| { |
| "paper_id": "2021", |
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| "date_generated": "2023-01-19T07:27:14.140520Z" |
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| "title": "Employing Low-Pass Filtered Temporal Speech Features for the Training of Ideal Ratio Mask in Speech Enhancement \u9673\u5f65\u540c \uf02a \u3001\u6d2a\u5fd7\u5049 \uf02a", |
| "authors": [ |
| { |
| "first": "Yan-Tong", |
| "middle": [], |
| "last": "Chen", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "National Chi Nan University", |
| "location": {} |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Jeih-Weih", |
| "middle": [], |
| "last": "Hung", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "National Chi Nan University", |
| "location": {} |
| }, |
| "email": "jwhung@ncnu.edu.tw" |
| } |
| ], |
| "year": "", |
| "venue": null, |
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| "abstract": "The masking-based speech enhancement method pursues a multiplicative mask that applies to the spectrogram of input noise-corrupted utterance, and a deep neural network (DNN) is often used to learn the mask. In particular, the features commonly used for automatic speech recognition can serve as the input of the DNN to learn the well-behaved mask that significantly reduce the noise distortion of processed utterances. This study proposes to preprocess the input speech features for the ideal ratio mask (IRM)-based DNN by lowpass filtering in order to alleviate the noise components. In particular, we employ the discrete wavelet transform (DWT) to decompose the temporal speech feature sequence and scale down the detail coefficients, which correspond to the high-pass portion of the sequence. Preliminary experiments conducted on a subset of TIMIT corpus reveal that the proposed method can make the resulting IRM achieve higher speech quality and intelligibility for the babble noise-corrupted signals compared with the original IRM, indicating that the lowpass filtered temporal feature sequence can learn a superior IRM network for speech enhancement.", |
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| "paper_id": "2021", |
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| "abstract": [ |
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| "text": "The masking-based speech enhancement method pursues a multiplicative mask that applies to the spectrogram of input noise-corrupted utterance, and a deep neural network (DNN) is often used to learn the mask. In particular, the features commonly used for automatic speech recognition can serve as the input of the DNN to learn the well-behaved mask that significantly reduce the noise distortion of processed utterances. This study proposes to preprocess the input speech features for the ideal ratio mask (IRM)-based DNN by lowpass filtering in order to alleviate the noise components. In particular, we employ the discrete wavelet transform (DWT) to decompose the temporal speech feature sequence and scale down the detail coefficients, which correspond to the high-pass portion of the sequence. Preliminary experiments conducted on a subset of TIMIT corpus reveal that the proposed method can make the resulting IRM achieve higher speech quality and intelligibility for the babble noise-corrupted signals compared with the original IRM, indicating that the lowpass filtered temporal feature sequence can learn a superior IRM network for speech enhancement.", |
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| "section": "Abstract", |
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| "section": "\u7dd2\u8ad6 (Introduction)", |
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| "text": "\uff0c\u8a31\u591a\u57fa\u65bc\u6df1\u5ea6\u5b78\u7fd2\u4e4b\u8a9e\u97f3\u5f37\u5316\u6cd5\u6839\u64da\u5176\u8a13\u7df4\u76ee\u6a19\u5927\u81f4 \u53ef\u4ee5\u5206\u70ba\u5169\u5927\u7bc4\u7587\uff1a\u5c0d\u6620\u5f0f (mapping) \u8207\u906e\u7f69\u5f0f (masking)\uff0c\u524d\u8005\u76f4\u63a5\u6c42\u53d6\u4e00\u500b\u5c0d\u6620\u51fd \u6578\uff0c\u4f7f\u6b64\u5c0d\u6620\u51fd\u6578\u4e4b\u7406\u60f3\u8f38\u51fa\u70ba\u4e7e\u6de8\u8a9e\u97f3\u7684\u5448\u73fe\u5f0f(\u7279\u5fb5)\uff0c\u5982\u6642\u57df\u8a0a\u865f\u6ce2\u5f62\u3001\u6642\u983b\u5716 (spectrogram) \u6216\u8033\u8778\u6642\u983b\u8b5c\u5716 (cochleagram)\uff0c\u5f8c\u8005\u662f\u6c42\u53d6\u4e00\u500b\u906e\u7f69 (mask)\uff0c\u7528\u4ee5\u8207\u539f\u59cb \u8f38\u5165\u8a0a\u865f\u6216\u7279\u5fb5\u5448\u73fe\u4f5c\u9ede\u5c0d\u9ede\u7684\u76f8\u4e58\uff0c\u4f7f\u76f8\u4e58\u5f8c\u7684\u8a0a\u865f\u5448\u73fe\u5f0f\u80fd\u8da8\u8fd1\u4e7e\u6de8\u6642\u7684\u72c0\u614b\u3002\u7c21 \u55ae\u4f86\u8aaa\u5c0d\u6620\u5f0f\u6240\u6c42\u53d6\u7684\u51fd\u6578\uff0c\u5c0d\u65bc\u8f38\u5165\u8a0a\u865f\u7279\u5fb5\u7684\u904b\u7b97\u53ef\u4ee5\u662f\u4efb\u610f\u7531\u6240\u4f7f\u7528\u4e4b\u6df1\u5ea6\u5b78\u7fd2 \u6a21\u578b\u5b9a\u7fa9\u7684\u975e\u7dda\u6027\u904b\u7b97\uff0c\u800c\u906e\u7f69\u5f0f\u6240\u6c42\u53d6\u7684\u51fd\u6578\u904b\u7b97\uff0c\u5247\u7c21\u5316\u6216\u9650\u5236\u70ba\u5c0d\u8f38\u5165\u8a0a\u865f\u7279\u5fb5 \u4f5c\u4e58\u6cd5 (\u5373\u52a0\u6b0a\u904b\u7b97)\u3002\u4e8c\u8005\u5404\u64c5\u52dd\u5834\uff0c\u4f46\u8fd1\u5e74\u4f86\u4f3c\u4e4e\u662f\u4ee5\u906e\u7f69\u5f0f\u7684\u8a9e\u97f3\u5f37\u5316\u66f4\u53d7\u91cd\u8996\u8207 \u767c \u5c55 \uff0c \u76f8 \u95dc \u7684 \u6f14 \u7b97 \u6cd5 \u5305 \u62ec \u4e86 \u7406 \u60f3 \u4e8c \u5143 \u906e \u7f69 (ideal binary mask, IBM) (Wang, 2005; Srinivasan et al., 2006) \u3001\u7406\u60f3\u6bd4\u4f8b\u906e\u7f69 (ideal ratio mask, IRM) (Srinivasan et al., 2006) \u3001\u983b \u8b5c\u5f37\u5ea6\u906e\u7f69 (spectral magnitude mask, SMM) (Wang et al., 2014) \u3001\u8907\u6578\u7406\u60f3\u6bd4\u4f8b\u906e\u7f69 (complex ideal ratio mask, cIRM) (Williamson et al., 2016) ", |
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| "text": "(Wang, 2005;", |
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| "text": "Srinivasan et al., 2006)", |
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| "text": "(Srinivasan et al., 2006)", |
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| "text": "(Wang et al., 2014)", |
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| "text": "(Williamson et al., 2016)", |
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| "section": "\u7dd2\u8ad6 (Introduction)", |
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| { |
| "text": "EQUATION", |
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| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
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| "raw_str": "\u5728\u672c\u7814\u7a76\u4e2d\uff0c\u6211\u5011\u9078\u64c7\u52a0\u4ee5\u7814\u7a76\u6539\u9032\u7684\u662f\u7406\u60f3\u6bd4\u4f8b\u906e\u7f69(ideal ratio mask, IRM)\u6cd5\uff0c\u6b64\u6cd5\u901a \u5e38\u662f\u6c42\u53d6\u8a9e\u97f3\u4e4b\u4e00\u822c\u6642\u983b\u5716 (spectrogram) \u6216\u8033\u8778\u6642\u983b\u5716 (cochleagram) \u5c0d\u61c9\u7684\u7406\u60f3\u906e \u7f69\u503c\uff1a , | , | | , | | , | ,", |
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| "section": "\u7dd2\u8ad6 (Introduction)", |
| "sec_num": "1." |
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| "text": "EQUATION", |
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| "end": 8, |
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| "raw_str": "X \u22ef (2) \u5176\u5c3a\u5bf8\u70ba \u3002 \u6b65\u9a5f\u4e8c\uff1a\u4e0a\u8ff0\u4e4b\u7279\u5fb5\u77e9\u9663X\u7684\u4efb\u4e00\u7b2c \u500b\u6a6b\u5217\u5411\u91cf X , X , . . . X ,", |
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| ], |
| "section": "\u7dd2\u8ad6 (Introduction)", |
| "sec_num": "1." |
| }, |
| { |
| "text": "Matlab toolbox for DNN based speech separation .Retrieved from http://web.cse.ohio-state.edu/pnl/DNN_toolbox/", |
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| "content": "<table><tr><td>\u5728\u672c\u7814\u7a76\u4e2d\uff0c\u4e3b\u8981\u662f\u91dd\u5c0d\u4e0a\u8ff0\u4e4b\u906e\u7f69\u5f0f\u8a9e\u97f3\u5f37\u5316\u6cd5\u52a0\u4ee5\u6539\u9032\uff0c\u6211\u5011\u63d0\u51fa\u5c0d\u65bc\u8a13\u7df4\u906e</td></tr><tr><td>\u7f69\u6a21\u578b\u7684\u8f38\u5165\u96dc\u8a0a\u8a9e\u97f3\u7684\u7279\u5fb5\u6642\u5e8f\u5217\u4f5c\u7c21\u55ae\u7684\u9810\u8655\u7406 (pre-processing)\uff0c\u4f7f\u5176\u5305\u542b\u7684\u96dc\u8a0a</td></tr><tr><td>\u5931\u771f\u8f03\u4f4e\uff0c\u4ee5\u671f\u5728\u4e4b\u5f8c\u7684\u8a13\u7df4\u906e\u7f69\u6b65\u9a5f\u80fd\u66f4\u52a0\u7cbe\u78ba\u3002\u800c\u4f7f\u7528\u7684\u9810\u8655\u7406\u65b9\u6cd5\uff0c\u662f\u900f\u904e\u7c21\u6613</td></tr><tr><td>\u7684\u4e00\u968e\u96e2\u6563\u5c0f\u6ce2\u8f49\u63db (discrete wavelet transform, DWT) (Mallat, 1999)]\uff0c\u5c07\u7279\u5fb5\u6642\u5e8f\u5217\u5206</td></tr><tr><td>\u70ba\u9ad8\u4f4e\u5169\u8abf\u8b8a\u983b\u5e36 (modulation frequency bands)\uff0c\u7136\u5f8c\u85c9\u7531\u4e00\u6b0a\u91cd\u7684\u76f8\u4e58\u4f86\u964d\u4f4e\u9ad8\u8abf\u8b8a</td></tr><tr><td>\u983b\u5e36\u4e4b\u5e8f\u5217\u7684\u632f\u5e45\uff0c\u518d\u5c07\u5176\u8207\u539f\u59cb\u4f4e\u8abf\u8b8a\u983b\u5e36\u5e8f\u5217\u642d\u914d\u3001\u900f\u904e\u4e00\u968e\u53cd\u96e2\u6563\u5c0f\u6ce2\u8f49\u63db</td></tr><tr><td>(inverse discrete wavelet transform, IDWT) \u91cd\u5efa\u7279\u5fb5\u5e8f\u5217\uff0c\u518d\u4f7f\u7528\u6b64\u76f8\u7576\u65bc\u900f\u904e\u4f4e\u901a\u6ffe\u6ce2</td></tr><tr><td>\u8655\u7406\u5f8c\u7684\u7279\u5fb5\u5e8f\u5217\u4f86\u8a13\u7df4\u906e\u7f69\u6a21\u578b\u3002</td></tr><tr><td>\u4e0a\u8ff0\u4f4e\u901a\u6ffe\u6ce2\u4e4b\u8655\u7406\uff0c\u4e3b\u8981\u662f\u57fa\u65bc\u5148\u524d\u8af8\u591a\u5b78\u8005\u6240\u63d0\u51fa\u7684\u89c0\u5bdf(Kanedera et al., 1997;</td></tr><tr><td>Chen & Bilmes, 2007)\uff1a\u4e7e\u6de8\u8a9e\u97f3\u7279\u5fb5\u6642\u5e8f\u5217\u4e3b\u8981\u5206\u5e03\u983b\u7387\u5728 1 Hz \u81f3 16 Hz \u4e4b\u9593\uff0c\u4ee5\u4e00\u822c</td></tr><tr><td>\u7684\u97f3\u6846\u53d6\u6a23\u7387 100 Hz \u800c\u8a00\uff0c\u7279\u5fb5\u5e8f\u5217\u53ef\u5305\u542b\u7684(\u8abf\u8b8a)\u983b\u5e36\u70ba[0,50 Hz]\uff0c\u56e0\u6b64\u5f8c\u534a\u983b\u5e36</td></tr><tr><td>\u9bae\u5c11\u5305\u542b\u8a9e\u97f3\u6210\u5206\uff0c\u6291\u5236\u6b64\u983b\u5e36\u4e0d\u6703\u5c0d\u8a9e\u97f3\u9020\u6210\u660e\u986f\u5931\u771f\uff0c\u4f46\u53ef\u6709\u6548\u6291\u5236\u96dc\u8a0a\u7684\u5e72\u64fe\u3002</td></tr><tr><td>\u53e6\u5916\uff0c\u57fa\u65bc\u6587\u737b(Wang et al., 2018)\u6240\u8ff0\uff0c\u4f7f\u7528\u5c0f\u6ce2\u8f49\u63db\u5206\u89e3\u8a9e\u97f3\u7279\u5fb5\u6642\u5e8f\u5217\u3001\u6d88\u9664</td></tr><tr><td>\u5176\u7d30\u7bc0\u4fc2\u6578 (detail coefficients\uff0c\u76f8\u7576\u65bc\u8abf\u8b8a\u9ad8\u983b\u6210\u5206) \u5f8c\u91cd\u5efa\u4e4b\u8a9e\u97f3\u7279\u5fb5\uff0c\u5728\u96dc\u8a0a\u74b0\u5883</td></tr><tr><td>\u4e0b\u6709\u660e\u986f\u9032\u6b65\u7684\u8a9e\u97f3\u8fa8\u8b58\u7387\uff0c\u6211\u5011\u53c3\u7167\u9019\u6a23\u7684\u505a\u6cd5\u4f86\u5be6\u73fe\u524d\u8ff0\u4e4b\u8a9e\u97f3\u7279\u5fb5\u5e8f\u5217\u7684\u4f4e\u901a\u6ffe</td></tr><tr><td>\u6ce2\u8655\u7406\uff0c\u671f\u8a31\u5b83\u5c0d\u61c9\u7684\u906e\u7f69\u6df1\u5ea6\u6a21\u578b\u80fd\u5f97\u5230\u66f4\u4f73\u7684\u8a9e\u97f3\u5f37\u5316\u6548\u679c\u3002</td></tr></table>", |
| "text": "\u3001\u76f8\u4f4d\u654f\u611f\u578b\u906e\u7f69 (phase-sensitive mask, PSM) (Erdogan et al., 2015) \u7b49\u3002", |
| "type_str": "table", |
| "num": null |
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| "content": "<table><tr><td>\u9673\u5f65\u540c\u8207\u6d2a\u5fd7\u5049 \u9673\u5f65\u540c\u8207\u6d2a\u5fd7\u5049</td></tr><tr><td>\u4ee5X \u6211\u5011\u5c07\u4efb\u4e00\u7dad\u7279\u5fb5\u6642\u5e8f\u5217X \u4ee3\u8868\u4e4b\uff0c\u5176\u7a31\u4f5cX\u7684\u7b2c \u7dad\u7279\u5fb5\u6642\u5e8f\u5217\uff0c\u5c3a\u5bf8\u70ba1 \u4ee5\u4e00\u968e\u96e2\u6563\u5c0f\u6ce2\u8f49\u63db\u52a0\u4ee5\u5206\u89e3\u5982\u4e0b\uff1a \uff0c\u5176\u4e2d1 cA , cD X \u5176\u4e2d (.) \u4ee3\u8868\u96e2\u6563\u5c0f\u6ce2\u8f49\u63db (discrete wavelet transform)\u3001 cA \u8f49\u63db\u5206\u89e3\u800c\u5f97\u7684\u8fd1\u4f3c\u4fc2\u6578(approximation coefficients)\u8207\u7d30\u7bc0\u4fc2\u6578(detail coefficients)\uff0c\u5176\u53ef \u3002 (4) \u8207 c \u5206\u5225\u70ba \u8996\u70ba\u539f\u59cb\u5e8f\u5217X \u4e4b\u4f4e\u901a\u6210\u5206\u8207\u9ad8\u901a\u6210\u5206\uff0c\u4e8c\u8005\u983b\u5bec\u5747\u7d04\u7b49\u65bc\u539f\u59cb\u5e8f\u5217\u983b\u5bec\u7684\u4e00\u534a\uff0c\u4e14 \u9ede\u6578\u6e1b\u534a\u3002 \u6b65\u9a5f\u4e09\uff1a\u6211\u5011\u5c07\u4e0a\u4e00\u6b65\u9a5f\u6240\u5f97\u7684\u7d30\u7bc0\u4fc2\u6578c ]\u4e58\u4e0a\u4e00\u500b\u5c0f\u65bc 1 \u7684\u6b0a\u91cd \uff0c\u518d\u8207\u539f\u8fd1\u4f3c \u4fc2\u6578\u76f8\u7d44\u5408\u3001\u7d93\u904e\u53cd\u96e2\u6563\u5c0f\u6ce2\u8f49\u63db\u91cd\u5efa\u7b2c \u7dad\u7279\u5fb5\u6642\u5e8f\u5217\uff0c\u8868\u793a\u5982\u4e0b\uff1a X cA , cD (5) \u5176\u4e2dX \u70ba\u66f4\u65b0\u7684\u7279\u5fb5\u6642\u5e8f\u5217\uff0c\u76f8\u8f03\u65bc\u539f\u59cb\u7279\u5fb5\u6642\u5e8f\u5217X \uff0cX \u5305\u542b\u8f03\u4f4e\u7684\u9ad8\u901a \u6210\u5206\uff0c\u56e0\u6b64\u61c9\u7576\u5305\u542b\u8f03\u5c11\u96dc\u8a0a\u9020\u6210\u7684\u5931\u771f\u3002 \u6b65\u9a5f\u56db\uff1a\u53c3\u7167\u4e00\u822c IRM \u6df1\u5ea6\u6a21\u578b\u7684\u8a13\u7df4\u6cd5\uff0c\u6211\u5011\u6539\u4ee5\u65b0\u7684\u7279\u5fb5\u5e8f\u5217 X , 1 \u4f5c \u70ba\u8f38\u5165\uff0c\u4ee5\u7406\u60f3 IRM \u906e\u7f69\u503c\u70ba\u76ee\u6a19\u8f38\u51fa\uff0c\u8a13\u7df4 IRM \u6df1\u5ea6\u6a21\u578b\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u82e5\u5f0f(4) \u4e2d\u7684\u6b0a\u91cd 1\uff0c\u5247\u6240\u8a13\u7df4\u7684 IRM \u6a21\u578b\u8207\u539f\u59cb(\u5373\u4f7f\u7528\u539f\u59cb\u7279\u5fb5\u8a13\u7df4)IRM \u6a21\u578b\u5b8c\u5168\u4e00 \u81f4\u3002 \u6e2c\u8a66\u968e\u6bb5\uff1a \u5c07\u6e2c\u8a66\u4e4b\u8a9e\u53e5\u5982\u540c\u8a13\u7df4\u8a9e\u53e5\u4e4b\u8655\u7406\u7684\u524d\u4e09\u500b\u6b65\u9a5f\u3001\u6c42\u53d6\u4f4e\u901a\u6ffe\u6ce2\u4e4b\u7279\u5fb5\u6642\u5e8f\u5217\uff0c\u5c07\u5176\u901a \u904e\u8a13\u7df4\u5b8c\u6210\u7684 IRM \u6a21\u578b\u6c42\u53d6\u906e\u7f69\u503c\uff0c\u5c07\u906e\u7f69\u503c\u8207\u539f\u8a2d\u5b9a\u4e4b\u5c0d\u61c9\u7684\u6642\u983b\u5716\u4f5c\u9ede\u4e58\u7a4d (dot product)\uff0c\u5373\u53ef\u5f97\u5f37\u5316\u5f8c\u7684\u6642\u983b\u5716\uff0c\u7d93\u7531\u9069\u7576\u7684\u53cd\u8f49\u63db\u91cd\u5efa\u6210\u5f37\u5316\u7248\u7684\u6642\u57df\u8a0a\u865f\u3002 3. \u5be6\u9a57\u8a2d\u7f6e (Experimental Setup) )\u6240 \u7d66\u4e88\u7684\u6b0a\u91cd \uff0c\u5206\u5225\u8a2d\u5b9a\u70ba 0, 0.25, 0.50, 0.75\uff0c\u85c9\u6b64\u89c0\u5bdf\u7d30\u7bc0\u4fc2\u6578\u4e4b\u58d3\u6291\u7a0b\u5ea6\u5c0d\u65bc IRM \u6548 \u679c\u4e4b\u5f71\u97ff(\u539f\u59cb IRM \u6240\u5c0d\u61c9\u4e4b\u6b0a\u91cd 1)\u3002 \u5728\u4f7f\u7528\u7684\u96e2\u6563\u5c0f\u6ce2\u8f49\u63db\u8207\u53cd\u8f49\u63db\u4e2d\uff0c\u6211\u5011\u4f7f\u7528 db2 \u5c0f\u6ce2\u51fd\u6578\u3002 \u5728\u6211\u5011\u7684\u8a55\u4f30\u5be6\u9a57\u4e0a\uff0c\u6211\u5011\u5c07\u5206\u70ba\u4e09\u90e8\u5206\u4f86\u5448\u73fe\u4e26\u8a0e\u8ad6\uff0c\u7b2c\u4e00\u90e8\u5206\u662f\u5c0d\u61c9\u65bc\u4f7f\u7528\u6240\u6709\u7a2e \u985e\u4e4b\u8f38\u5165\u7279\u5fb5\u7d44\u5408\u6240\u8a13\u7df4\u53ca\u6e2c\u8a66\u4e4b IRM \u6a21\u578b\uff0c\u7b2c\u4e8c\u90e8\u5206\u662f\u5c0d\u61c9\u65bc\u4f7f\u7528\u55ae\u4e00\u7a2e\u985e\u4e4b\u8f38\u5165\u7279 \u5fb5\u6240\u8a13\u7df4\u53ca\u6e2c\u8a66\u4e4b IRM \u6a21\u578b\uff0c\u6211\u5011\u5c07\u5728\u9019\u5169\u90e8\u5206\u4e2d\uff0c\u63a2\u7a76\u6240\u63d0\u65b0\u65b9\u6cd5\u4e4b\u4f4e\u901a\u6ffe\u6ce2\u7279\u5fb5\u6642 \u5e8f\u5217\u5c0d\u65bc IRM \u6548\u80fd\u7684\u6539\u8b8a\uff0c\u7b2c\u4e09\u90e8\u5206\u5247\u662f\u85c9\u7531\u6642\u983b\u5716\u7684\u5c55\u793a\uff0c\u89c0\u5bdf\u539f\u59cb\u8207\u66f4\u65b0\u4e4b IRM \u6240 \u5f37\u5316\u7684\u8a9e\u97f3\u8a0a\u865f\u7684\u5dee\u7570\u3002 4.1 \u672a\u8655\u7406\u8a9e\u97f3 \u7406\u60f3 IRM \u539f\u59cb IRM 1 \u539f\u59cb IRM 2 STOI 0.6130 0.9004 0.6763 0.6658 PESQ 1.6081 2.6408 1.7755 1.7748 \u8207 PESQ \u503c( 0.25\u5728 STOI \u5206\u6578\u9664\u5916\uff0c 0.25, 0.50 \u5728 PESQ \u5206\u6578\u9664\u5916) \uff0c\u6b64\u521d\u6b65 \u9a57\u8b49\u4e86\u6b64\u65b9\u6cd5\u5c0d\u65bc\u8a13\u7df4\u66f4\u4f73 IRM \u6a21\u578b\u3001\u4ee5\u6291\u5236\u96dc\u8a0a\u5e72\u64fe\u6709\u66f4\u597d\u7684\u6548\u679c\u3002 2. \u5168\u7136\u79fb\u9664 (\u8a2d\u5b9a 0) \u6216\u5c11\u91cf\u79fb\u9664 (\u8a2d\u5b9a 0.75) \u8abf\u8b8a\u9ad8\u983b\u6210\u5206\u4f3c\u4e4e\u662f\u8f03\u4f73\u9078\u9805\uff0c \u4e8c\u8005\u81f3\u5c11\u7686\u53ef\u4f7f PESQ \u8207 STOI \u503c\u63d0\u5347\uff0c 0\u5f97\u5230\u6700\u4f73\u7684 PESQ \u503c\uff0c\u800c 0.75\u5247\u4f7f STOI \u9032\u6b65\u6700\u5927\u3002 \u539f\u59cb IRM 1 \u4e0d\u540c\u6b0a\u91cd \u6291\u5236\u8abf\u8b8a\u9ad8\u983b\u4e4b IRM 1 0 0.25 0.50 0.75 STOI 0.6763 0.6767 0.6728 0.6799 0.6789 PESQ 1.7755 1.7844 1.7612 1.7717 1.7760 \u5176\u6b21\uff0c\u8868 3 \u9032\uff0c\u5176\u4ed6\u8f03\u5c0f\u503c\u7684 \u8a2d\u5b9a\u503c\u5247\u4e26\u672a\u4e00\u5236\u6027\u5730\u5f97\u5230\u660e\u986f\u9032\u6b65\u7684\u6548\u679c\uff0c\u9019\u53ef\u80fd\u539f\u56e0\u662f\uff0c\u7576 \u4f7f\u7528\u5dee\u91cf\u7279\u5fb5\u6642\uff0c\u5dee\u91cf\u7279\u5fb5\u672c\u8eab\u5c31\u5df2\u7d93\u6291\u5236\u539f\u59cb\u7279\u5fb5\u7684\u8abf\u8b8a\u9ad8\u983b\u6210\u5206\uff0c\u56e0\u6b64\u6b64\u6642\u7528\u8f03 \u5927\u7684 \u503c\u518d\u5c0d\u539f\u59cb\u7279\u5fb5\u7684\u8abf\u8b8a\u9ad8\u983b\u6210\u5206\u5c0f\u5e45\u6291\u5236\uff0c\u5373\u53ef\u9054\u5230\u9810\u671f\u4e4b\u9032\u6b65\u6548\u679c\u3002 \u539f\u59cb IRM 2 \u4e0d\u540c\u6b0a\u91cd \u6291\u5236\u8abf\u8b8a\u9ad8\u983b\u4e4b IRM 2 0 0.25 0.50 0.75 STOI 0.6658 0.6639 0.6671 0.6615 0.6682 PESQ 1.7748 1.7819 1.7916 1.7589 1.7996 \u8d77\u898b\uff0c\u9019\u88e1\u6211\u5011\u628a\u5dee\u91cf\u7279\u5fb5\u4e00\u4f75\u52a0\u5165\uff0c\u540c\u6642\uff0c\u6211\u5011\u5c07\u524d\u4e00\u7bc0\u56db\u985e\u7279\u5fb5\u7684\u7d44\u5408(\u4ee5\"combo\" \u8868\u793a)\u4e4b\u7d50\u679c\u5217\u5728\u8868\u7684\u6700\u4e0b\u4e00\u5217\uff0c\u4ee5\u4f9b\u6bd4\u8f03\u3002\u5f9e\u9019\u5169\u500b\u8868\u4e4b\u6578\u64da\uff0c\u6211\u5011\u6709\u4ee5\u4e0b\u5e7e\u9ede\u7684\u89c0 \u5bdf\u8207\u8a0e\u8ad6\uff1a 1. \u5c0d\u65bc\u8a9e\u97f3\u53ef\u8b80\u5ea6\u6307\u6a19 STOI \u800c\u8a00\uff0c\u4e0d\u4f7f\u7528\u4f4e\u901a\u6ffe\u6ce2\u4e4b\u56db\u985e\u7279\u5fb5\u4e2d\uff0c\u4ee5 MFCC \u8868\u73fe\u6700\u4f73 (0.6740) \uff0c\u751a\u81f3\u8d85\u8d8a\u4e86\u7d44\u5408\u7279\u5fb5\u7684\u7d50\u679c(0.6658) \uff0c\u7136\u800c\uff0c\u7576\u914d\u5408\u4f4e\u901a\u6ffe\u6ce2\u6642\uff0cMFCC \u53ef\u4ee5\u9054\u5230\u66f4\u4f73\u7684 STOI \u503c\uff0c\u4f8b\u5982\u7576\u4f7f\u7528 0.25\u7684\u6b0a\u91cd\u6642\uff0cMFCC \u5c0d\u61c9\u4e4b STOI \u503c\u53ef \u4ee5\u9032\u4e00\u6b65\u63d0\u5347\u81f3 0.6772\u3002\u6b64\u5916\uff0c\u4f4e\u901a\u6ffe\u6ce2\u8655\u7406\u4e26\u975e\u5c0d\u6bcf\u4e00\u7a2e\u7279\u5fb5\u90fd\u80fd\u5e36\u4f86\u6539\u9032\uff0c\u4f8b\u5982 \u5c0d\u65bc AMS \u7279\u5fb5\u800c\u8a00\uff0c\u4e0d\u4f7f\u7528\u4f4e\u901a\u6ffe\u6ce2\u6240\u5c0d\u61c9\u7684\u539f\u59cb IRM \u8868\u73fe\u6700\u597d\u3002 \u6839\u64da\u4ee5\u4e0a\u89c0\u5bdf\uff0c\u56db\u985e\u7279\u5fb5\u7684\u7d44\u5408\u672a\u5fc5\u5728 STOI \u8868\u73fe\u4e0a\u512a\u65bc\u55ae\u985e\u7279\u5fb5\uff0c\u800c\u5728 PESQ \u8868\u73fe\u4e0a \u53ea\u80fd\u4e9b\u8a31\u8d85\u8d8a\u500b\u5225\u55ae\u985e\u7279\u5fb5\uff0c\u9019\u53ef\u80fd\u539f\u56e0\u5728\u65bc\u67d0\u985e\u7279\u5fb5(\u5982 AMS)\u5728\u8868\u73fe\u4e0a\u8207\u5176\u4ed6\u7279\u5fb5 \u5dee\u7570\u8f03\u5927\uff0c\u5373\u4f7f\u5f8c\u7aef\u7684\u6df1\u5ea6\u6a21\u578b\u5728\u5b78\u7fd2\u4e2d\u7406\u61c9\u80fd\u6de1\u5316\u9019\u985e\u7279\u5fb5\u7684\u8ca0\u9762\u5f71\u97ff\uff0c\u4f46\u662f\u5f9e\u6e2c\u8a66 \u7d50\u679c\u4e0a\uff0c\u591a\u985e\u7279\u5fb5\u7684\u7d44\u5408\u4e26\u672a\u767c\u63ee\u986f\u8457\u7684\u52a0\u6210\u6027\u3002 \u8868 4. \u55ae\u4e00\u7a2e\u985e\u7279\u5fb5\u7684 STOI \u5206\u6578\u6bd4\u8f03\uff0c\u672a\u8655\u7406\u8a9e\u97f3\u8207\u7d93\u904e\u539f\u59cb IRM 2 (\u4f7f\u7528\u539f\u7279 \u5fb5\u8207\u5176\u5dee\u91cf\u7279\u5fb5\u6c42\u53d6) \u3001\u4e0d\u540c\u6b0a\u91cd\u03b1\u6291\u5236\u8abf\u8b8a\u9ad8\u983b\u4e4b IRM(\u6709\u642d\u914d\u5dee\u91cf\u7279\u5fb5)\u8655 \u7406\u5f8c\u5c0d\u61c9\u7684 STOI \u5e73\u5747\u5206\u6578\uff0c\u5176\u4e2d\"combo\"\u8868\u793a\u56db\u985e\u7279\u5fb5\u4e4b\u7d44\u5408 STOI \u5206\u6578 \u539f\u59cb IRM 2 \u4e0d\u540c\u6b0a\u91cd \u6291\u5236\u8abf\u8b8a\u9ad8\u983b\u4e4b IRM 2 0 0.25 0.50 0.75 AMS 0.6472 0.6430 0.6435 0.6458 0.6466 RASTAPLP 0.6559 0.6600 0.6607 0.6611 0.6556 MFCC 0.6740 0.6771 0.6772 0.6761 0.6770 GF 0.6695 0.6698 0.6667 0.6672 0.6692 combo 0.6658 0.6639 0.6671 0.6615 0.6682 \u8868 5. \u55ae\u4e00\u7a2e\u985e\u7279\u5fb5\u7684 PESQ \u5206\u6578\u6bd4\u8f03\uff0c\u672a\u8655\u7406\u8a9e\u97f3\u8207\u7d93\u904e\u539f\u59cb IRM 2 (\u4f7f\u7528\u539f\u7279 \u539f\u59cb\u7684 IRM \u6bd4\u4f7f\u7528\u4f4e\u901a\u6ffe\u6ce2\u6cd5\u5c0d\u61c9\u7684 IRM \u6548\u679c\u8f03\u4f73\uff0c\u800c\u5728 STOI \u5206\u6578\u4e0a\uff0c\u7576\u914d\u5408\u4f4e \u7522\u751f\u986f\u8457\u7684\u5931\u771f\uff0c\u63a5\u8457\uff0c\u6bd4\u8f03\u5716 1(b)\u8207\u5716 1(c)\u53ef\u770b\u51fa\uff0c\u7406\u60f3\u7684 IRM \u53ef\u5e36\u4f86\u986f\u8457\u7684\u8a9e\u97f3\u5f37 \u5fb5\u8207\u5176\u5dee\u91cf\u7279\u5fb5\u6c42\u53d6) \u3001\u4e0d\u540c\u6b0a\u91cd\u03b1\u6291\u5236\u8abf\u8b8a\u9ad8\u983b\u4e4b IRM(\u6709\u642d\u914d\u5dee\u91cf\u7279\u5fb5)\u8655 \u7406\u5f8c\u5c0d\u61c9\u7684 PESQ \u5e73\u5747\u5206\u6578\uff0c\u5176\u4e2d\"combo\"\u8868\u793a\u56db\u985e\u7279\u5fb5\u4e4b\u7d44\u5408 [Table 5. The averaged PESQ results for the original IRM 2 (using the original static and delta features of single type) and the lowpass filtered IRM 2 (using the lowpass filtered static and delta features of single type with different assignments of parameter \u03b1)] PESQ \u5206\u6578 \u539f\u59cb IRM 2 \u4e0d\u540c\u6b0a\u91cd \u6291\u5236\u8abf\u8b8a\u9ad8\u983b\u4e4b IRM 2 0 0.25 0.50 0.75 AMS 1.6721 1.6705 1.6712 1.6731 1.6758 RASTA-PLP 1.7463 1.7634 1.7634 1.7630 1.7426 4.3 \u589e\u52a0\u8a13\u7df4\u53ca\u6e2c\u8a66\u8cc7\u6599\u4e14\u4f7f\u7528\u55ae\u4e00\u7a2e\u985e\u4e4b\u8f38\u5165\u7279\u5fb5\u6240\u5f97\u7684IRM\u6548\u80fd\u5206 \u6790 (The \u5728\u524d\u4e00\u7bc0\u4e2d\uff0c\u6211\u5011\u53ef\u89c0\u5bdf\u51fa\u5728\u5404\u500b\u985e\u5225\u7684\u7279\u5fb5\u4e2d\uff0c\u55ae\u7368\u4f7f\u7528 MFCC \u7279\u5fb5\u7684 IRM \u6548\u80fd\u660e \u986f\u512a\u65bc\u5176\u4ed6\u7279\u5fb5\uff0c\u5176\u540c\u6642\u4f7f\u7528\u4f4e\u901a\u6ffe\u6ce2\u8207\u5dee\u91cf\u7279\u5fb5\u8655\u7406\u5176\u5e8f\u5217\u53ef\u5f97\u5230\u8f03\u4f73\u7684 STOI ( 0.25) \u8207 PESQ ( 0.75) \u5206\u6578\u3002\u5728\u672c\u7bc0\u88e1\uff0c\u6211\u5011\u60f3\u9032\u4e00\u6b65\u89c0\u5bdf\u6b64\u8868\u73fe\u826f\u597d\u7684\u7684 MFCC \u7279 \u5fb5\uff0c\u82e5\u518d\u589e\u52a0 1 \u500d\u7684\u8cc7\u6599\u6578\u91cf (\u5176\u4e2d\uff0c\u8a13\u7df4\u96c6\u5305\u542b\u4e86 10 \u4f4d\u8a9e\u8005\u3001\u6bcf\u4eba 10 \u53e5\u5171 100 \u500b\u8a9e \u53e5\uff0c\u800c\u6e2c\u8a66\u96c6\u5247\u5305\u542b\u4e86\u8207\u8a13\u7df4\u96c6\u4e0d\u540c\u7684 6 \u4f4d\u8a9e\u8005\u3001\u6bcf\u4eba 10 \u53e5\u5171 60 \u500b\u8a9e\u53e5) \u7684\u60c5\u6cc1\u4e0b\uff0c \u5176 IRM \u7684\u6548\u80fd\uff0c\u540c\u6642\u89c0\u5bdf\u5728\u4f7f\u7528\u6211\u5011\u6240\u63d0\u51fa\u7684\u4f4e\u901a\u6ffe\u6ce2\u6cd5\u5c0d\u65bc MFCC \u7279\u5fb5\u5728\u6b64\u72c0\u614b\u4e0b \u4e4b IRM \u6548\u80fd\u7684\u5f71\u97ff\uff0c\u9019\u4e00\u7cfb\u5217\u5be6\u9a57\u7d50\u679c\u5206\u5225\u5217\u5728\u8868 6(\u7121\u5dee\u91cf\u7279\u5fb5)\u8207\u8868 7(\u6709\u5dee\u91cf\u7279 \u5fb5)\u3002 \u5f9e\u8868 6 \u8207\u8868 7 \u6211\u5011\u53ef\u4ee5\u89c0\u5bdf\u51fa\u4ee5\u4e0b\u5e7e\u9ede\uff1a 0\u4e4b\u4f4e\u901a\u6ffe\u6ce2\u6cd5\uff0c\u53ef\u9054\u5230 1.8214\u3002 \u6700\u5f8c\u5728\u9019\u4e00\u5c0f\u7bc0\uff0c\u6211\u5011\u4f7f\u7528\u8a9e\u97f3\u8a0a\u865f\u7684\u5f37\u5ea6\u6642\u983b\u5716(magnitude spectrogram)\uff0c\u4f86\u6aa2\u8996\u539f\u59cb IRM \u8207\u6211\u5011\u63d0\u51fa\u4e4b\u4f4e\u901a\u6ffe\u6ce2\u7279\u5fb5\u4e4b IRM \u7684\u5f37\u5316\u6548\u80fd\uff0c\u5716 1(a)-(f) \u70ba\u4e00\u8a9e\u53e5\u5728\u5404\u7a2e\u72c0\u614b\u4e0b 1. \u5f97 PESQ \u6700\u4f73\u6b0a\u91cd\u662f \u901a\u6ffe\u6ce2\u6642\uff0c\u53ef\u4ee5\u6bd4\u539f\u59cb IRM \u9054\u5230\u66f4\u4f73\u7684\u7d50\u679c\uff0c\u4f8b\u5982\u7576\u4f7f\u7528 0.5\u7684\u6b0a\u91cd\u6642\uff0cMFCC \u5c0d\u61c9\u4e4b STOI \u503c\u53ef\u4ee5\u9032\u4e00\u6b65\u63d0\u5347\u81f3 0.6880\u3002\u7136\u800c\uff0c\u7372\u5f97 PESQ \u6700\u4f73\u6b0a\u91cd\u662f 0\u4e4b\u4f4e\u901a \u6ffe\u6ce2\u6cd5\uff0c\u53ef\u9054\u5230 1.8214\u3002 4. \u7576\u6bd4\u8f03\u8868 6 \u8207\u8868 7 \u7684\u6578\u64da\uff0c\u6211\u5011\u53ef\u4ee5\u6e05\u695a\u770b\u5230\uff0c\u984d\u5916\u4f7f\u7528\u5dee\u91cf\u7279\u5fb5\u53cd\u800c\u540c\u6642\u4f7f PESQ \u8207 STOI \u7684\u5206\u6578\u90fd\u964d\u4f4e\uff0c\u9019\u7d50\u679c\u4f3c\u4e4e\u8868\u660e\uff0c\u5728\u8a13\u7df4\u8cc7\u6599\u589e\u52a0\u6642\uff0c\u5dee\u91cf\u7279\u5fb5\u7684\u53c3\u8207\u4e26\u672a \u5c0d\u65bc IRM \u6a21\u578b\u4e4b\u8a13\u7df4\u6709\u6b63\u9762\u7684\u5f71\u97ff\uff0c\u9019\u80cc\u5f8c\u539f\u56e0\u53ef\u80fd\u662f\u6b64\u6642 IRM \u6a21\u578b\u4e4b\u8907\u96dc\u5ea6\u61c9\u8a72 \u9032\u4e00\u6b65\u63d0\u9ad8\u3001\u4ee5\u56e0\u61c9\u984d\u5916\u7684\u5dee\u91cf\u7279\u5fb5\u5e36\u4f86\u7684\u8cc7\u6599\u591a\u6a23\u6027\u3002\u5982\u679c\u5728\u539f\u59cb IRM \u6a21\u578b\u67b6\u69cb \u7684\u8a2d\u5b9a\u4e0b\uff0c\u4e0d\u4f7f\u7528\u5dee\u91cf\u7279\u5fb5\u53ef\u80fd\u662f\u8f03\u4f73\u7684\u9078\u64c7\uff0c\u540c\u6642\u914d\u5408\u4f4e\u901a\u6ffe\u6ce2\u8655\u7406\uff0c\u53ef\u4f7f PESQ \u5206\u6578\u9032\u4e00\u6b65\u63d0\u5347\u3002 MFCC \u7279\u5fb5 \u539f\u59cb IRM 1 \u4e0d\u540c\u6b0a\u91cd \u6291\u5236\u8abf\u8b8a\u9ad8\u983b\u4e4b IRM 1 0 0.25 0.50 0.75 STOI 0.6947 0.6900 0.6926 0.6918 0.6928 PESQ 1.8182 1.8214 1.7996 1.8056 1.8192 \u8868 7. \u672a\u8655\u7406\u8a9e\u97f3\u8207\u7d93\u904e\u539f\u59cb IRM 2 (\u4f7f\u7528\u539f MFCC \u7279\u5fb5\u8207\u5176\u5dee\u91cf\u7279\u5fb5\u6c42\u53d6) \u3001\u4e0d \u540c\u6b0a\u91cd\u03b1\u6291\u5236\u8abf\u8b8a\u9ad8\u983b\u4e4b IRM 2 (\u6709\u642d\u914d\u5dee\u91cf\u7279\u5fb5)\u8655\u7406\u5f8c\u5c0d\u61c9\u7684 MFCC \u7279\u5fb5 \u539f\u59cb IRM 2 \u4e0d\u540c\u6b0a\u91cd \u6291\u5236\u8abf\u8b8a\u9ad8\u983b\u4e4b IRM 2 0 0.25 0.50 0.75 STOI 0.6863 0.6841 0.6840 0.6880 0.6837 PESQ 1.8003 1.7966 1.7966 1.7853 1.7972 4.4 \u4f7f\u7528\u6642\u983b\u5716\u6f14\u793a\u7d50\u679c (Spectrogram Demonstration for Each Method) \u5316\u6548\u679c\uff0c\u6700\u5f8c\uff0c\u89c0\u5bdf\u539f\u59cb IRM \u8207\u4f4e\u901a\u6ffe\u6ce2\u7279\u5fb5\u4e4b IRM \u6240\u5c0d\u61c9\u7684\u5716 1(d) \u8207 \u5716 2(e) \uff0c\u76f8 \u5c0d\u65bc\u5716 1(b)\uff0c\u96dc\u8a0a\u6240\u9020\u6210\u7684\u5931\u771f\u660e\u986f\u964d\u4f4e\uff0c\u4f46\u6548\u679c\u4e26\u4e0d\u5982\u7406\u60f3 IRM \u6240\u5c0d\u61c9\u7684\u5716 1(c)\uff0c\u4f8b \u5982\u5728\u6642\u9593 0.1-0.3 \u79d2\u4e4b\u9593\u7684\u983b\u8b5c\u5f37\u5ea6\u4e26\u672a\u6709\u6548\u91cd\u5efa(\u5728\u7d05\u8272\u6846\u6240\u6a19\u793a\u5340\u57df)\uff0c\u7136\u800c\u5716 1(e) \u7684\u5728\u6b64\u5340\u57df\u7684\u983b\u8b5c\u91cd\u5efa\u7a0b\u5ea6\u7a0d\u512a\u65bc\u5716 1(d)\uff0c\u6839\u64da\u6b64\u6bd4\u8f03\u7d50\u679c\uff0c\u6211\u5011\u4f3c\u4e4e\u53ef\u770b\u51fa\uff0c\u4f4e\u901a\u6ffe \u6ce2\u7279\u5fb5\u4e4b IRM \u5728\u6b64\u8a9e\u53e5\u7684\u8655\u7406\u4e0a\u7565\u512a\u65bc\u539f\u59cb IRM\u3002 (a) \u539f\u59cb\u4e7e\u6de8\u8a9e\u97f3 [a. the original clean utterance] (b) \u647b\u5165-2 dB SNR \u4e4b babble \u96dc\u8a0a\u4e4b\u8a9e\u97f3 [b. the -2 dB SNR utterance with babble noise] (c) \u96dc\u8a0a\u8a9e\u97f3\u7d93\u7531\u7406\u60f3 IRM \u8655\u7406\u4e4b\u8a9e\u97f3 [c. the oracle-IRM enhanced utterance] (d) \u96dc\u8a0a\u8a9e\u97f3\u7d93\u7531\u539f\u59cb IRM \u8655\u7406\u4e4b\u8a9e\u97f3 [d. the original-IRM enhanced utterance] 4.2 \u5206\u6578\uff0c\u70ba\u4e86\u4f7f\u6574\u9ad4\u6548\u80fd\u512a\u5316 2. \u5c0d\u65bc\u8a9e\u97f3\u54c1\u8cea\u6307\u6a19 PESQ \u800c\u8a00\uff0c\u5728\u4e0d\u4f7f\u7528\u4f4e\u901a\u6ffe\u6ce2\u4e4b\u56db\u985e\u7279\u5fb5\u4e2d\uff0cMFCC \u4ecd\u8868\u73fe\u6700\u4f73 (1.7966) \uff0c\u8d85\u8d8a\u4e86\u7d44\u5408\u7279\u5fb5 (1.7748) \uff0c\u800c AMS \u7279\u5fb5\u8868\u73fe\u8f03\u4e0d\u597d\uff0c\u53ea\u6709 1.6721 \u4e4b PESQ \u503c\u3002\u7136\u800c\uff0c\u7576\u914d\u5408\u4f4e\u901a\u6ffe\u6ce2\u6642\uff0c\u5404\u7a2e\u985e\u7279\u5fb5\u7686\u53ef\u4ee5\u9054\u5230\u66f4\u4f73\u7684 PESQ \u503c\uff0c\u4f8b\u5982\u7576\u4f7f\u7528 0.75\u7684\u6b0a\u91cd\u6642\uff0cMFCC \u5c0d\u61c9\u4e4b PESQ \u503c\u53ef\u4ee5\u9032\u4e00\u6b65\u63d0\u5347\u81f3 1.7977\u3002\u7136\u800c\uff0c\u7372\u5f97 PESQ \u6700\u4f73\u4e4b\u7279\u5fb5\u662f\u7d44\u5408\u7279\u5fb5\u914d\u5408 0.75\u4e4b\u4f4e\u901a\u6ffe\u6ce2\u6cd5\uff0c\u53ef\u9054\u5230 1.7996\u3002 MFCC 1.7966 1.7870 1.7916 1.7946 1.7977 GF 1.7641 1.7791 1.7669 1.7635 1.7633 combo 1.7748 1.7819 1.7916 1.7589 1.7996 \u8868 6. \u672a\u8655\u7406\u8a9e\u97f3\u8207\u7d93\u904e\u539f\u59cb IRM 1 (\u4f7f\u7528\u539f MFCC \u7279\u5fb5\u6c42\u53d6) \u3001\u4e0d\u540c\u6b0a\u91cd\u03b1\u6291\u5236 filtered static MFCC features with different assignments of parameter \u03b1)] original static MFCC features) and the lowpass filtered IRM 1 (using the lowpass [Table 6. The averaged PESQ and STOI results for the original IRM 1 (using the MFCC \u800c\u5f97 \u8abf\u8b8a\u9ad8\u983b\u4e4b IRM 1 \u8655\u7406\u5f8c\u5c0d\u61c9\u7684 STOI \u8207 PESQ \u5e73\u5747\u5206\u6578\u3002\u539f\u7279\u5fb5\u7531\u55ae\u4e00\u7279\u5fb5 (e) \u96dc\u8a0a\u8a9e\u97f3\u7d93\u7531\u4f4e\u901a\u6ffe\u6ce2 IRM \u8655\u7406\u5f8c\u4e4b\u8a9e\u97f3</td></tr><tr><td>3. \u7576\u4f7f\u7528\u5dee\u91cf\u7279\u5fb5\u6642\uff0c\u4e0a\u4e00\u9ede\u7684\u7d50\u679c\u5247\u525b\u597d\u5c0d\u8abf\uff1a\u5373\u82e5\u589e\u52a0\u8a13\u7df4\u8a9e\u6599\uff0c\u5728 PESQ \u5206\u6578\u4e0a\uff0c \u6240\u5c0d\u61c9\u7684\u5f37\u5ea6\u6642\u983b\u5716\uff0c\u9996\u5148\uff0c\u6211\u5011\u6bd4\u8f03\u5716 1(a)\u8207\u5716 1(b)\uff0c\u767c\u73fe\u96dc\u8a0a\u5c0d\u65bc\u8a9e\u97f3\u5728\u6642\u983b\u5716\u4e0a</td></tr></table>", |
| "text": "Taal et al., 2011)\u4f5c\u70ba\u8a9e\u97f3\u53ef\u8b80\u6027(intelligibility) \u7684\u5ba2\u89c0\u6307\u6a19\uff0cPESQ \u5206\u6578\u4ecb\u65bc-0.5 \u8207 4.5 \u4e4b\u9593\uff0c STOI \u5206\u6578\u4ecb\u65bc 0 \u8207 1 \u4e4b\u9593\uff0c\u5206\u6578\u8d8a\u9ad8\u4ee3\u8868\u8a9e\u97f3\u7684\u54c1\u8cea/\u53ef\u8b80 \u6027\u8d8a\u4f73\u3002 \u9996\u5148\uff0c\u8868 1 \u5217\u51fa\u4e86\u6e2c\u8a66\u96dc\u8a0a\u8a9e\u53e5\u5728\u8655\u7406\u524d\u3001\u7d93\u7531\u7406\u60f3 IRM(\u906e\u7f69\u76f4\u63a5\u7531\u4e7e\u6de8\u8a9e\u97f3\u8207\u647b\u96dc \u4e4b\u96dc\u8a0a\u6c42\u5f97)\u53ca\u539f\u59cb IRM(\u4f7f\u7528\u539f\u59cb\u8f38\u5165\u7279\u5fb5\u8a13\u7df4\uff0c\u4e26\u53ef\u80fd\u984d\u5916\u52a0\u5165\u5dee\u91cf\u7279\u5fb5)\u8655\u7406\u5f8c \u6240\u5c0d\u61c9\u7684 PESQ \u8207 STOI \u7684\u5e73\u5747\u503c\u3002\u5f9e\u6b64\u8868\u4e2d\uff0c\u6211\u5011\u53ef\u4ee5\u770b\u5230\uff1a 1. \u96dc\u8a0a\u8a9e\u53e5\u7d93\u904e\u7406\u60f3 IRM \u8655\u7406\u5f8c\uff0c\u5728 PESQ \u8207 STOI \u90fd\u5f97\u5230\u4e86\u5927\u5e45\u7684\u63d0\u5347\u3002 2. \u539f\u59cb IRM \u96d6\u7136\u4e5f\u80fd\u5e36\u4f86\u986f\u8457\u7684\u6539\u9032\uff0c\u4f46\u6548\u679c\u660e\u986f\u8207\u7406\u60f3 IRM \u6709\u5dee\u8ddd\uff0c\u9019\u4ee3\u8868\u4e86\u85c9\u7531 \u96dc\u8a0a\u8a9e\u97f3(\u7279\u5fb5)\u4e2d\u4f30\u6e2c\u4e7e\u6de8\u8a9e\u97f3\u8207\u96dc\u8a0a\u6210\u5206\u4e4b\u7cbe\u6e96\u5ea6\u4ecd\u6709\u5f88\u5927\u7684\u9032\u6b65\u7a7a\u9593\u3002 3. \u5dee\u91cf\u7279\u5fb5\u7684\u6709\u7121\u4e26\u672a\u5c0d\u65bc\u8a13\u7df4\u800c\u5f97 IRM \u5728 STOI \u8207 PESQ \u7684\u8868\u73fe\u4e0a\u6709\u5927\u5e45\u5f71\u97ff\uff0c\u984d\u5916 \u4f7f\u7528\u5dee\u91cf\u7279\u5fb5\u751a\u81f3\u4f7f IRM \u5f97\u5230\u8f03\u4f4e\u7684 STOI \u5206\u6578\u3002 \u63a5\u4e0b\u4f86\uff0c\u6211\u5011\u958b\u59cb\u8a55\u4f30\u6240\u63d0\u4e4b\u65b0 IRM \u8a13\u7df4\u6cd5\uff0c\u8868 2 \u5217\u51fa\u4e86\u5728\u4e0d\u4f7f\u7528\u5dee\u91cf\u7279\u5fb5\u6642\uff0c\u7d66 \u5b9a\u8f38\u5165\u7279\u5fb5\u4e4b\u6642\u5e8f\u5217\u4e4b\u9ad8\u983b\u4fc2\u6578\u4e0d\u540c\u7684\u6b0a\u91cd \uff0c\u7d93\u8a13\u7df4\u4e4b IRM \u6240\u5c0d\u61c9\u7684 STOI \u8207 PESQ \u5206\u6578\uff0c\u5f9e\u6b64\u8868\u4e2d\uff0c\u6211\u5011\u6709\u4ee5\u4e0b\u7684\u767c\u73fe\uff1a 1. \u7576\u4f7f\u7528\u6211\u5011\u63d0\u51fa\u4e4b\u6291\u5236\u8abf\u8b8a\u9ad8\u983b\u7684\u7279\u5fb5\u6cd5\u6642\uff0c\u591a\u6578 \u6b0a\u91cd\u8a2d\u5b9a\u90fd\u5f97\u5230\u4e86\u66f4\u4f73\u7684 STOI \u5217\u51fa\u4e86\u5728\u984d\u5916\u4f7f\u7528\u5dee\u91cf\u7279\u5fb5\u6642\uff0c\u7d66\u5b9a\u8f38\u5165\u7279\u5fb5\u4e4b\u6642\u5e8f\u5217\u4e4b\u9ad8\u983b\u4fc2\u6578\u4e0d\u540c \u7684\u6b0a\u91cd \uff0c\u7d93\u8a13\u7df4\u4e4b IRM \u6240\u5c0d\u61c9\u7684 STOI \u8207 PESQ \u5206\u6578\uff0c\u5f9e\u6b64\u8868\u4e2d\uff0c\u6211\u5011\u6709\u4ee5\u4e0b\u7684\u767c\u73fe\uff1a 1. \u76f8\u8f03\u65bc\u539f\u59cb IRM \u800c\u8a00\uff0c\u4f7f\u7528\u8f03\u5927\u6b0a\u91cd (0.75) \u5728 STOI \u8207 PESQ \u4e0a\u90fd\u6709\u8f03\u660e\u986f\u7684\u6539 \u628a\u8868 6\u30017 \u8207\u8868 4\u30015 \u7684\u6578\u64da\u76f8\u6bd4\u8f03\uff0c\u6211\u5011\u53ef\u4ee5\u770b\u5230\u589e\u52a0\u8a13\u7df4\u8cc7\u6599\u91cf\u53ef\u4ee5\u540c\u6642\u4f7f\u6e2c\u8a66\u8cc7 \u6599\u7684 PESQ \u8207 STOI \u7684\u5206\u6578\u90fd\u660e\u986f\u9032\u6b65\uff0c\u9032\u800c\u9a57\u8b49\u8a13\u7df4\u8cc7\u6599\u7684\u589e\u52a0\u53ef\u4ee5\u4f7f IRM \u6a21\u578b\u5728 \u8a9e\u97f3\u5f37\u5316\u7684\u6548\u679c\u66f4\u597d\u3002 2. \u7576\u6c92\u6709\u4f7f\u7528\u5dee\u91cf\u7279\u5fb5\u6642\uff0c\u82e5\u589e\u52a0\u8a13\u7df4\u8a9e\u6599\uff0c\u5728 STOI \u5206\u6578\u4e0a\uff0c\u539f\u59cb\u7684 IRM \u6bd4\u4f7f\u7528\u4f4e\u901a \u6ffe\u6ce2\u6cd5\u5c0d\u61c9\u7684 IRM \u6548\u679c\u8f03\u4f73\uff0c\u4ee3\u8868\u6b64\u6642\u4f4e\u901a\u7387\u6ce2\u8655\u7406\u4e26\u672a\u5e36\u4f86 STOI \u5206\u6578\u7684\u9032\u6b65\uff0c\u7136 \u800c\u5728 PESQ \u5206\u6578\u4e0a\uff0c\u7576\u914d\u5408\u4f4e\u901a\u6ffe\u6ce2\u6642\uff0c\u53ef\u4ee5\u6bd4\u539f\u59cb IRM \u9054\u5230\u66f4\u4f73\u7684\u7d50\u679c\uff0c\u4f8b\u5982\u7576 \u4f7f\u7528 0.75\u7684\u6b0a\u91cd\u6642\uff0cMFCC \u5c0d\u61c9\u4e4b PESQ \u503c\u53ef\u4ee5\u9032\u4e00\u6b65\u63d0\u5347\u81f3 1.8192\u3002\u7136\u800c\uff0c\u7372", |
| "type_str": "table", |
| "num": null |
| } |
| } |
| } |
| } |
