d\u6ee4\u6ce2\u5668\u7ec4
\n fb_dim = len(wavelet_props) * self.filterbank.features_per_wavelet # \u6ee4\u6ce2\u5668\u7279\u5f81\u7ef4\u5ea6<\/p>\n

# \u5206\u7c7b\u5668：MLP\u7f51\u7edc
\n self.classifier = nn.Sequential(
\n nn.Linear(fb_dim + stats_dim, 64), # \u8f93\u5165\u5c42：\u7279\u5f81+\u7edf\u8ba1\u4fe1\u606f
\n nn.ReLU(), # \u6fc0\u6d3b\u51fd\u6570
\n nn.Linear(64, 1), # \u8f93\u51fa\u5c42：\u4e8c\u5206\u7c7b
\n )<\/p>\n

def forward(self, x_left, x_right, stats):
\n # \u63d0\u53d6\u5c0f\u6ce2\u7279\u5f81
\n f = self.filterbank(x_left, x_right)
\n # \u62fc\u63a5\u5c0f\u6ce2\u7279\u5f81\u548c\u7edf\u8ba1\u7279\u5f81
\n x = torch.cat([f, stats], dim=1)
\n # \u901a\u8fc7\u5206\u7c7b\u5668
\n return self.classifier(x) <\/p>\n<\/p>\n

\"\"<\/p>\n<\/p>\n<\/p>\n

\"\"<\/p>\n<\/p>\n

\"\"<\/p>\n<\/p>\n

\"\"<\/p>\n<\/p>\n<\/p>\n

\"\"<\/p>\n<\/p>\n<\/p>\n

\"\"<\/p>\n

\u62c5\u4efb\u300aMechanical System and Signal Processing\u300b\u300a\u4e2d\u56fd\u7535\u673a\u5de5\u7a0b\u5b66\u62a5\u300b\u300a\u5b87\u822a\u5b66\u62a5\u300b\u300a\u63a7\u5236\u4e0e\u51b3\u7b56\u300b\u7b49\u671f\u520a\u5ba1\u7a3f\u4e13\u5bb6，\u64c5\u957f\u9886\u57df：\u4fe1\u53f7\u6ee4\u6ce2\/\u964d\u566a，\u673a\u5668\u5b66\u4e60\/\u6df1\u5ea6\u5b66\u4e60，\u65f6\u95f4\u5e8f\u5217\u9884\u5206\u6790\/\u9884\u6d4b，\u8bbe\u5907\u6545\u969c\u8bca\u65ad\/\u7f3a\u9677\u68c0\u6d4b\/\u5f02\u5e38\u68c0\u6d4b<\/p>\n

\u53c2\u8003\u6587\u7ae0：<\/p>\n

\u57fa\u4e8e\u53ef\u5b66\u4e60Morlet\u5c0f\u6ce2\u5339\u914d\u6ee4\u6ce2\u548c\u7edf\u8ba1\u7279\u5f81\u878d\u5408\u7684\u5f15\u529b\u6ce2\u4fe1\u53f7\u68c0\u6d4b\u7b97\u6cd5（\u7b97\u6cd5\u5b8c\u5584\u4e2d，Python） – \u54e5\u5ef7\u6839\u6570\u5b66\u5b66\u6d3e\u7684\u6587\u7ae0 https:\/\/zhuanlan.zhihu.com\/p\/1998354219955160550<\/p>\n","protected":false},"excerpt":{"rendered":"

\u7b97\u6cd5\u7528\u4e8e\u5f15\u529b\u6ce2\u4fe1\u53f7\u68c0\u6d4b\u4efb\u52a1，\u7ed3\u5408\u4e86\u4f20\u7edf\u5339\u914d\u6ee4\u6ce2\u7684\u7269\u7406\u53ef\u89e3\u91ca\u6027\u548c\u6df1\u5ea6\u5b66\u4e60\u7684\u5b66\u4e60\u80fd\u529b\u3002\u9996\u5148，\u7b97\u6cd5\u5b9a\u4e49\u4e86\u53ef\u5b66\u4e60\u7684Morlet\u5c0f\u6ce2\u57fa\u51fd\u6570，\u8fd9\u4e9b\u5c0f\u6ce2\u7684\u9891\u7387\u3001\u5c3a\u5ea6\u7b49\u53c2\u6570\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u53ef\u4ee5\u4f18\u5316\u8c03\u6574，\u80fd\u591f\u81ea\u9002\u5e94\u5730\u63d0\u53d6\u5f15\u529b\u6ce2\u4fe1\u53f7\u7684\u7279\u5f81\u3002\u7136\u540e，\u901a\u8fc7\u5339\u914d\u6ee4\u6ce2\u5668\u7ec4\u5bf9\u5de6\u53f3\u63a2\u6d4b\u5668\u7684\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u8fdb\u884c\u5377\u79ef\u64cd\u4f5c，\u63d0\u53d6\u4fe1\u53f7\u7684\u5e45\u5ea6\u548c\u4f4d\u7f6e\u7279\u5f81\u3002\u4e3a\u4e86\u63d0\u9ad8\u7279\u5f81\u7684\u8868\u5f81\u80fd\u529b，\u7b97\u6cd5\u91c7\u7528\u4e86\u5206\u6876\u6700\u5927\u6c60\u5316\u7b56\u7565，\u5c06\u5377\u79ef\u54cd\u5e94\u5206<\/p>\n","protected":false},"author":2,"featured_media":65030,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[81,50,371,62,207,427,3548],"topic":[],"class_list":["post-65037","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-python","tag-50","tag-371","tag-62","tag-207","tag-427","tag-3548"],"yoast_head":"\n\u57fa\u4e8e\u53ef\u5b66\u4e60Morlet\u5c0f\u6ce2\u5339\u914d\u6ee4\u6ce2\u548c\u7edf\u8ba1\u7279\u5f81\u878d\u5408\u7684\u5f15\u529b\u6ce2\u4fe1\u53f7\u68c0\u6d4b\u7b97\u6cd5\uff08\u7b97\u6cd5\u5b8c\u5584\u4e2d\uff0cPython\uff09 - 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