Base_JSON/prefixO/json/O14/O14-1005.json

{
    "paper_id": "O14-1005",
    "header": {
        "generated_with": "S2ORC 1.0.0",
        "date_generated": "2023-01-19T08:04:43.053156Z"
    },
    "title": "Automatic Multi-track Mixing by Kernel Dependency Estimation",
    "authors": [
        {
            "first": "\u5433\u5b97\u5ead",
            "middle": [],
            "last": "Tsung",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "National Central University",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "Ting",
            "middle": [],
            "last": "Wu",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "National Central University",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "Chia-Hui",
            "middle": [],
            "last": "\u5f35\u5609\u60e0",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "National Central University",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "",
            "middle": [],
            "last": "Chang",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "National Central University",
                "location": {}
            },
            "email": ""
        }
    ],
    "year": "",
    "venue": null,
    "identifiers": {},
    "abstract": "Due to the revolution of digital music, people can create recordings in a home studio with cheaper gear. However multi-track recordings need to be mixed to combine them into one or more channels. The question is that mixing requires background knowledge in sound engineering and psychoacoustics. It is difficult to get good mixdown for non-specialist in sound engineer. In this paper, we use supervised learning method for automatically mixing multi-track recording into coherent and well-balanced piece. Due to lack of mixing parameters, first we estimate the weight of mixing parameters by using the relation between raw multi-track and mixdown. Given the mixing parameters for any music genre, we use kernel decency estimation method to create our mixing model. The experiment show KDE is 42",
    "pdf_parse": {
        "paper_id": "O14-1005",
        "_pdf_hash": "",
        "abstract": [
            {
                "text": "Due to the revolution of digital music, people can create recordings in a home studio with cheaper gear. However multi-track recordings need to be mixed to combine them into one or more channels. The question is that mixing requires background knowledge in sound engineering and psychoacoustics. It is difficult to get good mixdown for non-specialist in sound engineer. In this paper, we use supervised learning method for automatically mixing multi-track recording into coherent and well-balanced piece. Due to lack of mixing parameters, first we estimate the weight of mixing parameters by using the relation between raw multi-track and mixdown. Given the mixing parameters for any music genre, we use kernel decency estimation method to create our mixing model. The experiment show KDE is 42",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Abstract",
                "sec_num": null
            }
        ],
        "body_text": [],
        "back_matter": [],
        "bib_entries": {
            "BIBREF0": {
                "ref_id": "b0",
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            "BIBREF3": {
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            "BIBREF5": {
                "ref_id": "b5",
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                    {
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            "BIBREF6": {
                "ref_id": "b6",
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            "BIBREF7": {
                "ref_id": "b7",
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            "BIBREF8": {
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                "year": 2008,
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        },
        "ref_entries": {
            "FIGREF0": {
                "type_str": "figure",
                "num": null,
                "text": "Production)\u4e0a\u5927\u81f4\u5206\u70ba\u4e09\u500b\u968e\u6bb5\uff0c\u5275\u4f5c\u7de8\u66f2(Pre-Production)\u3001 \u8072\u97f3\u9304\u88fd(Production)\u3001\u5f8c\u88fd(Post-Production)\u3002\u5176\u4e2d\u5f8c\u88fd\u53c8\u5206\u70ba\u6df7\u97f3(Mixing)\u53ca\u6bcd\u5e36\u5f8c\u88fd (Mastering)\u5169\u90e8\u5206; \u6df7\u97f3\u5728\u97f3\u6a02\u88fd\u4f5c\u4e0a\u662f\u500b\u975e\u5e38\u91cd\u8981\u7684\u904e\u7a0b; \u5176\u4e3b\u8981\u7684\u5de5\u4f5c\u662f\u8981\u628a\u5148\u524d \u9304\u88fd\u597d\u7684\u591a\u8ecc(Multi-Track)\u7684\u8072\u97f3\uff0c\u5982\u4eba\u8072\u3001\u5409\u4ed6\u3001\u7235\u58eb\u9f13\u7b49\u8072\u8ecc\u6df7\u5408\u9032\u540c\u4e00\u500b\u7acb\u9ad4\u8072 \u8ecc(stereo channel)\u6216\u55ae\u8072\u8ecc(Mono Channel)\u4e2d\u3002 \u8fd1\u5e74\u4f86\u7531\u65bc\u6578\u4f4d\u97f3\u6a02\u7684\u84ec\u52c3\u767c\u5c55\uff0c\u9304\u97f3\u5668\u6750\u8d8a\u4f86\u8d8a\u666e\u53ca\u3002\u4f7f\u5f97\u975e\u6df7\u97f3\u5c08\u696d\u4eba\u58eb\u4e5f\u80fd \u5229\u7528\u9304\u97f3\u754c\u9762(Audio Interface)\u9304\u88fd\u51fa\u4e0d\u932f\u7684\u6210\u54c1; \u4f46\u662f\u4e00\u65e6\u9304\u88fd\u4e86\u591a\u8ecc(Multi-Track Recording)\u5c31\u6703\u9762\u81e8\u5230\u6df7\u97f3(Mixing)\u7684\u554f\u984c\uff0c\u5373\u9700\u8981\u628a\u591a\u8ecc\u7684\u8072\u97f3\u6df7\u5408\u5728\u540c\u4e00\u500b\u8ecc\u4e2d; \u6df7 \u97f3\u727d\u626f\u5230\u8a31\u591a\u97f3\u97ff\u53ca\u8072\u5b78\u5fc3\u7406\u5b78\u7684\u76f8\u95dc\u6280\u8853\u8207\u77e5\u8b58\uff0c\u975e\u5c08\u696d\u4eba\u58eb\u8981\u6df7\u51fa\u5c1a\u53ef\u7684\u6210\u54c1\u6709\u4e00 \u5b9a\u7684\u96e3\u5ea6\uff0c\u6df7\u51fa\u4f86\u7684\u7d50\u679c\u5f80\u5f80\u6703\u7167\u6210\u6574\u9996\u6b4c\u8046\u807d\u7684\u6e05\u6670\u5ea6\u964d\u4f4e\u3001\u8072\u97f3\u4e0d\u624e\u5be6\u3001\u97f3\u91cf\u843d\u5dee \u592a\u5927\u3001\u7a7a\u9593\u611f\u4e0d\u5920\u3001\u8072\u97f3\u96dc\u4e82\u7b49\u554f\u984c; \u800c\u4e14\u6df7\u97f3\u7684\u8655\u7406\u65b9\u5f0f\u57fa\u672c\u4e0a\u6703\u96a8\u8457\u6a02\u5668\u3001\u97f3\u6a02\u985e \u578b\u800c\u6709\u6240\u4e0d\u540c\uff0c\u4e0d\u540c\u7684\u97f3\u6a02\u985e\u578b\u6703\u6709\u4e0d\u540c\u7684\u6df7\u97f3\u98a8\u683c(Mixing Style)\uff0c\u9019\u66f4\u52a0\u589e\u52a0\u4e86\u4e00\u822c \u975e\u5c08\u696d\u4eba\u58eb\u5b78\u7fd2\u6df7\u97f3\u7684\u96e3\u5ea6\u3002\u6240\u4ee5\u8981\u5982\u4f55\u85c9\u7531\u96fb\u8166\u4f86\u5e6b\u52a9\u6df7\u97f3\u4fbf\u662f\u672c\u7bc7\u8ad6\u6587\u7684\u76ee\u6a19\u3002\u63a5 \u4e0b\u4f86\u5c07\u6703\u8a73\u7d30\u4ecb\u7d39\u6df7\u97f3\u7684\u76f8\u95dc\u80cc\u666f\u77e5\u8b58\u3002",
                "uris": null
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                "content": "<table><tr><td>1.1 \u6df7\u97f3(Mixing) \u6df7\u97f3\u5728\u97f3\u6a02\u88fd\u4f5c\u4e0a\u662f\u975e\u5e38\u91cd\u8981\u7684\u904e\u7a0b\uff0c\u4e0d\u540c\u7684\u6df7\u97f3\u65b9\u5f0f\u5728\u6700\u5f8c\u7684\u6210\u54c1\u4e0a\u6703\u6709\u622a\u7136\u4e0d \u540c\u7684\u6a21\u6a23\u3002\u6df7\u97f3\u7684\u597d\u58de\u6703\u5f71\u97ff\u6574\u9996\u6b4c\u7684\u8868\u73fe\uff0c\u597d\u7684\u6df7\u97f3\u53ef\u4ee5\u63a9\u84cb\u7455\u75b5\u3001\u653e\u5927\u512a\u9ede\uff0c\u63d0\u5347 \u6574\u9ad4\u7684\u8cea\u611f\u3002\u5728\u6df7\u97f3\u7684\u904e\u7a0b\u4e2d\uff0c\u6df7\u97f3\u5e2b(Mixing Engineer)\u6703\u4f9d\u7167\u5404\u97f3\u8ecc/\u6a02\u5668\u9593\u7684\u983b\u7387 (Frequency) \u3001\u97ff\u5ea6(loudness) \u3001\u97f3\u8272\u3001\u97f3\u5834\u5b9a\u4f4d(Panoramic Position) \u3001\u7a7a\u9593\u611f\u7b49\u8072\u97f3\u5143 \u7d20\u52a0\u4ee5\u8abf\u914d\uff0c\u4ee5\u8b93\u6bcf\u500b\u97f3\u8ecc(track)/\u6a02\u5668\u6700\u4f73\u5316\uff0c\u8b93\u6bcf\u500b\u97f3\u8ecc\u5728\u6700\u5f8c\u6df7\u5728\u4e00\u8d77\u6642\u4e00\u6a23\u80fd\u4fdd \u6301\u6e05\u6670\uff0c\u4fdd\u6709\u5c64\u6b21\uff0c\u4f7f\u5f97\u97f3\u6a02\u5448\u73fe\u66f4\u751f\u52d5\u3001\u66f4\u52d5\u807d\u3002 \u5728\u958b\u59cb\u6df7\u97f3\u524d\uff0c\u6df7\u97f3\u5e2b\u6703\u5148\u4f5c\u6df7\u97f3\u898f\u5283(Mixing Design)\uff0c\u898f\u5283\u6574\u9996\u6b4c\u7684\u97f3\u50cf(Sound Image)\uff0c\u6c7a\u5b9a\u6bcf\u500b\u97f3\u8ecc\u5728\u6574\u9996\u6b4c\u88e1\u7684\u5b9a\u4f4d\u4ee5\u53ca\u5176\u91cd\u8981\u6027\u3002\u5982\u4e0b\u5716 1 \u70ba\u4e00\u9996\u7235\u58eb\u6a02\u7684\u6df7\u97f3 \u898f\u5283\u793a\u610f\u5716\uff0c\u6211\u5011\u53ef\u4ee5\u767c\u73fe\u5728\u9019\u9996\u6b4c\u4e2d\u4eba\u8072(Vocal)\u8a2d\u8a08\u5728\u97f3\u50cf\u7684\u6b63\u4e2d\u9593\uff0c\u5409\u4ed6(Guitar) \u5206\u5225\u843d\u5728\u4eba\u8072\u7684\u5de6\u53f3; \u97f3\u91cf\u65b9\u9762\u4e3b\u5409\u4ed6(Lead Guitar)\u7565\u5927\u65bc\u4eba\u8072\u7b49\u7b49\uff0c\u6df7\u97f3\u7684\u904e\u7a0b\u4e2d\u4e3b \u8981\u5c31\u662f\u8abf\u914d\u9019\u4e9b\u97f3\u91cf(Volume)\u3001\u7b49\u5316(Equalization)\u3001\u64fa\u4f4d(Pan)\u4f86\u9054\u6210\u6211\u5011\u7684\u6df7\u97f3\u898f\u5283\u8b93 \u6574\u9ad4\u66f4\u52a0\u548c\u8ae7\uff0c\u8b93\u500b\u6a02\u5668\u878d\u5165\u5176\u4e2d\u3002 \u5716 1 \u6df7\u97f3\u8a2d\u8a08\u793a\u610f\u5716\uff1a\u4eba\u8072(Vocal)\u5c45\u4e2d \u5716\u7247\u4f86\u6e90\uff1aThe Art of Mixing -David Gibson 1.2 \u7814\u7a76\u52d5\u6a5f(Motivation) \u5728\u81ea\u52d5\u6df7\u97f3(Automatic Mixing)\u7684\u7814\u7a76\u4e2d\uff0c\u5927\u591a\u90fd\u662f\u5229\u7528\u8072\u97f3\u7279\u5fb5\u9593\u7684\u95dc\u4fc2\u4f30\u8a08\u4ee5\u53ca \u9810\u6e2c\u5176\u6df7\u97f3\u7684\u53c3\u6578\uff0c\u6240\u4f7f\u7528\u7684\u8072\u97f3\u9577\u5ea6\u5927\u591a\u70ba\u4e00\u9996\u6b4c\u4e2d 30 \u79d2\u7684\u7247\u6bb5\uff0c\u6700\u5f8c\u6240\u6df7\u97f3\u51fa\u4f86 \u7684\u6210\u54c1\u7576\u7136\u4e5f\u6703\u76f8\u4f3c\u65bc\u9019 30 \u79d2\u7684\u7247\u6bb5\u3002\u4f46\u662f\u6df7\u97f3\u5be6\u969b\u4e0a\u662f\u6703\u56e0\u70ba\u6a4b\u6bb5\u7684\u4e0d\u540c\u800c\u6709\u4e0d\u540c \u7684\u6df7\u97f3\u65b9\u5f0f\u7684\uff0c\u4f8b\u5982\u5728\u4e3b\u6b4c(Verse)\u8207\u526f\u6b4c(Chorus)\u7684\u6df7\u97f3\u65b9\u5f0f\u6703\u662f\u4e0d\u4e00\u6a23\u7684\uff0c\u5f8c\u8005\u5176\u6a4b \u6bb5\u901a\u5e38\u70ba\u6b4c\u66f2\u4e2d\u6fc0\u6602\u7684\u90e8\u4efd\uff0c\u914d\u5668\u4f7f\u7528\u6703\u524d\u8005\u591a\uff0c\u5728\u97f3\u91cf\u6216\u983b\u7387\u4e0a\u6bd4\u4f8b\u6703\u6709\u6240\u4e0d\u540c\u3002 \u518d\u8005\u6b4c\u66f2\u97f3\u6a02\u985e\u578b\u4e5f\u6703\u5f71\u97ff\u6df7\u97f3\u7684\u65b9\u5f0f\uff0c\u540c\u6a23\u662f\u7235\u58eb\u9f13\u5728\u6d41\u884c\u6b4c\u8207\u7235\u58eb\u6a02\u4e2d\u97f3\u8272\u8207 \u5176\u5360\u6709\u7684\u6bd4\u4f8b\u4e5f\u6703\u6709\u6240\u4e0d\u540c\uff0c\u540c\u6a23\u7684\u6a02\u5668\u5728\u4e0d\u540c\u7684\u97f3\u6a02\u985e\u578b\u4e2d\u6703\u6709\u4e0d\u540c\u7684\u97f3\u8272\u4ee5\u53ca\u89d2\u8272\uff0c \u4f8b\u5982\uff0c\u5728\u7235\u58eb\u6a02\u4e2d\u9f13\u624b\u4e5f\u53ef\u4ee5\u662f\u6a02\u66f2\u4e2d\u7684\u4e3b\u89d2\uff0c\u5728\u6d41\u884c\u6b4c\u4e2d\u4e3b\u89d2\u5f80\u5f80\u662f\u4eba\u8072\u6216\u662f\u96fb\u5409\u4ed6\u3002 \u6240\u4ee5\u82e5\u5229\u7528 30 \u79d2\u7247\u6bb5\u5efa\u7acb\u7684\u6a21\u578b\u4f86\u5957\u7528\u5728\u6574\u9996\u6b4c\u66f2\u7684\u6df7\u97f3\u5c07\u6703\u9020\u6210\u6574\u9996\u6b4c\u8f03\u5e73\u6de1\u7121\u5473; \u5728 Jeffrey Scott et al.[2]\u7684\u8a2a\u554f\u4e2d\u4e5f\u6709\u63d0\u5230\uff0c\u6df7\u97f3\u5176\u5be6\u662f\u500b\u5225\u7684(Case By Case)\uff0c\u6df7\u97f3\u5e2b\u57fa \u672c\u4e0a\u90fd\u6703\u4f9d\u7167\u5404\u97f3\u8ecc\u7684\u8072\u97ff\u3001\u97f3\u6a02\u985e\u578b\u4e0d\u540c\u800c\u6709\u4e0d\u540c\u7684\u8655\u7406\u65b9\u5f0f\uff0c\u518d\u8005\u97f3\u6a02\u611f\u53d7\u662f\u975e\u5e38 \u4e3b\u89c0\u7684\uff0c\u4e0d\u540c\u7684\u4eba\u6703\u6709\u4e0d\u540c\u7684\u504f\u597d\uff0c\u6240\u4ee5\u6bd4\u8f03\u96e3\u53bb\u8a02\u51fa\u4e00\u500b\u901a\u5247(General Rule)\u4f86\u9032\u884c\u6df7 \u97f3\u3002 \u4e00\u500b\u6df7\u97f3\u6a21\u578b\u5305\u62ec\u97f3\u91cf\u3001\u983b\u7387(Equalization)\u3001\u6a02\u5668\u64fa\u4f4d(Panning)\u7b49\u591a\u500b\u53c3\u6578\uff0c\u5927\u591a \u6578\u7684\u7814\u7a76\u90fd\u662f\u7368\u7acb\u7684\u53bb\u9810\u6e2c\u5404\u8ecc\u7684\u6df7\u97f3\u53c3\u6578\uff0c\u5982\u591a\u7dda\u6027\u8ff4\u6b78\uff0c\u5229\u7528\u5404\u8ecc\u7279\u5fb5\u9593\u7684\u95dc\u4fc2\u53bb \u5efa\u7acb\u5404\u8ecc\u7684\u8ff4\u6b78\u6a21\u578b\u3002\u4f46\u5be6\u969b\u4e0a\u6bcf\u8ecc\u9593\u7684\u6df7\u97f3\u53c3\u6578\u662f\u6709\u4f9d\u8cf4\u95dc\u4fc2\u7684(Dependency)\uff0c\u8209\u4f8b \u4f86\u8aaa\u6709\u4e00\u8ecc\u7684\u97f3\u91cf\u4e0a\u5347\u52e2\u5fc5\u5c31\u6703\u6709\u4e00\u8ecc\u7684\u97f3\u91cf\u4e0b\u964d\uff0c\u9019\u6a23\u6574\u9996\u6b4c\u7684\u97f3\u91cf\u624d\u4e0d\u6703\u5ffd\u5927\u5ffd\u5c0f\uff0c \u82e5\u662f\u5c0d\u5404\u8ecc\u7368\u7acb\u53bb\u5efa\u7acb\u5176\u6a21\u578b\u7684\u8a71\uff0c\u6700\u5f8c\u7684\u6210\u54c1\u5c07\u6703\u55aa\u5931\u5176\u4f9d\u8cf4\u95dc\u4fc2\u3002\u6240\u4ee5\u5728\u53c3\u6578\u9810\u6e2c \u7684\u65b9\u6cd5\u9078\u7528\u4e0a\u6211\u5011\u8a8d\u70ba\u9700\u8981\u8003\u616e\u5230\u4f9d\u8cf4\u6027\u7684\u554f\u984c\u3002 \u7531\u4ee5\u4e0a\u4e09\u9ede\uff0c\u672c\u7bc7\u8ad6\u6587\u63a1\u7528\u5c0d\u4e0d\u540c\u7684\u97f3\u6a02\u985e\u578b\u4e0d\u540c\u7684\u6a4b\u6bb5\uff0c\u5229\u7528\u548c\u4f9d\u8cf4\u4f30\u8a08(Kernel Dependency Estimation)[1]\u7684\u65b9\u6cd5\u4f86\u5efa\u7acb\u6700\u5f8c\u7684\u6a21\u578b\u3002\u9996\u5148\u6211\u5011\u6703\u5148\u9700\u8981\u4f7f\u7528\u8005\u5148\u63d0\u4f9b\u4e00 \u4e9b\u8a72\u6b4c\u66f2\u7684\u8a0a\u606f\uff0c\u4f8b\u5982\u97f3\u6a02\u5f62\u614b\u3001\u6a4b\u6bb5\u3001\u5206\u8ecc\u7684\u6a02\u5668\u6a19\u7c64\u7b49\u7b49\uff0c\u63a5\u8457\u5728\u5c0d\u500b\u5225\u7684\u6a4b\u6bb5\u5957 \u7528\u8a72\u97f3\u6a02\u985e\u578b\u7684\u6838\u4f9d\u8cf4\u6a21\u578b\u9810\u6e2c\u51fa\u6df7\u97f3\u53c3\u6578\u5f8c\u5373\u5b8c\u6210\u6df7\u97f3\u3002(\u5716 2) \u5716 2 \u7cfb\u7d71\u4f7f\u7528\u6982\u5ff5\u5716 \u672c\u7bc7\u8ad6\u6587\u7684\u67b6\u69cb\u5982\u4e0b\uff0c\u7b2c\u4e8c\u7ae0\u5c07\u6703\u8a0e\u8ad6\u81ea\u52d5\u6df7\u97f3\u7684\u76f8\u95dc\u7814\u7a76\uff0c\u7b2c\u4e09\u7ae0\u5247\u6703\u4ecb\u7d39\u672c\u7bc7 \u8ad6\u6587\u7684\u7814\u7a76\u65b9\u6cd5\u53ca\u6240\u4f7f\u7528\u7684\u8cc7\u6599\u96c6\uff0c\u7b2c\u56db\u7ae0\u70ba\u5be6\u9a57\uff0c\u6211\u5011\u6703\u5c0d\u6211\u5011\u7684\u6df7\u97f3\u6a21\u578b\u53bb\u505a\u4ea4\u53c9 \u9a57(Cross validation) \u4f86\u8a55\u4f30\u6a21\u578b\u7684\u6b63\u78ba\u6027\u4ee5\u53ca\u4f9d\u8cf4\u6027\u7684\u529f\u7528\uff0c\u7b2c\u4e94\u7ae0\u70ba\u7d50\u8ad6\u4ee5\u53ca\u672a\u4f86\u5de5 \u4f5c\u3002 \u4e8c\u3001 \u76f8\u95dc\u7814\u7a76 \u5728\u4ecb\u7d39\u6df7\u97f3\u53c3\u6578\u9810\u6e2c\u6a21\u578b\u4e4b\u524d\uff0c\u6211\u5011\u5fc5\u9808\u8a2d\u6cd5\u53d6\u5f97\u4e00\u4e9b\u6b4c\u66f2\u7684\u6df7\u97f3\u53c3\u6578\u4ee5\u4f5c\u70ba\u8a13\u7df4 \u8cc7\u6599\uff0c\u4e0d\u904e\u6df7\u97f3\u53c3\u6578\u5728\u5be6\u969b\u4e0a\u662f\u96e3\u4ee5\u53d6\u5f97\u7684\uff0c\u7531\u65bc\u6df7\u97f3\u5e2b\u4f7f\u7528\u7684\u8edf\u9ad4\u4e0d\u540c\uff0c\u4e0d\u540c\u7684\u5668\u6750 /\u8edf\u9ad4\u6703\u6709\u4e0d\u540c\u7684\u8a2d\u5b9a\u3001\u57fa\u6e96\u53ca\u523b\u5ea6\uff0c\u9019\u8b93\u5f97\u5230\u6df7\u97f3\u53c3\u6578\u9019\u4e00\u985e\u7684\u8cc7\u8a0a\u8b8a\u5f97\u975e\u5e38\u56f0\u96e3\uff0c \u4e14\u6df7\u97f3\u5e2b\u5728\u6df7\u97f3\u6642\u4e5f\u9bae\u5c11\u6703\u628a\u53c3\u6578\u8a18\u9304\u4e0b\u4f86\u3002\u6240\u4ee5\u5728\u591a\u8ecc\u6df7\u97f3(Multitrack Mixing)\u7684\u76f8\u95dc \u7814\u7a76\u4e2d\u5927\u90e8\u4efd\u7684\u7814\u7a76\u8457\u91cd\u65bc\u5982\u4f55\u4f30\u8a08\u51fa\u6df7\u97f3\u53c3\u6578\uff0c\u5305\u62ec\uff1a\u97f3\u91cf(Volume)\u3001\u983b\u7387(Frequency)\u3001 \u52d5\u614b(Dynamic)\u7b49\u7b49\u3002\u53e6\u4e00\u90e8\u4efd\u7684\u7814\u7a76\u8457\u91cd\u5728\u5982\u4f55\u5efa\u7acb\u6df7\u97f3\u53c3\u6578\u6a21\u578b\u3002 2.1. \u6df7\u97f3\u57fa\u672c\u5143\u7d20(Basic Factor of Mixing) \u5728\u6df7\u97f3\u7684\u904e\u7a0b\u4e2d\u97f3\u91cf\u5e73\u8861\u662f\u4ef6\u5f88\u91cd\u8981\u7684\u4e8b\uff0c\u6df7\u97f3\u5e2b(Mixing Engineer)\u6703\u8abf\u6574\u6bcf\u500b\u97f3 \u8ecc\u9593\u5f7c\u6b64\u4e4b\u9593\u7684\u97f3\u91cf(\u5716 1)\uff0c\u6c7a\u5b9a\u5404\u8ecc\u5728\u9019\u9996\u6b4c\u4e2d\u7684\u97f3\u91cf\u6bd4\u4f8b\uff0c\u5373\u662f\u6c7a\u5b9a\u5404\u8ecc\u5728\u97f3\u50cf (Image)\u7684\u524d\u5f8c\u9806\u5e8f; \u82e5\u5176\u4e2d\u4e00\u97f3\u8ecc\u7684\u97f3\u91cf\u6bd4\u5176\u4ed6\u97f3\u8ecc\u9084\u8981\u5927\u5f88\u591a\u7684\u8a71\uff0c\u6574\u9996\u6b4c\u5c07\u6703\u807d\u8d77 \u4f86\u982d\u91cd\u8173\u8f15\u3002 \u5728\u97f3\u8272\u4fee\u6b63\u7684\u904e\u7a0b\u4e2d\uff0c\u6df7\u97f3\u5e2b(Mixing Engineer)\u6703\u7528\u5230\u7b49\u5316\u5668(Equalizer, EQ)\u7684\u5de5\u5177 \u4f86\u53bb\u5c0d\u6bcf\u500b\u97f3\u8ecc\u7684\u983b\u7387\u505a\u4fee\u6b63\u8abf\u6574\u3002\u4f8b\u5982\u6211\u5011\u767c\u73fe\u5409\u4ed6\u7684\u97f3\u8272\u8ddf\u5176\u4ed6\u7684\u6a02\u5668\u76f8\u6bd4\u592a\u4eae\u592a \u5c16\u92b3\uff0c\u4ee5\u81f3\u65bc\u7121\u6cd5\u878d\u5165\u9019\u9996\u6b4c\u4e2d\uff0c\u6211\u5011\u5c31\u53ef\u4ee5\u7528\u7b49\u5316\u5668\u53bb\u5c0d\u5409\u4ed6\u7684\u9ad8\u983b\u90e8\u5206\u505a\u8870\u6e1b(cut)\u3002 \u983b\u7387\u9593\u7684\u5e73\u8861\u5728\u6df7\u97f3\u904e\u7a0b\u4e2d\u4e5f\u662f\u91cd\u8981\u7684\u904e\u7a0b\u4e4b\u4e00\u3002 \u6a02\u5668\u64fa\u4f4d\u662f\u5c07\u9304\u88fd\u597d\u7684\u8072\u97f3\u8a0a\u865f\u653e\u7f6e\u65bc\u65b0\u7684\u96d9\u8072\u9053\u6216\u591a\u8072\u9053\u7684\u8072\u5834(Sound Field)\u3002 \u7531\u65bc\u6211\u5011\u4e00\u822c\u97f3\u97ff\u8a2d\u5099\u7684\u74b0\u5883\u57fa\u672c\u4e0a\u662f\u4ee5\u96d9\u8072\u9053\u70ba\u4e3b(stereo)\uff0c\u96d9\u8072\u9053\u7684\u6df7\u97f3\u53ef\u4ee5\u5728\u8046\u807d \u4e0a\u589e\u52a0\u5e73\u9762\u7684\u807d\u611f\uff0c\u800c\u4e0d\u662f\u53ea\u6709\u4e00\u9ede;\u6240\u4ee5\u6211\u5011\u5728\u6df7\u97f3\u7684\u6642\u5019\u6703\u6c7a\u5b9a\u5404\u6a02\u5668\u7684\u64fa\u4f4d\uff0c\u770b \u662f\u8981\u64fa\u5728\u4e2d\u9593\u9084\u662f\u64fa\u5728\u9760\u8fd1\u5de6\u8072\u9053/\u5de6\u5587\u53ed\u9084\u662f\u53f3\u8072\u9053/\u53f3\u5587\u53ed\u7b49\u7b49\u4f86\u589e\u52a0\u6574\u9ad4\u7684\u7a7a\u9593\u611f\uff0c Left_output = cos(p)*input Right_output = sin(p)*input (1) \u5176\u4e2d p \u70ba\u504f\u96e2\u4e2d\u592e\u9ede\u7684\u89d2\u5ea6\uff0cLeft_output, Right_output \u5206\u5225\u70ba\u5de6\u8072\u9053\u53f3\u8072\u9053\u7684\u8f38\u51fa\u3002 2.2. \u6df7\u97f3\u53c3\u6578\u4f30\u8a08(Mixing Parameter Estimation) \u5982\u524d\u4e00\u7bc0\u7814\u7a76\u52d5\u6a5f\u6240\u63d0\u5230\uff0c\u7531\u65bc\u6df7\u97f3\u53c3\u6578\u96e3\u4ee5\u53d6\u5f97\uff0c\u6211\u5011\u9700\u8981\u5229\u7528\u539f\u59cb\u5206\u8ecc\u548c\u6df7\u97f3 \u6210\u54c1(Mixdown)\u9593\u7684\u95dc\u4fc2\u4f86\u4f30\u8a08\u51fa\u6bcf\u9996\u6b4c\u7684\u6df7\u97f3\u53c3\u6578\u3002\u5728 Jeffrey Scott et al. [2]\u7684\u7814\u7a76\u4e2d\uff0c \u4ed6\u5011\u63a1\u7528\u4e86\u8a2a\u554f(interview)\u7684\u65b9\u5f0f\uff0c\u8a2a\u554f\u4e86\u7dda\u4e0a\u7684\u6df7\u97f3\u5e2b(Mixing Engineer)\u4e86\u89e3\u4ed6\u5011\u5982\u4f55 \u6df7\u97f3\u3001\u5982\u4f55\u8655\u7406\u8072\u97f3\uff0c\u5229\u7528\u8a2a\u554f\u5f8c\u5f97\u5230\u7684\u4e00\u4e9b\u901a\u5247\u4f86\u7576\u4f5c\u4ed6\u5011\u6700\u5f8c\u6df7\u97f3\u6a21\u578b\u5efa\u7acb\u7684\u4f9d\u64da\u3002 \u5728\u5927\u90e8\u5206\u81ea\u52d5\u6df7\u97f3(Automatic Mixing)\u7684\u7814\u7a76\u4e2d[3, 4]\uff0c\u5728\u6df7\u97f3\u53c3\u6578\u4f30\u8a08\u4e0a\u5927\u591a\u662f\u5047\u8a2d\u5176 \u8a08\u3002 \u5716 3 \u6700\u5c0f\u5e73\u65b9\u6cd5 \u7531\u65bc\u63a1\u7528\u7dda\u6027\u7d44\u5408\u7684\u5047\u8a2d\uff0c\u6bcf\u8ecc\u9593\u5f7c\u6b64\u8981\u662f\u7dda\u6027\u7368\u7acb(Linearly independent)\u7684\uff0c\u9019\u6703 \u5f71\u97ff\u6700\u5f8c\u8072\u97f3\u7279\u5fb5\u7684\u9078\u7528 \u4ee5\u53ca\u97f3\u8ecc\u7684\u9078\u64c7\uff0c\u9078\u64c7\u542b\u6709\u8f03\u591a\u4e32\u97f3\u7684\u97f3\u8ecc (\u4f8b\u5982\u9304\u88fd overhead \u6642\u6703\u9023\u5927\u9f13\u5c0f\u9f13\u7b49\u5176\u4ed6\u9f13\u7d44\u7684\u8072\u97f3\u4e00\u4f75\u9304\u9032)\uff0c\u5728\u5be6\u52d9\u4e0a\u56e0\u70ba\u5305\u542b\u591a\u500b\u6a02\u5668\u7684 \u8072\u97f3\uff0c\u4f7f\u5f97\u97f3\u8ecc\u9593\u5f7c\u6b64\u4e26\u975e\u7368\u7acb\u6703\u9020\u6210\u5176\u4f30\u8a08\u7d50\u679c\u6703\u6709\u8aa4\u5dee\u3002 2.3. \u6df7\u97f3\u53c3\u6578\u9810\u6e2c \u4e09\u3001 \u7814\u7a76\u65b9\u6cd5 \u672c\u7cfb\u7d71\u4e3b\u8981\u6d41\u7a0b\u5982\u4e0b\u5716 4 \u6240\u793a\uff0c\u6211\u5011\u6703\u5148\u628a\u539f\u59cb\u7684\u5206\u8ecc\u9304\u97f3\u6a94\u4f9d\u7167\u4f7f\u7528\u8005\u63d0\u4f9b\u8a72 \u97f3\u6a02\u7684\u8cc7\u8a0a\u505a\u524d\u8655\u7406\uff0c\u63a5\u4e0b\u4f86\u505a\u8072\u97f3\u7279\u5fb5\u7684\u64f7\u53d6(Feature Extraction)\u4ee5\u4fbf\u4e4b\u5f8c\u6a21\u578b(Model) \u7684\u8a13\u7df4\u53ca\u6e2c\u8a66\u3002\u7531\u65bc\u4e0d\u540c\u7684\u985e\u578b\u3001\u4e0d\u540c\u6a4b\u6bb5\u7684\u97f3\u6a02\u6703\u6709\u4e0d\u540c\u7684\u6df7\u97f3\u65b9\u5f0f\uff0c\u5728\u6a21\u578b\u5efa\u7acb\u6642 \u6211\u5011\u6703\u7279\u5225\u4f9d\u7167\u4e0d\u540c\u7684\u97f3\u6a02\u985e\u578b\u5efa\u7acb\u500b\u5225\u7684\u6df7\u97f3\u53c3\u6578\u6a21\u578b\uff0c\u5728\u4f9d\u4e0d\u540c\u7684\u6a4b\u6bb5\u53bb\u5efa\u7acb\u6a21\u578b\uff0c \u5982\u5716 5 \u6211\u5011\u6703\u5c0d ROCK \u7684\u7684\u97f3\u6a02\u985e\u578b\u5efa\u7acb Intro\u3001Verse\u3001Chorus\uff0cPOP \u97f3\u6a02\u985e\u578b\u4e5f\u5efa\u7acb \u5176\u4e09\u500b\u6a4b\u6bb5\u7684\u6a21\u578b\uff0c\u4ee5\u6b64\u985e\u63a8\u3002\u8a13\u7df4(Training)\u7684\u90e8\u5206\u6709\u5169\u5927\u6b65\u9a5f; \u7b2c\u4e00\u6b65\u9a5f\u662f\u6df7\u97f3\u53c3\u6578 \u4f30\u8a08(Parameter Estimation)\uff0c\u7531\u65bc\u6df7\u97f3\u53c3\u6578\u96e3\u4ee5\u53d6\u5f97\uff0c\u539f\u59cb\u8a13\u7df4\u8cc7\u6599\u4e2d\u4e5f\u7121\u6b64\u8cc7\u8a0a\uff0c\u6211 \u5011 \u6703 \u5148 \u5229 \u7528 \u539f \u59cb \u5206 \u8ecc \u9304 \u97f3 \u548c \u6df7 \u97f3 \u6210 \u54c1 (Mixdown) \u505a \u6700 \u5c0f \u5e73 \u65b9 \u6cd5 \u4f30 \u8a08 (Least Square Estimation)\uff0c\u85c9\u6b64\u4f86\u4f30\u8a08\u51fa\u8a13\u7df4\u8cc7\u6599\u4e2d\u6bcf\u9996\u6b4c\u7684\u6df7\u97f3\u53c3\u6578\u7684\u6b0a\u91cd(Weight of Mixing Parameter) \u3002 3.1 \u8cc7\u6599\u96c6 (DATASET) \u6211\u5011\u4f7f\u7528\u7684\u8cc7\u6599\u96c6(Data Set)\u662f\u4f86\u81ea\u570b\u5916\u4e00\u672c\u95dc\u65bc\u6df7\u97f3\u7684\u5c08\u9580\u66f8\u7c4d\"Mixing Secrets for Small Studio\"[7]\uff0c\u6b64\u66f8\u6709\u63d0\u4f9b\u591a\u9996\u539f\u59cb\u5206\u8ecc\u6a94\u6848\u7d66\u8b80\u8005\u7528\u65bc\u6df7\u97f3\u7df4\u7fd2\u7528\uff0c\u5176\u542b\u62ec\u7684 \u97f3\u6a02\u985e\u578b\u6416\u6efe\u3001\u7235\u58eb\u3001\u9109\u6751\u7b49\u591a\u7a2e\u97f3\u6a02\u985e\u578b\uff0c\u5982\u4e0b\u8868 1 \u6240\u793a\uff0c\u6b64\u66f8\u5c07\u76f8\u4f3c\u7684\u97f3\u6a02\u985e\u578b \u5206\u6210\u56db\u5927\u985e\u3002\u6b64\u8cc7\u6599\u96c6\u8f03\u7279\u5225\u7684\u9ede\u5728\u65bc\u63d0\u4f9b\u7684\u8072\u97f3\u6a94\u6848\u9577\u5ea6\u662f\u6574\u9996\u6b4c(Full Multitrack)\uff0c \u4e00\u822c\u4ee5\u5f80\u97f3\u6a02\u8cc7\u8a0a\u63a2\u52d8(Music Information Retrieval)\u7814\u7a76\u6240\u4f7f\u7528\u7684\u8cc7\u6599\u96c6\u5927\u591a\u662f 20~30 sec \u7684\u9577\u5ea6\uff0c\u9bae\u5c11\u6709\u63d0\u4f9b\u6574\u9996\u6b4c\u7684\u8cc7\u6599\u96c6\u3002\u6b64\u7279\u9ede\u6709\u5229\u65bc\u5e6b\u52a9\u6211\u5011\u5c0d\u65bc\u4e0d\u540c\u7684\u97f3\u6a02\u6a4b\u6bb5 (Music Section)\u53bb\u5efa\u7acb\u4e0d\u540c\u7684\u6a21\u578b\uff0c\u4f86\u8b93\u6700\u5f8c\u7684\u6a21\u578b\u80fd\u66f4\u9069\u7528\u65bc\u5be6\u969b\u7684\u60c5\u6cc1\uff0c\u6b64\u8cc7\u6599\u96c6 \u6240\u63d0\u4f9b \u7684\u6df7\u97f3 \u6210\u54c1 (Mixdown)\u4e00\u6a23\u4e5f\u662f \u6574\u9996 \u6b4c\u7684\u9577 \u5ea6\u3002 \u6a94 \u6848\u683c\u5f0f \u70ba\u7121\u640d WAV \u6a94 (uncompressed WAV files, 24bit and 44.1 kHz sample rate)\u3002 \u8868 1 \u97f3\u6a02\u985e\u578b\u7d71\u8a08\u8868 Genre \uff03 of Song \u7684\u5206\u8ecc\u6a94\u6848\u53ca\u6700\u5f8c\u5404\u6df7\u97f3\u7684\u53c3\u6578\u4f86\u9032\u884c\u76e3\u7763\u5f0f\u5b78\u7fd2(Supervised Learning)\u3002\u4f46\u662f\u5be6\u969b\u4e0a\u6df7 \u97f3\u53c3\u6578\u7684\u8cc7\u8a0a\u662f\u975e\u5e38\u96e3\u53d6\u5f97\u7684\u3002\u7531\u65bc\u6df7\u97f3\u5e2b\u4f7f\u7528\u7684\u8edf\u9ad4\u4e0d\u540c\uff0c\u4e0d\u540c\u7684\u5668\u6750/\u8edf\u9ad4\u6703\u6709\u4e0d \u540c\u7684\u8a2d\u5b9a\u3001\u57fa\u6e96\u53ca\u523b\u5ea6\uff0c\u800c\u4e14\u6df7\u97f3\u5e2b\u5728\u6df7\u97f3\u6642\u4e5f\u9bae\u5c11\u6703\u628a\u53c3\u6578\u8a18\u9304\u4e0b\u4f86\uff0c\u9019\u8b93\u5f97\u5230\u6df7\u97f3 \u53c3\u6578\u9019\u4e00\u985e\u7684\u8cc7\u8a0a\u7684\u53d6\u5f97\u8b8a\u5f97\u975e\u5e38\u56f0\u96e3\u3002\u70ba\u4e86\u4e4b\u5f8c\u7684\u76e3\u7763\u5f0f\u5b78\u7fd2\uff0c\u6211\u5011\u5fc5\u9808\u5148\u4f30\u8a08\u51fa\u8cc7 \u6599\u96c6\u4e2d\u6bcf\u9996\u6b4c\u7684\u6df7\u97f3\u53c3\u6578\uff0c\u5229 \u7528 \u539f \u59cb \u5206 \u8ecc\u6a94 \u6848 (Raw Multi-track) \u53ca\u6700\u5f8c\u6df7\u97f3\u6210\u54c1 (Mixdown)\u4f86\u4f30\u8a08\u51fa\u6bcf\u9996\u6b4c\u7684\u6df7\u97f3\u53c3\u6578\uff0c\u91dd\u5c0d\u6bcf\u4e00\u9996\u6b4c\u6c42\u5f97\u5176\u6df7\u97f3\u53c3\u6578\u7576\u4f5c\u4e4b\u5f8c\u76e3\u7763\u5f0f \u5b78\u7fd2(Supervised Learning)\u7684\u4f9d\u64da\u3002 \u70ba\u4e86\u5f9e\u539f\u59cb\u5206\u8ecc\u6a94\u6848\u4f30\u8a08\u51fa\u5176\u6df7\u97f3\u53c3\u6578\uff0c\u6211\u5011\u5047\u8a2d\u539f\u59cb\u5206\u8ecc\u8207\u6700\u5f8c\u6df7\u97f3\u7684\u6210\u54c1 (Final Mix)\u7684\u95dc\u4fc2\u662f\u4e00\u500b\u7dda\u6027\u7684\u7d44\u5408(Linear Combination)\uff0c\u5982\u4e0b(2)\u5f0f\u70ba\u4e00\u9996\u6b4c\u7684\u7dda\u6027\u7d44 \u5408\u95dc\u4fc2\u3002 \u03b1 1 U 1 +\u03b1 2 U 2 +\u2026..+\u03b1 k U k =V (2) \u03b1 i \u70ba\u7b2c i \u8ecc\u7684\u6df7\u97f3\u53c3\u6578\u6b0a\u91cd\uff0cU i = [u 1i , u 2i , \u2026 , u Ni ] T \u70ba\u7b2c i \u8ecc\u7279\u5fb5\u5411\u91cf\uff0c\uff36\u70ba\u6700\u5f8c\u6df7\u97f3\u7d50 \u679c\u7684\u7279\u5fb5\u5411\u91cf(Feature Vector)\uff0c\u6bcf\u4e00\u8ecc\u62bd\u53d6 N \u500b frames \u505a\u70ba\u5176\u4ee3\u8868\u3002\u5176\u4e2d\u5728\u4e0d\u540c\u7684\u6df7 \u983b\u7387(Frequency) \u983b\u7387\u53c3\u6578\u5373\u662f\u63a7\u5236\u6574\u9996\u6b4c\u4e2d\u5404\u97f3\u8ecc\u5728\u983b\u7387\u4e0a\u7684\u5e73\u8861\u3002\u5728\u6df7\u97f3\u904e\u7a0b\u4e2d\u70ba\u4e86\u4fee\u6b63\u97f3 \u8ecc\u7684\u97f3\u8272\u6216\u983b\u7387\u6642\u6703\u7528\u5230\u7b49\u5316\u5668\u4f86\u5e6b\u52a9\u6211\u5011\u5c0d\u8072\u97f3\u7684\u983b\u7387\u4f5c\u8abf\u6574\uff0c\u4e5f\u5c31\u662f\u8aaa\u983b\u7387\u53c3 \u6578\u5373\u662f\u7b49\u5316\u5668(Equalizer) \u53c3 \u6578 \u3002 \u5728 \u8a2d \u8a08 \u7b49 \u5316 \u5668 \u6642 \u6703 \u5148 \u5c07 \u6574 \u500b \u983b \u8b5c \u5207 \u6210 \u591a \u6bb5 (Multi-band)\uff0c\u5982\u5207\u6210\u4e09\u584a\u7684\u8a71\u5373\u662f\u9ad8\u983b\u3001\u4e2d\u983b\u3001\u4f4e\u983b\u3002\u63a5\u8457\u9078\u51fa\u5404\u983b\u6bb5\u7684\u4e2d\u5fc3\u983b\u7387 (Center Frequency)\u53ca\u983b\u5bec(bandwidth)\u5f8c\u5373\u5b8c\u6210\u8a2d\u8a08\u3002\u672c\u7bc7\u8ad6\u6587\u5728\u983b\u7387\u53c3\u6578\u65b9\u9762\u4e00\u6a23 \u63a1\u7528\u591a\u983b\u6bb5\u7b49\u5316\u65b9\u5f0f(Multi-band Equalization)\u4f86\u6a21\u64ec\u5be6\u969b\u7b49\u5316\u5668\u7684\u64cd\u4f5c\uff0c\u5373\u662f\u628a\u983b \u7387\u53c3\u6578\u4f30\u8a08\u7684\u554f\u984c\u5207\u6210\u4e86\u591a\u500b\u8072\u97f3\u53c3\u6578\u7684\u5b50\u554f\u984c\u3002\u5982\u4e0b\u5716 6 \u6240\u793a\u3002 \u5716 8 KDE \u793a\u610f\u5716 X Y* Y Y R k Y* R m . . . . . . . . . . . . . . . . ! (3)BACK to ORIGINAL S P ACE (1)P CA (2)Le a rning the Ma p \u5b89\u975c\u7684\u6a23\u672c(\u5373\u662f\u8aaa\u5728\u8a72\u6a23\u672c\u7684\u7576\u6642\u6709\u8f03\u591a\u8ecc\u97f3\u91cf\u662f\u8da8\u8fd1\u65bc 0)\u6642\u6703\u5c0e\u81f4\u5747\u65b9\u8aa4\u5dee\u503c\u98c6\u9ad8\uff0c \u5f62\u6210 Outlier\uff0c\u5982\u6416\u6efe\u985e\u4e2d\u7684\u7b2c 17 \u9996\u7684\u7d50\u679c\u3002 \u8868 3 \u526f\u6b4c\u97f3\u91cf\u6a21\u578b\u4ea4\u53c9\u9a57\u8b49\u7d50\u679c Rock/Metal POP Jazz/Country Alt Rock/Funk song1 0.137 0.043 0.145 0.027 song2 0.148 0.224 0.005 0.087 song3 0.192 0.057 0.018 0.040 song4 0.172 0.030 0.018 0.053 song5 0.205 0.056 0.138 0.038 song6 0.035 0.070 0.037 0.426 song7 0.166 0.067 0.017 0.018 song8 0.135 0.055 song9 0.052 song10 0.120 song11 0.199 song12 0.040 \u5716 10 \u97f3\u8ecc\u6bd4\u8f03\u5716 \u5716 11 \u4e0d\u540c m \u503c\u6bd4\u8f03 \u63a5\u8457\u6211\u5011\u5be6\u4f5c\u4e86\u76f8\u95dc\u7814\u7a76\u4e2d[3]\u6240\u4f7f\u7528\u7684\u591a\u7dda\u6027\u8ff4\u6b78(\u7121\u8003\u616e\u4f9d\u8cf4\u6027)\u8207\u672c\u7bc7\u8ad6\u6587\u7684 KDE(\u8003\u616e\u4f9d\u8cf4\u6027)\u7684\u65b9\u6cd5\u505a\u6bd4\u8f03\uff0c\u7d50\u679c\u5982\u4e0b\u5716 12\uff0c\u53ef\u767c\u73fe\u5728\u5927\u591a\u6578\u7684\u8ecc\u4e0a KDE \u6bd4\u591a\u7dda sub-band)\u5716 7 \u6df7\u97f3\u53c3\u6578\u9810\u6e2c\u793a\u610f\u5716 \u7528\u65bc\u672c\u7bc7\u8ad6\u6587\u7684\u4e3b\u984c\u4f86\u8aaa\uff0c\u6211\u5011\u7684\uff38\u5c31\u662f\u6211\u5011\u6bcf\u9996\u5206\u8ecc\u7684\u8072\u97f3\u7279\u5fb5\u5411\u91cf\uff0cY \u5c31\u662f\u6211 song13 0.092 song14 0.048 \u5728\u983b\u7387(Equalization)\u6a21\u578b\u65b9\u9762\u6211\u5011\u540c\u6a23\u4e5f\u4f5c\u4e86\u4ea4\u53c9\u9a57\u8b49\uff0c\u7531\u8868\u4e2d\u53ef\u77e5\u5927\u81f4\u4e0a\u5404\u5206\u8ecc \u6027\u8ff4\u6b78\u6709\u8f03\u597d\u7684\u8868\u73fe\u3002\u986f\u793a\u5176\u4f9d\u8cf4\u6027\u4f30\u8a08\u65b9\u5f0f\u8f03\u80fd\u8003\u91cf\u5404\u8ecc\u4e4b\u9593\u7684\u5e73\u8861\u3002 \u5716 13 \u985e\u5225\u4ea4\u53c9\u9a57\u8b49 \u7b2c\u4e8c\u6b65\u9a5f\u662f\u6838\u4f9d\u8cf4\u4f30\u8a08(Kernel Dependency Estimation)\u7684\u6a21\u578b\u5efa\u7acb\uff0c\u6211\u5011\u6703\u5229\u7528\u6bcf \u9996\u6b4c\u7684\u6df7\u97f3\u53c3\u6578\u6b0a\u91cd\u53ca\u7279\u5fb5\u5411\u91cf\u4f86\u7576\u4f5c\u8a13\u7df4\u6838\u4f9d\u8cf4\u4f30\u8a08(Kernel Dependency Estimation) \u7684\u4f9d\u64da\uff0c\u8a13\u7df4\u597d\u7684\u6a21\u578b\u5c07\u6703\u7528\u4f86\u9810\u6e2c\u5404\u500b\u6df7\u97f3\u53c3\u6578\u7684\u6b0a\u91cd\u3002\u6700\u5f8c\u4f9d\u7167\u6a21\u578b\u9810\u6e2c\u51fa\u7684\u6b0a\u91cd \u9032\u884c\u6df7\u97f3\u3002 \u5728\u63a5\u4e0b\u4f86\u7684\u7ae0\u7bc0\u6211\u5011\u6703\u8a73\u7d30\u4ecb\u7d39\u5404\u6b65\u9a5f\u7684\u505a\u6cd5\uff0c\u5728\u7ae0\u7bc0 3.1 \u6703\u5148\u5c0d\u6211\u5011\u6240\u4f7f\u7528\u7684\u8cc7 \u6599\u96c6(Data Set)\u505a\u4ecb\u7d39\u4ee5\u53ca\u524d\u8655\u7406\u7684\u90e8\u5206\u3002\u7ae0\u7bc0 3.2 \u6703\u4ecb\u7d39\u6211\u5011\u5982\u4f55\u5229\u7528\u6700\u5c0f\u5e73\u65b9\u6cd5\u4f86\u4f30 \u8a08\u5404\u500b\u6df7\u97f3\u53c3\u6578\u53ca\u70ba\u4f55\u9700\u8981\u505a\u6df7\u97f3\u53c3\u6578\u4f30\u8a08\u3002\u7ae0\u7bc0 3.3 \u6211\u5011\u6703\u4ecb\u7d39\u6838\u4f9d\u8cf4\u4f30\u8a08(\u4e0b\u9762\u7c21\u7a31 Alt Rock / Blues / Country Rock / Indie / Funk / Reggae 7 Rock / Punk / Metal 17 Pop / Singer-Songwriter 10 Acoustic / Jazz / Country / Orchestral 8 Total 42 \u97f3\u53c3\u6578\u6703\u7528\u4e0d\u540c\u7684\u8072\u97f3\u7279\u5fb5\uff0c\u4f8b\u5982\u5728\u97f3\u91cf\u53c3\u6578\u65b9\u9762\u6703\u63a1\u7528\u8072\u97f3\u7684\u65b9\u5747\u6839\u4f86\u7576\u4f5c\u8861\u91cf\u7684\u4f9d \u64da\uff0c\u983b\u7387\u53c3\u6578\u65b9\u9762\u6703\u63a1\u7528\u8072\u97f3\u7684\u983b\u8b5c(Spectrum)\u7b49\u7b49\u3002\u5229\u7528\u6b64\u7dda\u6027\u7d44\u5408\u7684\u95dc\u4fc2\uff0c\u6211\u5011\u53ef \u4ee5\u5229\u7528\u6700\u5c0f\u5e73\u65b9\u6cd5(Least Square Method)\u4f86\u4f30\u8a08\u51fa\u6df7\u97f3\u53c3\u6578 \u03b1 \u7684\u6578\u503c\uff0c\u6700\u5c0f\u5e73\u65b9\u6cd5(Least Square Method)\u662f\u4ee5\u89c0\u6e2c\u503c U \u8207\u9810\u6e2c\u503c \u00db \u4e4b\u5dee\u7684\u5e73\u65b9\u548c\u4f5c\u70ba\u6700\u4f73\u5316\u7684\u76ee\u6a19\u51fd\u6578 (RMS)\uff0c\u6bcf\u500b\u97f3\u6846\u9577\u5ea6\u7d04\u70ba 20 \u6beb\u79d2\uff0c\u5247(1)\u5373\u53ef\u8868\u793a\u70ba \u5716 6 \u591a\u983b\u6bb5\u983b\u8b5c \u5728\u9032\u884c\u983b\u7387\u53c3\u6578\u4f30\u8a08\u6642\uff0c\u6211\u5011\u6703\u5148\u7528\u5feb\u901f\u5085\u7acb\u8449\u8f49\u63db(Fast Fourier Transform)\u5148\u5f97\u5230 \u5404\u97f3\u8ecc\u7684\u983b\u8b5c(Spectrum)\uff0c\u63a5\u8457\u628a\u5404\u97f3\u8ecc\u7684\u983b\u8b5c\u4f9d\u7167\u6211\u5011\u9810\u5148\u5206\u6bb5\u7684\u983b\u7387\u5206\u5225\u53bb\u89e3 \u8ff4\u6b78\u554f\u984c(Regression Problem)\uff0c\u4ee5\u4f30\u8a08\u51fa\u5404\u983b\u6bb5\u5728\u5404\u8ecc\u9593\u7684\u5e73\u8861\u53c3\u6578\u3002\u5982\u4e0b(5)\u5f0f \u9019\u500b\u554f\u984c\u57fa\u672c\u4e0a\u662f\u53ef\u4ee5\u5229\u7528\u5e38\u898b\u7684\u53c3\u6578\u9810\u6e2c(Parameter Prediction)\u7684\u65b9\u6cd5\u5206\u5225\u53bb\u6c42 \u89e3\uff0c\u4f8b\u5982\u591a\u7dda\u6027\u8ff4\u6b78(Multiple Linear Regression)\u3001\u52d5\u614b\u7dda\u6027\u7cfb\u7d71(Linear Dynamic System); \u4f46\u662f\u7531\u65bc\u5728\u6df7\u97f3\u6642\u6bcf\u4e00\u8ecc\u9593\u7684\u53c3\u6578\u9593\u662f\u6709\u4e92\u76f8\u5f71\u97ff\u7684\uff0c\u5176\u4e2d\u4e00\u8ecc\u7684\u97f3\u91cf\u8b8a\u5927\u5f8c\u52e2\u5fc5\u5176\u4ed6 \u8ecc\u6703\u8b8a\u5c0f\u8072\u4e9b\uff0c\u5f7c\u6b64\u662f\u4f9d\u8cf4\u95dc\u4fc2\u7684(dependent)\u3002\u82e5\u7528\u5404\u5225\u8a13\u7df4\u6a21\u578b\u4f86\u9810\u6e2c\u6211\u5011\u7684\u6df7\u97f3\u53c3 \u6578\u7684\u8a71\uff0c\u6211\u5011\u5c07\u6703\u5f97\u5230 k \u500b\u7368\u7acb\u7684\u8ff4\u6b78\u6a21\u578b\uff0c\u9019 \u5c0d\u65bc\u6df7\u97f3\u7d50\u679c\u53ef\u80fd\u6703\u7522\u751f\u8f03\u4e0d\u548c\u8ae7\u7684\u5f71 \u97ff\u3002\u6240\u4ee5\u672c\u7bc7\u8ad6\u6587\u63a1\u7528\u4e86\u6838\u4f9d\u8cf4\u4f30\u8a08\u7684\u65b9\u5f0f\u4f86\u89e3\u6c7a\u4e0a\u8ff0\u7684\u554f\u984c\uff0c\u7528\u6838\u4f9d\u8cf4\u4f30\u8a08\u8a13\u7df4\u51fa\u6211 \u5011\u6700\u5f8c\u7684\u6a21\u578b\u3002\u63a5\u4e0b\u4f86\u5c07\u6703\u5148\u4ecb\u7d39\u6838\u4f9d\u8cf4\u4f30\u8a08\u7684\u57fa\u672c\u7cbe\u795e\u3002 song15 0.269 song16 0.748 song17 2.175 Mean 0.290 0.075 0.054 0.099 \u5716 9 \u662f\u5176\u4e2d\u4e00\u7a2e\u97f3\u6a02\u985e\u578b\u4e2d\u526f\u6b4c(Chorus)\u4ea4\u53c9\u9a57\u8b49\u7684\u8a73\u7d30\u5716\u8868\uff0cX \u8ef8\u4ee3\u8868\u5176\u96a8\u6a5f\u62bd \u7684\u6e96\u78ba\u7387\u7d04\u70ba 0.015\uff0c\u986f\u793a\u5728\u983b\u7387\u53c3\u6578\u9810\u6e2c\u4e0a\u6709\u8457\u8f03\u597d\u7684\u8868\u73fe\u3002 \u8868 4 \u983b\u7387\u6a21\u578b\u4ea4\u53c9\u9a57\u8b49 Rock/Metal POP Jazz/Country Alt Rock/Funk Kick 0.011 0.019 0.003 0.008 Snare 0.043 0.015 0.011 0.018 Hihat 0.003 0.001 0.020 0.021 \u4e94\u3001 \u7d50\u8ad6\u8207\u672a\u4f86\u5de5\u4f5c \u5728\u97f3\u6a02\u88fd\u4f5c\u4e0a\u6df7\u97f3\u662f\u975e\u5e38\u91cd\u8981\u7684\u4e00\u500b\u904e\u7a0b\uff0c\u97f3\u6a02\u6210\u54c1\u54c1\u8cea\u597d\u58de\uff0c\u53d6\u6c7a\u65bc\u6df7\u97f3\u662f\u5426\u6df7 \u5f97\u597d\u800c\u4e14\u6df7\u97f3\u727d\u6d89\u5230\u8a31\u591a\u97f3\u97ff\u53ca\u8072\u5b78\u5fc3\u7406\u5b78\u7684\u76f8\u95dc\u6280\u8853\u8207\u77e5\u8b58\uff0c\u975e\u5c08\u696d\u4eba\u58eb\u8981\u6df7\u51fa\u5c1a\u53ef \u5011\u539f\u5148\u4f30\u8a08\u51fa\u4f86\u7684\u6df7\u97f3\u53c3\u6578 Y\u2208R \u6295\u5f71\u56de\u539f\u672c\u7684 k \u7dad\u7a7a\u9593\u4e0a\u5373\u6c42\u51fa\u5404\u8ecc\u6df7\u97f3\u53c3\u6578\u6b0a\u91cd\u3002 \u6a23\u6e2c\u8a66\u7684\u8072\u97f3\u6a23\u672c(Sound Sample)\uff0c Y \u8ef8\u70ba\u5176\u6240\u5c0d\u61c9\u5747\u65b9\u8aa4\u5dee\uff0c\u5716\u8868\u4e2d\u540c\u6a23\u984f\u8272\u7684\u7dda\u689d TOM 0.008 0.017 0.013 0.007 DRUMROOM 0.013 0.004 0.036 \u7684\u6210\u54c1\u6709\u4e00\u5b9a\u7684\u96e3\u5ea6\uff0c\u6240\u4ee5\u672c\u7bc7\u8ad6\u6587\u63d0\u51fa\u4e00\u500b\u5229\u7528\u76e3\u7763\u5f0f\u5b78\u7fd2\u4f86\u9032\u884c\u81ea\u52d5\u591a\u8ecc\u6df7\u97f3\u7684\u7cfb 0.001 \u4ee3\u8868\u540c\u4e00\u9996\u6b4c\u7684\u8072\u97f3\u6a23\u672c\u3002\u53ef\u4ee5\u767c\u73fe KDE \u6240\u8a13\u7df4\u51fa\u7684\u6a21\u578b\u5728\u5c0d\u540c\u4e00\u9996\u6b4c\u7684\u8072\u97f3\u6a23\u672c\u6642 OVERHEAD 0.017 0.028 0.003 0.046 \u7d71\u3002\u8207\u5176\u4ed6\u7bc7\u76f8\u95dc\u7814\u7a76\u4e0d\u540c\u7684\u662f\u5176\u4e3b\u8981\u7684\u6838\u5fc3\u65b9\u6cd5\u662f\u6838\u4f9d\u8cf4\u4f30\u8a08(Kernel Dependency KDE)\u7684\u6838\u5fc3\u6982\u5ff5\u4ee5\u53ca\u5728\u672c\u7bc7\u8ad6\u6587\u7684\u554f\u984c\u4e2d\u8a72\u5982\u4f55\u8a2d\u8a08\u3002 \u5716 4 \u7cfb\u7d71\u67b6\u69cb\u5716 \u5716 5 \u6df7\u97f3\u53c3\u6578\u6a21\u578b Rock Intro. Ve rse Chorus Jazz Intro. Ve rse Chorus Pop Intro. Ve rse Chorus \u8cc7\u6599\u96c6\u524d\u8655\u7406\u7684\u90e8\u5206\uff0c\u7531\u65bc\u6b64\u8cc7\u6599\u96c6\u6db5\u84cb\u7684\u97f3\u6a02\u985e\u578b\u5305\u62ec Alt Rock/Blues \u3001 3.3.1 \u6838\u4f9d\u8cf4\u4f30\u8a08(Kernel Dependency Estimation) \u6703\u6709\u4e00\u81f4\u6548\u679c\u3002 PERCUSSION 0.008 0.006 0.025 0.005 BASS 0.011 0.007 0.004 Estimation)\uff0c\u5229\u7528\u6df7\u97f3\u53c3\u6578\u9593\u7684\u4f9d\u8cf4\u6027(dependency)\uff0c\u4f86\u505a\u6df7\u97f3\u53c3\u6578\u7684\u9810\u6e2c; \u53e6\u4e00\u500b\u8207\u5176 0.017 \u56db\u3001 \u5be6\u9a57 EG 0.006 0.019 0.024 0.005 \u4ed6\u76f8\u95dc\u8ad6\u6587\u4e0d\u540c\u7684\u662f\u8a13\u7df4\u7684\u55ae\u4f4d\u4e0d\u540c\uff0c\u7531\u65bc\u6df7\u97f3\u5176\u5be6\u662f\u975e\u5e38\u500b\u5225\u7684(Case By Case)\uff0c\u6df7 Rock/Punk\u3001Pop/Singer-Songwriter\u3001Acoustic/Jazz/Country \u7b49\uff0c\u6bcf\u500b\u97f3\u6a02\u985e\u578b\u6240\u4f7f\u7528\u7684\u914d \u5668\u6703\u7565\u6709\u4e0d\u540c\uff0c\u4f8b\u5982\u5728\u7235\u58eb\u6a02\u4e2d\u7ba1\u6a02\u5668\u7684\u4f7f\u7528\u6bd4\u4f8b\u6703\u6bd4\u6416\u6efe\u6a02\u4f86\u7684\u9ad8\u3002\u9019\u6a23\u6703\u9020\u6210\u4e4b\u5f8c AG 0.000 0.004 0.015 0.006 LEADVOX 0.021 0.013 0.005 \u5716 12 KDE \u8207 MLR \u6bd4\u8f03\u5716 \u97f3\u5e2b\u57fa\u672c\u4e0a\u90fd\u6703\u4f9d\u7167\u5404\u97f3\u8ecc\u7684\u7684\u8072\u97ff\u3001\u97f3\u6a02\u985e\u578b\u4e0d\u540c\u800c\u6709\u4e0d\u540c\u7684\u8655\u7406\u65b9\u5f0f; \u6240\u4ee5\u5728\u672c\u7bc7 0.025 (3) (5) \u6838\u4f9d\u8cf4\u4f30\u8a08(\u4ee5\u4e0b\u7c21\u7a31 KDE)\u662f\u4e00\u7a2e\u7528\u65bc\u5c0b\u627e\u8f38\u5165 X \u8207\u8f38\u51fa Y \u9593\u4f9d\u8cf4\u95dc\u4fc2\u7684\u5b78\u7fd2 \u5be6\u9a57\u5206\u70ba\u4e09\u500b\u90e8\u5206\u4f5c\u8a0e\u8ad6\uff0c\u7b2c\u4e00\u90e8\u5206\u7684\u5be6\u9a57\u662f\u8a55\u4f30\u7531\u6838\u4f9d\u8cf4\u4f30\u8a08\u6240\u5efa\u7acb\u7684\u6a21\u578b\u7684\u6b63 BACKVOX 0.002 0.003 0.032 0.017 \u8ad6\u6587\u7684\u6a21\u578b\u5efa\u7acb\u7684\u904e\u7a0b\u6211\u5011\u6703\u4f9d\u7167\u4e0d\u540c\u7684\u97f3\u6a02\u985e\u578b\u4ee5\u53ca\u6a4b\u6bb5\u5efa\u7acb\u4e0d\u540c\u7684\u6a21\u578b\uff0c\u4ee5\u65b9\u4fbf\u6700 \u7121\u6cd5\u5c07\u6b64\u8cc7\u6599\u96c6\u5957\u7528\u81f3\u6211\u5011\u7684\u6a21\u578b\u4e2d\u3002\u6240\u4ee5\u70ba\u4e86\u89e3\u6c7a\u6b64\u554f\u984c\u6211\u5011\u7d71\u8a08\u4e86\u5404\u97f3\u6a02\u985e\u578b\u6240\u4f7f \u7528\u7684\u914d\u5668\uff0c\u6c7a\u5b9a\u51fa\u6bcf\u500b\u985e\u578b\u57fa\u672c\u97f3\u8ecc(Basic Track)\u5982\u8868 2\uff0c\u6211\u5011\u5b9a\u7fa9\u4e86 12 \u7a2e\u97f3\u8ecc\u7684\u5f62\u614b\u3002 \u524d\u8655\u7406\u7684\u90e8\u5206\u6703\u5148\u5c0d\u5206\u8ecc\u505a\u97f3\u6a02\u683c\u5f0f\u4e0a\u7684\u8f49\u63db\uff0c\u5f85\u6bcf\u9996\u6b4c\u7684\u6df7\u97f3\u53c3\u6578\u6b0a\u91cd\u4f30\u8a08\u51fa\u4f86\u5f8c\u6703 \u5728\u6df7\u97f3\u6642\u7531\u65bc\u6df7\u97f3\u7684\u53c3\u6578\u773e\u591a\uff0c\u672c\u7bc7\u8ad6\u6587\u53ea\u8a0e\u8ad6\u4e86\u5176\u4e2d\u4e09\u500b\u6700\u91cd\u8981\u53c3\u6578\uff1aVolume\u3001 Frequency\u3001Pan\uff0c\u5728\u4f30\u8a08\u4e0d\u540c\u7684\u53c3\u6578\u6642\u6211\u5011\u6703\u4f7f\u7528\u4e0d\u540c\u7684\u8072\u97f3\u7279\u5fb5\u3002\u63a5\u4e0b\u4f86\u6703\u5c0d\u5404\u53c3\u6578 \u6240\u7528\u7684\u7279\u5fb5\u4f86\u505a\u4ecb\u7d39 \u6a02\u5668\u64fa\u4f4d(PANNING) \u6a02\u5668\u64fa\u4f4d\u5373\u662f\u6c7a\u5b9a\u8a72\u97f3\u8ecc\u5728\u5de6\u8072\u9053\u53ca\u53f3\u8072\u9053\u4e4b\u97f3\u91cf\u6bd4\u4f8b\uff0c\u5982\u5f0f(1)\u3002\u5728\u672c\u7bc7\u8ad6\u6587 \u67b6\u69cb\uff0cKDE \u7684\u6d41\u7a0b\u5982\u4e0b\u5716 8 \u6240\u793a\uff0c\u6b65\u9a5f\u4e3b\u8981\u5206\u70ba\u4e09\u500b\u6b65\u9a5f\uff1a 1. \u78ba\u6027; \u7b2c\u4e8c\u90e8\u5206\u662f\u8a55\u4f30 KDE \u4f9d\u8cf4\u6027(Dependency)\u7684\u6548\u679c\uff0c\u6bd4\u8f03\u4e0d\u540c\u7684 m \u503c\u5c0d\u6a21\u578b\u6b63\u78ba \u6027\u7684\u5f71\u97ff\u7a0b\u5ea6; \u7b2c\u4e09\u90e8\u5206\u662f\u8a55\u4f30\u4e0d\u540c\u7684\u97f3\u6a02\u985e\u578b\u6df7\u97f3\u65b9\u5f0f\u7684\u5dee\u7570\u6027\u3002\u5be6\u9a57\u8a55\u4f30\u7684\u65b9\u5f0f\u662f Mean 0.012 0.011 0.016 0.015 \u5f8c\u5be6\u969b\u4e0a\u7684\u5229\u7528\u3002\u7531\u5be6\u9a57\u7d50\u679c\u5f97\u53ef\u77e5\uff0c\u4e0d\u540c\u7684\u97f3\u6a02\u985e\u578b\u5176\u6df7\u97f3\u65b9\u5f0f\u662f\u6709\u5176\u7368\u7279\u6027\u7684\u3002 4.3 \u8de8\u985e\u578b\u6e2c\u8a66(Cross Genre Testing) \u672a\u4f86\u5de5\u4f5c\u65b9\u9762\uff0c\u7531\u65bc\u672c\u7bc7\u8ad6\u6587\u6709\u5206\u6210\u56db\u5927\u97f3\u6a02\u985e\u578b\u53bb\u505a\u6a21\u578b\u5efa\u7acb\uff0c\u5c0e\u81f4\u5404\u985e\u5225\u8a13\u7df4 1. \u5229\u7528\u4e00\u6b21\u6311\u4e00\u500b\u4ea4\u53c9\u9a57\u8b49\u5c0d\u540c\u4e00\u97f3\u6a02\u985e\u578b\u7684\u6b4c\u505a\u5747\u65b9\u8aa4\u5dee(Mean Square Error)\u8a55\u4f30\u3002\u5747 4.2 KDE \u65b9\u6cd5\u7684\u6548\u679c(Effect of KDE Method) \u5728\u7b2c\u4e00\u90e8\u5206\u7684\u5be6\u9a57\u6a21\u578b\u7684\u5efa\u7acb\u4ee5\u53ca\u6e2c\u8a66\u90fd\u4fb7\u9650\u5728\u540c\u4e00\u500b\u97f3\u6a02\u985e\u578b\u4e2d\uff0c\u5728\u7b2c\u4e09\u90e8\u5206\u7684 \u8cc7\u6599\u6709\u504f\u5c11\u7684\u50be\u5411\uff0c\u672a\u4f86\u5e0c\u671b\u5408\u4f75\u5176\u4ed6\u8cc7\u6599\u96c6\u4ee5\u88dc\u8db3\u5176\u6578\u91cf;\u518d\u8005\u7531\u65bc\u6df7\u97f3\u53c3\u6578\u662f\u975e\u5e38 \u505a\u57fa\u672c\u8ecc\u7684\u5408\u4f75(Track Merge)\uff0c\u5c07\u540c\u4e00\u985e\u578b\u7684\u97f3\u8ecc\u4f9d\u7167\u5176\u6b0a\u91cd\u5148\u5408\u4f75\u6210\u8a72\u985e\u578b\u57fa\u672c\u97f3\u8ecc\uff0c \u5c07\u540c\u4e00\u985e\u578b\u7684\u6a94\u6848\u5148\u5408\u4f75\u6210\u4e00\u500b;\u4f8b\u5982\u5728\u67d0\u4e00\u9996\u6b4c\u4e2d\u5409\u4ed6\u9304\u4e86\u5169\u628a\uff0c\u6211\u5011\u6703\u5148\u5c07\u9019\u5169\u628a \u7684\u9304\u97f3\u5408\u4f75\u6210\u4e00\u500b\uff0c\u4ee5\u65b9\u4fbf\u4e4b\u5f8c\u8a13\u7df4\u53ca\u6e2c\u8a66\u8a72\u985e\u578b\u7684\u97f3\u6a02\u3002 \u8868 2 \u57fa\u672c\u8ecc\u8868 12 Basic Track Type (1)Kick (2)Snare (3)Hihat (4)TOM (5)DrumRoom (6)OVERHEAD (7)PERCUSSION (8)BASS (9)Electric Guitar (10)Acoustic Guitar (11)LeadVox. (12)BackVox \u6211\u5011\u5c07\u64fa\u4f4d\u53c3\u6578\u7684\u554f\u984c\u8f49\u5316\u6210\u524d\u4e00\u7ae0\u7bc0\u97f3\u91cf\u53c3\u6578\u4f30\u8a08\u7684\u554f\u984c\uff0c\u5373\u5de6\u8072\u9053\u505a\u4e00\u6b21\u97f3\u91cf \u65b9\u8aa4\u5dee\u7684\u8a08\u7b97\u65b9\u5f0f\u5982\u4e0b\uff1a \u5be6\u9a57\uff0c\u6211\u5011\u60f3\u8981\u8a55\u4f30\u4e0d\u540c\u7684\u97f3\u6a02\u98a8\u683c\u7684\u97f3\u6a02\u662f\u5426\u6709\u5176\u6df7\u97f3\u7279\u8272\uff0c\u6211\u5011\u5c07\u6703\u5957\u7528\u7531\u4e0d\u540c\u985e \u96e3\u53d6\u5f97\u7684\uff0c\u672c\u7bc7\u8ad6\u6587\u662f\u63a1\u7528\u4f30\u8a08\u7684\u65b9\u5f0f\u4f30\u8a08\u5176\u8cc7\u6599\u96c6\u4e2d\u7684\u6df7\u97f3\u53c3\u6578\u6b0a\u91cd\uff0c\u672a\u4f86\u53ef\u4ee5\u6539\u7528 \u97f3\u91cf (VOLUME) \u97f3\u91cf\u53c3\u6578\u4e5f\u88ab\u7a31\u4f5c\u589e\u76ca(Gain)\uff0c\u4e3b\u8981\u5728\u63a7\u5236\u6bcf\u500b\u97f3\u8ecc\u9593\u7684\u97f3\u91cf\u4f7f\u5f97\u6574\u9ad4\u97f3\u91cf\u9054 \u6210\u5e73\u8861\u3002\u672c\u7bc7\u8ad6\u6587\u4f7f\u7528\u4e86\u65b9\u5747\u6839(Root Mean Square)\u7684\u65b9\u5f0f\u53bb\u6e2c\u91cf\u540c\u4e00\u500b\u97f3\u6846(Frame) \u4e2d\u5404\u97f3\u8ecc\u7684\u8072\u58d3(Sound Pressure Level)\uff0c\u4ee5\u6b64\u4f5c\u70ba\u97f3\u91cf\u53c3\u6578\u7684\u7279\u5fb5\u5411\u91cf\uff0c\u5beb\u6210\u77e9\u9663 \u5f62\u5f0f\u5982(4)\u5f0f\u3002G k \u70ba\u7b2c k \u8ecc\u7684\u6b0a\u91cd\uff0cN \u70ba\u97f3\u6846\u7684\u9577\u5ea6\u3002 \u53c3\u6578\u4f30\u8a08\uff0c\u53f3\u8072\u9053\u505a\u4e00\u6b21\u97f3\u91cf\u53c3\u6578\u4f30\u8a08\uff0c\u9019\u6a23\u5373\u53ef\u6c7a\u5b9a\u8a72\u97f3\u8ecc\u5728\u5de6\u53f3\u8072\u9053\u4e4b\u6bd4\u4f8b\uff0c \u5982\u4e0b\u5f0f(6)\u3001(7)\u6240\u793a\u3002 \u03b1 1 u L1 +\u03b1 2 u L2 +\u2026..+\u03b1 k u Lk =V (6) \u03b1 1 u R1 +\u03b1 2 u R2 +\u2026..+\u03b1 k u RK =V (7) 3.3 \u6838\u4f9d\u8cf4\u4f30\u8a08\u6a21\u578b\u5efa\u7acb(Kernel Dependency Estimation Model) \u4f30\u8a08\u5b8c\u8cc7\u6599\u96c6\u4e2d\u6bcf\u9996\u6b4c\u7684\u4e09\u7a2e\u6df7\u97f3\u53c3\u6578(Volume\u3001EQ\u3001Panning)\u5f8c\uff0c\u6211\u5011\u6709\u4e86\u6bcf\u9996 \u6b4c\u7684\u7279\u5fb5\u5411\u91cf(X)\uff0c\u53ca\u6bcf\u9996\u6b4c\u7684\u6df7\u97f3\u53c3\u6578(Y)\uff0c\u5373\u53ef\u85c9\u7531\u5b78\u7fd2\u51fa X, Y \u9593\u7684\u95dc\u4fc2\u5efa\u7acb\u6211\u5011 \u5c0d\u8f38\u51fa Y \u505a\u964d\u7dad\u7684\u52d5\u4f5c\uff0c\u6295\u5f71\u81f3\u8f03\u4f4e\u7684\u7dad\u5ea6\u4e0b\u5f62\u6210 Y'\u3002 2. \u5b78\u7fd2(Learning The Map)\uff1a\u5c0d\u6bcf\u500b\u57fa\u790e\u539f\u4ef6(Principal Component)j\uff0c1\u2264j\u2264m \uff0c\u6211\u5011\u6703 \u5b78\u7fd2\u4e00\u500b\u5c0d\u61c9\u51fd\u6578(Mapping Function)r j (X)\uff0c\u5c0d\u61c9\u51fd\u6578 r \u6703\u5c07\u6703\u628a X \u5c0d\u61c9\u81f3 Y'\u7b2c j \u500b \u57fa\u790e\u539f\u4ef6\uff0c\u5373\u662f\u5728\u8f03\u4f4e\u7684\u7a7a\u9593\u4e0a\u53bb\u89e3 m \u500b\u8ff4\u6b78\u554f\u984c\u3002 3. \u9810\u6e2c(Prediction)\uff1a\u5c0d\u65bc\u4e00\u500b\u65b0\u7684\u8f38\u5165 x'\uff0c\u6211\u5011\u53ef\u4ee5\u5229\u7528\u5148\u524d\u5b78\u7fd2\u597d\u7684\u5c0d\u61c9\u51fd\u6578\u6c42\u51fa y'' (Mean Square Error)\uff0c\u6e2c\u8a66\u6642\u6703\u5148\u5c0d\u6e2c\u8a66\u6b4c\u66f2\u505a\u96a8\u6a5f\u62bd\u6a23 3000 \u500b\u6a23\u672c\u4f86\u505a\u6e2c\u8a66\uff0cm \u503c\u7686 \u97f3(Multi-track Recording)\u6642\uff0c\u55ae\u4e00\u8ecc\u4e5f\u6703\u53c3\u96dc\u8457\u5176\u4ed6\u8ecc\u7684\u8072\u97f3(\u4e32\u97f3)\uff0c\u5982 Overhead\u3001 \u97f3\u65b9\u5f0f\uff0c\u6240\u8a13\u7df4\u51fa\u4f86\u7684\u6a21\u578b\u6709\u5176\u7368\u7279\u6027\u3002 = [ r 1 (x') r 2 (x')\u2026..r m (x')]\uff0cy'' \u2208R m \u3002\u6700\u5f8c\u518d\u5c07 Y''\u6295\u5f71\u56de\u539f\u672c\u7684\u7a7a\u9593\u4e0a\uff0c\u5373\u6c42\u51fa X'\u6240 = 1 \u2211 (\u03b1 \u2212 \u03b1 ) 2 =1 \u578b\u6240\u8a13\u7df4\u51fa\u7684\u6a21\u578b\u4f86\u770b\u770b\u5176\u6548\u679c\u662f\u5426\u6709\u5dee\u5225\u3002\u7d50\u679c\u5982\u4e0b\u5716 13\uff0cX \u8ef8\u5206\u5225\u662f\u5148\u524d\u5b9a\u7fa9\u7684\u57fa \u76f8\u95dc\u7814\u7a76\u7684\u4f30\u8a08\u65b9\u5f0f\u6216\u662f\u5be6\u969b\u6536\u96c6\u6df7\u97f3\u53c3\u6578\u4ee5\u8b93\u6700\u5f8c\u7684\u6df7\u97f3\u6a21\u578b\u80fd\u66f4\u8cbc\u8fd1\u5be6\u969b\u4e0a\u7684\u60c5 (8) \u03b1\u4ee3\u8868\u7531 KDE \u6a21\u578b\u6240\u9810\u6e2c\u7684\u6df7\u97f3\u53c3\u6578\u6b0a\u91cd\uff0c\u03b1 \u662f\u7531\u53c3\u6578\u4f30\u8a08\u5f97\u51fa\u5be6\u969b\u7684\u6df7\u97f3\u53c3\u6578\u6b0a\u91cd\u503c\u3002 4.1 \u3127\u6b21\u4e00\u500b\u4ea4\u53c9\u6e2c\u8a66(Leave-one-out Cross Validation) \u8868 3 \u70ba\u6bcf\u500b\u97f3\u6a02\u985e\u578b\u526f\u6b4c\u97f3\u91cf\u53c3\u6578\u7684\u4e00\u6b21\u4e00\u500b\u4ea4\u53c9\u9a57\u8b49 (Leave-one-out Cross Validation)\u7684\u7d50\u679c\uff0c\u5176\u503c\u70ba KDE \u6a21\u578b\u6240\u9810\u6e2c\u7684\u503c\u8207\u5be6\u969b\u6df7\u97f3\u53c3\u6578\u6b0a\u91cd\u7684\u5747\u65b9\u8aa4\u5dee\u503c \u5716 9 POP/Sing-Song writer \u5728\u53e6\u4e00\u65b9\u9762\u7531\u97f3\u8ecc\u7684\u89c0\u9ede\u4f86\u770b\uff0c\u5982\u5716 10\uff0c\u6211\u5011\u53ef\u4ee5\u767c\u73fe\u5728 Kick\u3001DrumRoom\u3001 Overhead \u7b49\u97f3\u8ecc\u4e0a\u5404\u985e\u578b\u6703\u6709\u8f03\u5927\u7684\u8aa4\u5dee\u51fa\u73fe\uff0c\u5176\u539f\u56e0\u8981\u6b78\u548e\u65bc\u5728\u5be6\u969b\u4e0a\u591a\u8ecc\u540c\u6b65\u9304 \u5c0d\u65bc\u6df7\u97f3\u53c3\u6578\u9810\u6e2c\u7684\u5e6b\u52a9\u3002 \u6cc1\uff0c\u4f8b\u5982\u5efa\u69cb\u4e00\u500b\u7dda\u4e0a\u6df7\u97f3\u7cfb\u7d71\u7684\u65b9\u5f0f\u8b93\u4f7f\u7528\u8005\u5be6\u969b\u6df7\u97f3\u8a18\u9304\u5176\u6df7\u97f3\u53c3\u6578\u3002\u53e6\u5916\u5728 KDE \u65b9\u6cd5\u7684\u90e8\u5206\u7531\u65bc\u672c\u7bc7\u672a\u5957\u7528\u5176\u6838\u51fd\u6578(Kernel function)\u7684\u90e8\u5206\uff0c\u672a\u4f86\u53ef\u4ee5\u5728\u505a\u6295\u5f71\u524d\u5148\u5957 \u7528\u6838\u51fd\u6578\u4f86\u63d0\u5347\u6df7\u97f3\u6a21\u578b\u7684\u6548\u679c\u3002\u5be6\u9a57\u90e8\u5206\u8a55\u4f30\u7684\u5c0d\u8c61\u4e5f\u53ef\u4ee5\u65b0\u589e\u8ddf\u8cc7\u6599\u96c6\u7684\u6df7\u97f3\u6210\u54c1 \u505a\u6bd4\u8f03\u8a66\u8457\u770b\u770b\u76ee\u524d\u7684\u6df7\u97f3\u6a21\u578b\u8207\u5be6\u969b\u4e0a\u7684\u5dee\u8ddd\u5982\u4f55\u3002 \u672c\u8ecc\uff0cY \u8ef8\u662f\u5176\u5c0d\u61c9\u7684\u5747\u65b9\u8aa4\u5dee\u503c\uff0c\u7576\u4e2d\u6e2c\u8a66\u8cc7\u6599\u70ba \u986f\u793a\u4e0d\u592a\u9069\u5408\u3002\u6211\u5011\u4e5f\u767c\u73fe\u97f3\u6a02\u985e\u578b\u76f8\u4f3c\u7684\u6b4c\u5176\u6df7\u97f3\u65b9\u5f0f\u6703\u8f03\u76f8\u8fd1\u5982\u5716\u4e2d ROCK \u8207 Alt ROCK \u7684\u5747\u65b9\u8aa4\u5dee\u503c\u8f03\u70ba\u63a5\u8fd1\u3002\u7d93\u7531\u6b64\u5be6\u9a57\u6211\u5011\u53ef\u4ee5\u5f97\u77e5\u4e0d\u540c\u7684\u97f3\u6a02\u985e\u578b\u6709\u5176\u4e0d\u540c\u7684\u6df7 \u53c3\u8003\u6587\u737b</td></tr><tr><td>\u800c\u975e\u5168\u90e8\u7684\u6a02\u5668\u90fd\u64e0\u5728\u4e00\u8d77\u3002\u5728\u5be6\u4f5c\u4e0a\u5373\u662f\u5206\u5225\u8abf\u6574\u5de6\u8072\u9053\u8207\u53f3\u8072\u9053\u7684\u97f3\u91cf\uff0c\u5982\u4e0b(1) \u5f0f \u97f3\u3002 \u5efa\u7acb\u4e00\u500b\u6a21\u578b\uff0c\u85c9\u7531\u6bcf\u4e00\u8ecc\u7684\u7279\u5fb5\u4f86\u9810\u6e2c\u5176\u53c3\u6578\u7684\u503c\u3002\u6240\u4ee5\u8a13\u7df4\u7684\u904e\u7a0b\u4e2d\u52e2\u5fc5\u9700\u8981\u539f\u59cb \u6700\u5f8c\u7684\u6df7\u97f3\u6a21\u578b\u3002\u5728\u6a21\u578b\u5efa\u7acb\u7684\u90e8\u5206\u6211\u5011\u6703\u4f9d\u7167\u4e0d\u540c\u7684\u97f3\u6a02\u985e\u578b\uff0c\u4e0d\u540c\u7684\u97f3\u6a02\u6a4b\u6bb5\u5efa\u7acb \u5c0d\u61c9\u7684\uff39\u503c\u3002 \u70ba 8\u3002\u7531\u8868\u53ef\u77e5\u7531 KDE \u6240\u8a13\u7df4\u51fa\u7684\u6a21\u578b\u5747\u65b9\u8aa4\u5dee\u5e73\u5747\u5927\u7d04\u843d\u5728 0.129 \u5de6\u53f3\u7684\u6578\u503c\uff0c\u9810\u6e2c DrumRoom \u7b49\u8ecc\u6703\u5305\u542b kick\u3001snare \u7b49\u5176\u4ed6\u9f13\u7d44\u7684\u6a02\u5668\u3002\u6b64\u539f\u56e0\u9055\u53cd\u4e86\u7576\u521d\u7814\u7a76\u65b9\u6cd5\u4e00 (4) \u4e00\u7d44\u6a21\u578b\uff0c\u5176\u4e2d\u4e00\u7d44\u6a21\u578b\u5305\u62ec\u4e86\u97f3\u91cf\u6a21\u578b\u5169\u500b(\u5de6\u8072\u9053\u3001\u53f3\u8072\u9053)\uff0c\u983b\u7387\u6a21\u578b\u4e94\u500b(5 \u51fa\u7684\u6b0a\u91cd\u6709\u8457\u9084\u4e0d\u932f\u7684\u6e96\u78ba\u5ea6\uff0c\u4f46\u7531\u65bc\u662f\u63a1\u7528\u96a8\u6a5f\u62bd\u6a23\u7684\u65b9\u5f0f\uff0c\u82e5\u62bd\u5230\u7684\u8072\u97f3\u6a23\u672c\u70ba\u8f03 \u958b\u59cb\u7684\u5047\u8a2d: \u6211\u5011\u5047\u8a2d\u5206\u8ecc\u5373\u6df7\u97f3\u6210\u54c1\u9593\u662f\u500b\u7dda\u6027\u7d44\u5408\u7684\u95dc\u4fc2\uff0c\u5206\u8ecc\u9593\u5fc5\u9808\u8981\u662f\u7dda\u6027\u7368</td></tr></table>",
                "text": "\u3002\u5229\u7528\u6b64\u6a21\u578b\u5efa\u7acb\u7684\u65b9\u6cd5\u8b93\u6700\u5f8c\u7684\u6210\u54c1\u66f4\u80fd\u61c9\u7528\u5728\u5be6\u969b\u7684\u6b4c\u66f2\u4e2d\uff0c\u8a13\u7df4\u6a21\u578b\u7684 \u6a23\u672c\u6578\u6211\u5011\u81ea\u5404\u8a13\u7df4\u6b4c\u66f2\u4e2d\u96a8\u6a5f\u62bd\u53d6 3000 \u500b\u6a23\u672c\u4f86\u7576\u4f5c\u6211\u5011\u7684\u8a13\u7df4\u8cc7\u6599\u3002\u5982\u5716 7\uff0cu nk \u70ba\u8a72\u6a21\u578b\u7684\u7b2c n \u500b\u6a23\u672c\u7684\u7b2c k \u8ecc\u7684\u8072\u97f3\u7279\u5fb5\u503c\uff0c\u03b1 nk \u70ba\u7b2c n \u500b\u6a23\u672c\u4e4b\u7b2c\uff2b\u8ecc\u7684\u6df7\u97f3\u53c3\u6578 \u6b0a\u91cd\u503c;K \u70ba\u5206\u8ecc\u7684\u500b\u6578\uff0c\u5728\u672c\u7bc7\u8ad6\u6587\u70ba 12\u3002 \u7b2c\u4e8c\u90e8\u5206\u7684\u5be6\u9a57\u662f\u8981\u4f86\u8a55\u4f30 KDE \u5373\u5176\u4f9d\u8cf4\u6027\u7684\u6210\u6548\uff0c\u9996\u5148\u6211\u5011\u8a0e\u8ad6\u4e86\u4e0d\u540c\u7684 m \u503c \u6240\u5e36\u4f86\u7684\u5f71\u97ff\uff0c\u7d50\u679c\u5982\u4e0b\u5716 11\uff0c\u70ba ROCK \u5728\u526f\u6b4c\u6642\u4e0d\u540c m \u503c\u7684\u5747\u65b9\u5dee\uff0cX \u8ef8\u70ba\u4e0d\u540c\u7684 m \u503c\uff0cY \u70ba\u6240\u5c0d\u61c9\u5176\u6df7\u97f3\u3002\u6211\u5011\u53ef\u4ee5\u767c\u73fe\u7576 m \u503c\u8d8a\u4f4e KDE \u6a21\u578b\u6703\u6709\u8f03\u597d\u7684\u7d50\u679c\uff0c\u8f03\u4f4e \u7684 m \u503c\u4e5f\u53ef\u52a0\u901f KDE \u7684\u8a08\u7b97(eg.PCA space \u5f9e R 12 \u964d\u4f4e\u70ba R 8 )\u3002\u9019\u7d50\u679c\u4e5f\u986f\u793a\u4e86\u4f9d\u8cf4\u6027 ROCK \u9019\u4e00\u985e\u7684\u6b4c\uff0c\u4e00\u5171\u6709 17 \u9996\uff0c \u5716\u4e2d\u7684\u7dda\u689d\u70ba\u5206\u5225\u7528 POP\u3001jazz\u3001Alt Rock \u6240\u8a13\u7df4\u51fa\u7684\u6a21\u578b\u5957\u7528\u81f3 ROCK \u985e\u5225\u7684\u6e2c\u8a66\u7d50 \u679c\u3002\u6211\u5011\u53ef\u4ee5\u767c\u73fe\u5c07\u5225\u7684\u985e\u5225\u7684\u6a21\u578b\u5957\u7528\u5728\u4e0d\u540c\u985e\u578b\u7684\u6b4c\u6642\u6703\u5c0e\u81f4\u6a21\u578b\u7684\u6b63\u78ba\u5ea6\u4e0b\u964d\u3001 \u8aa4\u5dee\u589e\u5927\uff0c\u5982\u5716\u4e2d\u7684 POP \u8207 JAZZ \u7684\u7d50\u679c\uff0c\u5169\u985e\u5225\u7684\u6a21\u578b\u5957\u7528\u5728 ROCK \u97f3\u6a02\u4e0a\u5176\u7d50\u679c"
            }
        }
    }
}