{"id":1177260,"date":"2025-01-15T17:56:00","date_gmt":"2025-01-15T09:56:00","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1177260.html"},"modified":"2025-01-15T17:56:03","modified_gmt":"2025-01-15T09:56:03","slug":"python-%e5%a6%82%e4%bd%95%e5%a4%84%e7%90%86%e8%af%ad%e9%9f%b3%e6%95%b0%e6%8d%ae-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1177260.html","title":{"rendered":"python \u5982\u4f55\u5904\u7406\u8bed\u97f3\u6570\u636e"},"content":{"rendered":"

\"python<\/p>\n

Python\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5904\u7406\u8bed\u97f3\u6570\u636e\uff0c\u5305\u62ec\u8bfb\u53d6\u97f3\u9891\u6587\u4ef6\u3001\u9884\u5904\u7406\u97f3\u9891\u6570\u636e\u3001\u7279\u5f81\u63d0\u53d6\u3001\u8bad\u7ec3\u548c\u8bc4\u4f30\u6a21\u578b\u3001\u751f\u6210\u97f3\u9891\u7b49\u3002<\/strong>\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5176\u4e2d\u7684\u4e00\u4e2a\u65b9\u9762\uff1a\u7279\u5f81\u63d0\u53d6\u3002\u7279\u5f81\u63d0\u53d6\u662f\u8bed\u97f3\u5904\u7406\u4e2d\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u901a\u8fc7\u63d0\u53d6\u5982\u6885\u5c14\u9891\u7387\u5012\u8c31\u7cfb\u6570\uff08MFCC\uff09\u3001\u8272\u8c31\u56fe\uff08Chroma\uff09\u3001\u9891\u8c31\u56fe\uff08Spectrogram\uff09\u7b49\u7279\u5f81\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u97f3\u9891\u4fe1\u53f7\uff0c\u5e76\u4e3a\u540e\u7eed\u7684\u6a21\u578b\u8bad\u7ec3\u63d0\u4f9b\u6709\u6548\u7684\u6570\u636e\u8868\u793a\u3002<\/p>\n<\/p>\n

\u7279\u5f81\u63d0\u53d6\u662f\u97f3\u9891\u5904\u7406\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u6b65\u9aa4\uff0c\u56e0\u4e3a\u76f4\u63a5\u5904\u7406\u539f\u59cb\u97f3\u9891\u6570\u636e\uff08\u5373\u65f6\u95f4\u57df\u6570\u636e\uff09\u901a\u5e38\u662f\u4e0d\u53ef\u884c\u7684\u3002\u7279\u5f81\u63d0\u53d6\u7684\u76ee\u6807\u662f\u5c06\u539f\u59cb\u97f3\u9891\u4fe1\u53f7\u8f6c\u6362\u4e3a\u9002\u5408\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\u5904\u7406\u7684\u7279\u5f81\u8868\u793a\u3002\u5e38\u7528\u7684\u7279\u5f81\u63d0\u53d6\u65b9\u6cd5\u5305\u62ecMFCC\u3001\u8272\u8c31\u56fe\u548c\u9891\u8c31\u56fe\u7b49\u3002\u4ee5\u4e0b\u662f\u5173\u4e8e\u7279\u5f81\u63d0\u53d6\u7684\u8be6\u7ec6\u63cf\u8ff0\uff1a<\/p>\n<\/p>\n

\u4e00\u3001\u8bfb\u53d6\u97f3\u9891\u6587\u4ef6<\/h3>\n<\/p>\n

\u5728\u8fdb\u884c\u8bed\u97f3\u6570\u636e\u5904\u7406\u4e4b\u524d\uff0c\u6211\u4eec\u9996\u5148\u9700\u8981\u8bfb\u53d6\u97f3\u9891\u6587\u4ef6\u3002Python\u4e2d\u6709\u8bb8\u591a\u5e93\u53ef\u4ee5\u7528\u4e8e\u8bfb\u53d6\u97f3\u9891\u6587\u4ef6\uff0c\u5982librosa<\/code>\u3001wave<\/code>\u3001pydub<\/code>\u7b49\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528librosa<\/code>\u8bfb\u53d6\u97f3\u9891\u6587\u4ef6\u3002<\/p>\n<\/p>\n

import librosa<\/p>\n
\u8bfb\u53d6\u97f3\u9891\u6587\u4ef6<\/strong><\/h2>\n
file_path = 'path\/to\/your\/audio\/file.wav'<\/p>\n
audio, sample_rate = librosa.load(file_path, sr=None)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0clibrosa.load<\/code>\u51fd\u6570\u4f1a\u8bfb\u53d6\u97f3\u9891\u6587\u4ef6\u5e76\u8fd4\u56de\u4e24\u4e2a\u503c\uff1a\u97f3\u9891\u65f6\u95f4\u5e8f\u5217\uff08audio<\/code>\uff09\u548c\u91c7\u6837\u7387\uff08sample_rate<\/code>\uff09\u3002sr=None<\/code>\u8868\u793a\u4fdd\u6301\u539f\u59cb\u91c7\u6837\u7387\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u9884\u5904\u7406\u97f3\u9891\u6570\u636e<\/h3>\n<\/p>\n
\u8bfb\u53d6\u97f3\u9891\u6587\u4ef6\u540e\uff0c\u6211\u4eec\u901a\u5e38\u9700\u8981\u5bf9\u97f3\u9891\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u4ee5\u786e\u4fdd\u5176\u8d28\u91cf\u548c\u4e00\u81f4\u6027\u3002\u5e38\u89c1\u7684\u9884\u5904\u7406\u6b65\u9aa4\u5305\u62ec\u53bb\u566a\u3001\u5f52\u4e00\u5316\u3001\u5206\u5e27\u7b49\u3002<\/p>\n<\/p>\n
1\u3001\u53bb\u566a<\/h4>\n<\/p>\n
\u53bb\u566a\u662f\u6307\u53bb\u9664\u97f3\u9891\u4e2d\u7684\u566a\u58f0\uff0c\u4fdd\u7559\u6709\u7528\u7684\u8bed\u97f3\u4fe1\u53f7\u3002\u53ef\u4ee5\u4f7f\u7528\u5404\u79cd\u53bb\u566a\u7b97\u6cd5\uff0c\u5982\u5747\u503c\u6ee4\u6ce2\u3001\u4e2d\u503c\u6ee4\u6ce2\u7b49\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
from scipy.signal import medfilt<\/p>\n
\u4e2d\u503c\u6ee4\u6ce2\u53bb\u566a<\/strong><\/h2>\n
def denoise(audio, kernel_size=3):<\/p>\n
    return medfilt(audio, kernel_size=kernel_size)<\/p>\n
audio_denoised = denoise(audio)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5f52\u4e00\u5316<\/h4>\n<\/p>\n
\u5f52\u4e00\u5316\u662f\u6307\u5c06\u97f3\u9891\u6570\u636e\u7684\u632f\u5e45\u8c03\u6574\u5230\u4e00\u5b9a\u8303\u56f4\u5185\uff0c\u901a\u5e38\u662f[-1, 1]\u3002\u8fd9\u6709\u52a9\u4e8e\u63d0\u9ad8\u540e\u7eed\u5904\u7406\u7684\u7a33\u5b9a\u6027\u3002<\/p>\n<\/p>\n
# \u5f52\u4e00\u5316<\/p>\n
audio_normalized = audio \/ np.max(np.abs(audio))<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5206\u5e27<\/h4>\n<\/p>\n
\u5206\u5e27\u662f\u6307\u5c06\u97f3\u9891\u4fe1\u53f7\u5212\u5206\u4e3a\u591a\u4e2a\u77ed\u65f6\u5e27\uff0c\u4ee5\u4fbf\u8fdb\u884c\u77ed\u65f6\u5085\u91cc\u53f6\u53d8\u6362\uff08STFT\uff09\u7b49\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
# \u5206\u5e27<\/p>\n
frame_length = 2048<\/p>\n
hop_length = 512<\/p>\n
frames = librosa.util.frame(audio_normalized, frame_length=frame_length, hop_length=hop_length).T<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u7279\u5f81\u63d0\u53d6<\/h3>\n<\/p>\n
\u7279\u5f81\u63d0\u53d6\u662f\u8bed\u97f3\u5904\u7406\u4e2d\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u901a\u8fc7\u63d0\u53d6\u97f3\u9891\u4fe1\u53f7\u7684\u7279\u5f81\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u97f3\u9891\u6570\u636e\u3002\u5e38\u7528\u7684\u7279\u5f81\u63d0\u53d6\u65b9\u6cd5\u5305\u62ecMFCC\u3001\u8272\u8c31\u56fe\u548c\u9891\u8c31\u56fe\u7b49\u3002<\/p>\n<\/p>\n
1\u3001\u6885\u5c14\u9891\u7387\u5012\u8c31\u7cfb\u6570\uff08MFCC\uff09<\/h4>\n<\/p>\n
MFCC\u662f\u6700\u5e38\u7528\u7684\u8bed\u97f3\u7279\u5f81\u4e4b\u4e00\uff0c\u5b83\u901a\u8fc7\u6a21\u62df\u4eba\u8033\u5bf9\u4e0d\u540c\u9891\u7387\u7684\u654f\u611f\u5ea6\uff0c\u5c06\u9891\u8c31\u8f6c\u6362\u4e3a\u5012\u8c31\u7cfb\u6570\u3002<\/p>\n<\/p>\n
# \u63d0\u53d6MFCC\u7279\u5f81<\/p>\n
mfccs = librosa.feature.mfcc(y=audio_normalized, sr=sample_rate, n_mfcc=13)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8272\u8c31\u56fe\uff08Chroma\uff09<\/h4>\n<\/p>\n
\u8272\u8c31\u56fe\u662f\u8868\u793a\u97f3\u9891\u4fe1\u53f7\u4e2d\u4e0d\u540c\u97f3\u9ad8\u7684\u80fd\u91cf\u5206\u5e03\u7684\u7279\u5f81\uff0c\u5e38\u7528\u4e8e\u97f3\u4e50\u5206\u6790\u548c\u97f3\u9ad8\u68c0\u6d4b\u3002<\/p>\n<\/p>\n
# \u63d0\u53d6\u8272\u8c31\u56fe\u7279\u5f81<\/p>\n
chromagram = librosa.feature.chroma_stft(y=audio_normalized, sr=sample_rate, hop_length=hop_length)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u9891\u8c31\u56fe\uff08Spectrogram\uff09<\/h4>\n<\/p>\n
\u9891\u8c31\u56fe\u662f\u8868\u793a\u97f3\u9891\u4fe1\u53f7\u5728\u4e0d\u540c\u9891\u7387\u4e0a\u7684\u80fd\u91cf\u5206\u5e03\u7684\u7279\u5f81\uff0c\u5e38\u7528\u4e8e\u8bed\u97f3\u8bc6\u522b\u548c\u97f3\u9891\u5206\u7c7b\u3002<\/p>\n<\/p>\n
# \u63d0\u53d6\u9891\u8c31\u56fe\u7279\u5f81<\/p>\n
spectrogram = librosa.stft(y=audio_normalized, n_fft=2048, hop_length=hop_length)<\/p>\n
spectrogram_db = librosa.amplitude_to_db(np.abs(spectrogram))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u8bad\u7ec3\u548c\u8bc4\u4f30\u6a21\u578b<\/h3>\n<\/p>\n
\u7279\u5f81\u63d0\u53d6\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e9b\u7279\u5f81\u6765\u8bad\u7ec3\u548c\u8bc4\u4f30\u673a\u5668\u5b66\u4e60\u6a21\u578b\u3002\u5e38\u7528\u7684\u6a21\u578b\u5305\u62ec\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u3001\u968f\u673a\u68ee\u6797\u3001\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\uff08DNN\uff09\u7b49\u3002\u4e0b\u9762\u4ee5\u652f\u6301\u5411\u91cf\u673a\u4e3a\u4f8b\uff0c\u4ecb\u7ecd\u5982\u4f55\u8bad\u7ec3\u548c\u8bc4\u4f30\u6a21\u578b\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
from sklearn.svm import SVC<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
\u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
X = mfccs.T  # \u7279\u5f81<\/p>\n
y = labels  # \u6807\u7b7e<\/p>\n
\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model = SVC(kernel='linear')<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f'Accuracy: {accuracy:.2f}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u751f\u6210\u97f3\u9891<\/h3>\n<\/p>\n
\u9664\u4e86\u5904\u7406\u548c\u5206\u6790\u97f3\u9891\u6570\u636e\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528Python\u751f\u6210\u97f3\u9891\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528librosa<\/code>\u5e93\u5c06\u5904\u7406\u540e\u7684\u97f3\u9891\u4fe1\u53f7\u4fdd\u5b58\u4e3a\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import soundfile as sf<\/p>\n
\u4fdd\u5b58\u5904\u7406\u540e\u7684\u97f3\u9891<\/strong><\/h2>\n
sf.write('processed_audio.wav', audio_normalized, sample_rate)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u8bfb\u53d6\u3001\u9884\u5904\u7406\u3001\u63d0\u53d6\u7279\u5f81\u3001\u8bad\u7ec3\u548c\u8bc4\u4f30\u6a21\u578b\u4ee5\u53ca\u751f\u6210\u97f3\u9891\u3002\u7279\u5f81\u63d0\u53d6\u662f\u8bed\u97f3\u5904\u7406\u4e2d\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u901a\u8fc7\u63d0\u53d6MFCC\u3001\u8272\u8c31\u56fe\u548c\u9891\u8c31\u56fe\u7b49\u7279\u5f81\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u97f3\u9891\u4fe1\u53f7\uff0c\u5e76\u4e3a\u540e\u7eed\u7684\u6a21\u578b\u8bad\u7ec3\u63d0\u4f9b\u6709\u6548\u7684\u6570\u636e\u8868\u793a\u3002\u5e0c\u671b\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u60a8\u80fd\u591f\u5bf9Python\u5904\u7406\u8bed\u97f3\u6570\u636e\u6709\u4e00\u4e2a\u5168\u9762\u7684\u4e86\u89e3\uff0c\u5e76\u80fd\u591f\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5bfc\u5165\u548c\u4f7f\u7528\u8bed\u97f3\u5904\u7406\u5e93\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u7528\u4e8e\u5904\u7406\u8bed\u97f3\u6570\u636e\uff0c\u4f8b\u5982SpeechRecognition<\/code>\u3001pydub<\/code>\u548clibrosa<\/code>\u3002\u8981\u5f00\u59cb\u4f7f\u7528\u8fd9\u4e9b\u5e93\uff0c\u60a8\u9700\u8981\u5148\u901a\u8fc7pip\u5b89\u88c5\u5b83\u4eec\uff0c\u4f8b\u5982\u4f7f\u7528\u547d\u4ee4pip install SpeechRecognition pydub librosa<\/code>\u3002\u5b89\u88c5\u540e\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u5bfc\u5165\u76f8\u5e94\u7684\u6a21\u5757\u6765\u5f00\u59cb\u5904\u7406\u8bed\u97f3\u6570\u636e\uff0c\u5e76\u4f7f\u7528\u5b83\u4eec\u63d0\u4f9b\u7684\u51fd\u6570\u8fdb\u884c\u97f3\u9891\u5f55\u5236\u3001\u8f6c\u5f55\u548c\u5206\u6790\u3002<\/p>\n
\u5982\u4f55\u5c06\u97f3\u9891\u6587\u4ef6\u8f6c\u6362\u4e3a\u6587\u672c\uff1f<\/strong>
\u8981\u5c06\u97f3\u9891\u6587\u4ef6\u8f6c\u6362\u4e3a\u6587\u672c\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528SpeechRecognition<\/code>\u5e93\u3002\u9996\u5148\uff0c\u5bfc\u5165\u5e93\u5e76\u52a0\u8f7d\u97f3\u9891\u6587\u4ef6\u3002\u7136\u540e\u4f7f\u7528recognize_google()<\/code>\u7b49\u65b9\u6cd5\u5c06\u97f3\u9891\u6587\u4ef6\u8f6c\u6362\u4e3a\u6587\u672c\u3002\u8bf7\u786e\u4fdd\u60a8\u7684\u97f3\u9891\u8d28\u91cf\u826f\u597d\uff0c\u8fd9\u6837\u8f6c\u5f55\u7684\u51c6\u786e\u6027\u4f1a\u66f4\u9ad8\u3002\u6b64\u5916\uff0c\u60a8\u4e5f\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u8bc6\u522b\u5f15\u64ce\uff0c\u6bd4\u5982Google API\u6216IBM Watson\u7b49\uff0c\u4ee5\u63d0\u9ad8\u8bc6\u522b\u6548\u679c\u3002<\/p>\n
\u5982\u4f55\u5904\u7406\u548c\u5206\u6790\u97f3\u9891\u4fe1\u53f7\u7684\u7279\u5f81\uff1f<\/strong>
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