WEKO3
アイテム
{"_buckets": {"deposit": "1e6c0437-3673-440b-be48-7503846fdea8"}, "_deposit": {"created_by": 15, "id": "10365", "owners": [15], "pid": {"revision_id": 0, "type": "depid", "value": "10365"}, "status": "published"}, "_oai": {"id": "oai:sucra.repo.nii.ac.jp:00010365", "sets": ["506"]}, "author_link": [], "item_113_alternative_title_1": {"attribute_name": "タイトル(別言語)", "attribute_value_mlt": [{"subitem_alternative_title": "音声のピッチ同期線形予測分析のための改善法"}]}, "item_113_biblio_info_9": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2015", "bibliographicIssueDateType": "Issued"}}]}, "item_113_date_35": {"attribute_name": "作成日", "attribute_value_mlt": [{"subitem_date_issued_datetime": "2015-12-16", "subitem_date_issued_type": "Created"}]}, "item_113_date_granted_20": {"attribute_name": "学位授与年月日", "attribute_value_mlt": [{"subitem_dategranted": "2015-03-24"}]}, "item_113_degree_grantor_22": {"attribute_name": "学位授与機関", "attribute_value_mlt": [{"subitem_degreegrantor": [{"subitem_degreegrantor_name": "埼玉大学"}], "subitem_degreegrantor_identifier": [{"subitem_degreegrantor_identifier_name": "12401", "subitem_degreegrantor_identifier_scheme": "kakenhi"}]}]}, "item_113_degree_name_21": {"attribute_name": "学位名", "attribute_value_mlt": [{"subitem_degreename": "博士(工学)"}]}, "item_113_description_13": {"attribute_name": "形態", "attribute_value_mlt": [{"subitem_description": "103 p.", "subitem_description_type": "Other"}]}, "item_113_description_23": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Linear prediction (LP) analysis has been applied to speech system over the last few decades. LP technique is well-suited for speech analysis due to its ability to model speech production process approximately. Hence LP analysis has been widely used for speech enhancement, low-bit-rate speech coding in cellular telephony, speech recognition, characteristic parameter extraction (vocal tract resonances frequencies, fundamental frequency called pitch) and so on. However, the performance of the conventional LP method is degraded by high-pitched harmonic structure of glottal excitation source and background noise. In order to improve the performance of LP analysis, it is necessary to reduce the effect of these two factors, which is a most challenging task for LP analysis.\nThe objective of this dissertation is to develop some approaches to improve the performance of the LP analysis based on pitch synchronous analysis. We consider a pitch synchronous LP analysis for high-pitched speech using a weighted short time energy (STE) function for the purpose of downgrading the effect of the harmonic structure of the glottal excitation source. Unlike some conventional techniques, which require the electroglottography (EGG) signal or complicated epoch extraction algorithms, we utilize a simple STE computation of speech signal and prediction residual signal to extract the interval of glottal closed phase during a glottal cycle and do not need to estimate the instant of glottal closure and opening exactly.\nTo reduce the infuence of the background noise, we propose a noise compensation LP method based on pitch synchronous analysis under white noise environment. Exploiting the periodicity of voiced speech and random distribution of background white noise, a more accurate estimation of noise power is calculated on each current frame of speech. The advantage, that the noise power is estimated from each current frame, can avoid the estimation delay and accuracy problem. Sometimes the background noise could be white or colored signals. A noise whitening method for the noise compensation LP Method is proposed so that the new noise estimator can be also applied to colored environment.\nWe further propose a crosscorrelation sequence-based LP analysis under noisy environment. The crosscorrelation sequence is utilized to replace the original speech signal which is sensitive to background noise, and applied to LP analysis. The approach can improve the performance of LP analysis under noisy environment.\nIn this dissertation, we focus on resolving the two factors that degrade the performance of LP analysis and new approaches have been proposed and implemented. The experimental results, based on synthetic and real speeches, demonstrate the effectiveness of the new approaches for improving the performance of the LP analysis.", "subitem_description_type": "Abstract"}]}, "item_113_description_24": {"attribute_name": "目次", "attribute_value_mlt": [{"subitem_description": "Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13\nAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14\n1 Introduction 16\n1.1 Overview of LP analysis . . . . . . . . . . . . . . . . . . . . . . . . 17\n1.2 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 18\n1.3 Dissertation Organization . . . . . . . . . . . . . . . . . . . . . . . 19\n2 Pitch Synchronous Linear Prediction Analysis of High-Pitched\nSpeech Using Weighted Short-Time Energy Function 20\n2.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . 20\n2.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21\n2.3 STE Function Based on Residual Signal . . . . . . . . . . . . . . . 22\n2.4 Pitch Synchronous Analysis Based on Weighted STE Function . . . 25\n2.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 28\n2.5.1 Results for Synthetic Speech Excited by Impulse Trains . . . 29\n2.5.2 Results for Synthetic Speech Excited by Realistic Excitation 29\n2.5.3 Results for Real Speech . . . . . . . . . . . . . . . . . . . . . 36\n2.5.4 Stability of the Resulting All-Pole Filter . . . . . . . . . . . 39\n2.6 Discussion and Summary . . . . . . . . . . . . . . . . . . . . . . . . 40\n3 Noise Compensation LP Method Based on Pitch Synchronous\nAnalysis 42\n3.1 Problem Description of Noise Compensation and Related Works . . 43\n3.2 Noise Estimation Based on Pitch Synchronous Analysis . . . . . . . 45\n3.2.1 Pitch Synchronization . . . . . . . . . . . . . . . . . . . . . 46\n3.2.2 Enhanced Speech and Modified Noise Signals . . . . . . . . 47\n3.2.3 Proposed Noise Estimator . . . . . . . . . . . . . . . . . . . 49\n3.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 52\n3.3.1 Simulation for Verifying the Proposed Noise Estimator . . . 52\n3.3.2 Results using Synthetic Speech . . . . . . . . . . . . . . . . 53\n3.3.3 Results using Real Speech . . . . . . . . . . . . . . . . . . . 55\n3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61\n4 Pink Noise Whitening Method for Noise Compensation LP Method\nBased on Pitch Synchronous Analysis 62\n4.1 Constraint Problem of Noise Compensation . . . . . . . . . . . . . 62\n4.2 Proposed Prediction Whitening Filter . . . . . . . . . . . . . . . . . 63\n4.3 Improved PSAS Method . . . . . . . . . . . . . . . . . . . . . . . . 69\n4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 71\n4.4.1 Results for Synthetic Speech . . . . . . . . . . . . . . . . . . 71\n4.4.2 Results for Real Speech . . . . . . . . . . . . . . . . . . . . . 74\n4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76\n5 Linear Prediction Analysis of Crosscorrelation Sequence for Voiced\nSpeech 78\n5.1 Problem Description of LP Analysis of Autocorrelation Sequence . . 79\n5.2 LP Analysis of Crosscorrelation Sequence . . . . . . . . . . . . . . . 80\n5.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 82\n5.3.1 Results for Synthetic Speech . . . . . . . . . . . . . . . . . . 82\n5.3.2 Results for Real Vowel . . . . . . . . . . . . . . . . . . . . . 83\n5.4 Pitch Synchronous LP Analysis of Crosscorrelation Sequence . . . . 85\n5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90\n6 Conclusions 91\n6.1 Summary of the Work . . . . . . . . . . . . . . . . . . . . . . . . . 91\n6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92\nA Discussion of Noise Power When K is Odd 93", "subitem_description_type": "Other"}]}, "item_113_description_25": {"attribute_name": "注記", "attribute_value_mlt": [{"subitem_description": "指導教員 : 島村徹也", "subitem_description_type": "Other"}]}, "item_113_description_33": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"subitem_description": "text", "subitem_description_type": "Other"}]}, "item_113_description_34": {"attribute_name": "フォーマット", "attribute_value_mlt": [{"subitem_description": "application/pdf", "subitem_description_type": "Other"}]}, "item_113_dissertation_number_19": {"attribute_name": "学位授与番号", "attribute_value_mlt": [{"subitem_dissertationnumber": "甲第983号"}]}, "item_113_identifier_registration": {"attribute_name": "ID登録", "attribute_value_mlt": [{"subitem_identifier_reg_text": "10.24561/00010359", "subitem_identifier_reg_type": "JaLC"}]}, "item_113_publisher_11": {"attribute_name": "出版者名", "attribute_value_mlt": [{"subitem_publisher": "埼玉大学大学院理工学研究科"}]}, "item_113_publisher_12": {"attribute_name": "出版者名(別言語)", "attribute_value_mlt": [{"subitem_publisher": "Graduate School of Science and Engineering, Saitama University"}]}, "item_113_record_name_8": {"attribute_name": "書誌", "attribute_value_mlt": [{"subitem_record_name": "博士論文(埼玉大学大学院理工学研究科(博士後期課程))"}]}, "item_113_text_3": {"attribute_name": "著者 ローマ字", "attribute_value_mlt": [{"subitem_text_value": "LIU, Liqing"}]}, "item_113_text_31": {"attribute_name": "版", "attribute_value_mlt": [{"subitem_text_value": "[出版社版]"}]}, "item_113_text_36": {"attribute_name": "アイテムID", "attribute_value_mlt": [{"subitem_text_value": "GD0000646"}]}, "item_113_text_4": {"attribute_name": "著者 所属", "attribute_value_mlt": [{"subitem_text_value": "埼玉大学大学院理工学研究科(博士後期課程)理工学専攻"}]}, "item_113_text_5": {"attribute_name": "著者 所属(別言語)", "attribute_value_mlt": [{"subitem_text_value": "Graduate School of Science and Engineering, Saitama University"}]}, "item_113_version_type_32": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_access_right": {"attribute_name": "アクセス権", "attribute_value_mlt": [{"subitem_access_right": "open access", "subitem_access_right_uri": "http://purl.org/coar/access_right/c_abf2"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "劉, 麗清", "creatorNameLang": "ja"}, {"creatorName": "リュウ, レイセイ", "creatorNameLang": "ja-Kana"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-01-23"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "GD0000646.pdf", "filesize": [{"value": "5.6 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_note", "mimetype": "application/pdf", "size": 5600000.0, "url": {"label": "GD0000646.pdf", "objectType": "fulltext", "url": "https://sucra.repo.nii.ac.jp/record/10365/files/GD0000646.pdf"}, "version_id": "8497d958-8b16-494a-a9f3-54ad715ad671"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "doctoral thesis", "resourceuri": "http://purl.org/coar/resource_type/c_db06"}]}, "item_title": "Improved Methods for Pitch Synchronous Linear Prediction Analysis of Speech", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Improved Methods for Pitch Synchronous Linear Prediction Analysis of Speech", "subitem_title_language": "en"}]}, "item_type_id": "113", "owner": "15", "path": ["506"], "permalink_uri": "https://doi.org/10.24561/00010359", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2015-12-17"}, "publish_date": "2015-12-17", "publish_status": "0", "recid": "10365", "relation": {}, "relation_version_is_last": true, "title": ["Improved Methods for Pitch Synchronous Linear Prediction Analysis of Speech"], "weko_shared_id": -1}
Improved Methods for Pitch Synchronous Linear Prediction Analysis of Speech
https://doi.org/10.24561/00010359
https://doi.org/10.24561/000103594dffa491-a5df-45c8-b3b5-aa2e531fd426
名前 / ファイル | ライセンス | アクション |
---|---|---|
GD0000646.pdf (5.6 MB)
|
|
Item type | 学位論文 / Thesis or Dissertation(1) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
公開日 | 2015-12-17 | |||||||||
タイトル | ||||||||||
言語 | en | |||||||||
タイトル | Improved Methods for Pitch Synchronous Linear Prediction Analysis of Speech | |||||||||
言語 | ||||||||||
言語 | eng | |||||||||
資源タイプ | ||||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||||||
資源タイプ | doctoral thesis | |||||||||
ID登録 | ||||||||||
ID登録 | 10.24561/00010359 | |||||||||
ID登録タイプ | JaLC | |||||||||
アクセス権 | ||||||||||
アクセス権 | open access | |||||||||
アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||
タイトル(別言語) | ||||||||||
その他のタイトル | 音声のピッチ同期線形予測分析のための改善法 | |||||||||
著者 |
劉, 麗清
× 劉, 麗清
|
|||||||||
著者 ローマ字 | ||||||||||
LIU, Liqing | ||||||||||
著者 所属 | ||||||||||
埼玉大学大学院理工学研究科(博士後期課程)理工学専攻 | ||||||||||
著者 所属(別言語) | ||||||||||
Graduate School of Science and Engineering, Saitama University | ||||||||||
書誌 | ||||||||||
収録物名 | 博士論文(埼玉大学大学院理工学研究科(博士後期課程)) | |||||||||
書誌情報 |
発行日 2015 |
|||||||||
出版者名 | ||||||||||
出版者 | 埼玉大学大学院理工学研究科 | |||||||||
出版者名(別言語) | ||||||||||
出版者 | Graduate School of Science and Engineering, Saitama University | |||||||||
形態 | ||||||||||
内容記述タイプ | Other | |||||||||
内容記述 | 103 p. | |||||||||
学位授与番号 | ||||||||||
学位授与番号 | 甲第983号 | |||||||||
学位授与年月日 | ||||||||||
学位授与年月日 | 2015-03-24 | |||||||||
学位名 | ||||||||||
学位名 | 博士(工学) | |||||||||
学位授与機関 | ||||||||||
学位授与機関識別子Scheme | kakenhi | |||||||||
学位授与機関識別子 | 12401 | |||||||||
学位授与機関名 | 埼玉大学 | |||||||||
抄録 | ||||||||||
内容記述タイプ | Abstract | |||||||||
内容記述 | Linear prediction (LP) analysis has been applied to speech system over the last few decades. LP technique is well-suited for speech analysis due to its ability to model speech production process approximately. Hence LP analysis has been widely used for speech enhancement, low-bit-rate speech coding in cellular telephony, speech recognition, characteristic parameter extraction (vocal tract resonances frequencies, fundamental frequency called pitch) and so on. However, the performance of the conventional LP method is degraded by high-pitched harmonic structure of glottal excitation source and background noise. In order to improve the performance of LP analysis, it is necessary to reduce the effect of these two factors, which is a most challenging task for LP analysis. The objective of this dissertation is to develop some approaches to improve the performance of the LP analysis based on pitch synchronous analysis. We consider a pitch synchronous LP analysis for high-pitched speech using a weighted short time energy (STE) function for the purpose of downgrading the effect of the harmonic structure of the glottal excitation source. Unlike some conventional techniques, which require the electroglottography (EGG) signal or complicated epoch extraction algorithms, we utilize a simple STE computation of speech signal and prediction residual signal to extract the interval of glottal closed phase during a glottal cycle and do not need to estimate the instant of glottal closure and opening exactly. To reduce the infuence of the background noise, we propose a noise compensation LP method based on pitch synchronous analysis under white noise environment. Exploiting the periodicity of voiced speech and random distribution of background white noise, a more accurate estimation of noise power is calculated on each current frame of speech. The advantage, that the noise power is estimated from each current frame, can avoid the estimation delay and accuracy problem. Sometimes the background noise could be white or colored signals. A noise whitening method for the noise compensation LP Method is proposed so that the new noise estimator can be also applied to colored environment. We further propose a crosscorrelation sequence-based LP analysis under noisy environment. The crosscorrelation sequence is utilized to replace the original speech signal which is sensitive to background noise, and applied to LP analysis. The approach can improve the performance of LP analysis under noisy environment. In this dissertation, we focus on resolving the two factors that degrade the performance of LP analysis and new approaches have been proposed and implemented. The experimental results, based on synthetic and real speeches, demonstrate the effectiveness of the new approaches for improving the performance of the LP analysis. |
|||||||||
目次 | ||||||||||
内容記述タイプ | Other | |||||||||
内容記述 | Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1 Introduction 16 1.1 Overview of LP analysis . . . . . . . . . . . . . . . . . . . . . . . . 17 1.2 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.3 Dissertation Organization . . . . . . . . . . . . . . . . . . . . . . . 19 2 Pitch Synchronous Linear Prediction Analysis of High-Pitched Speech Using Weighted Short-Time Energy Function 20 2.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3 STE Function Based on Residual Signal . . . . . . . . . . . . . . . 22 2.4 Pitch Synchronous Analysis Based on Weighted STE Function . . . 25 2.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.5.1 Results for Synthetic Speech Excited by Impulse Trains . . . 29 2.5.2 Results for Synthetic Speech Excited by Realistic Excitation 29 2.5.3 Results for Real Speech . . . . . . . . . . . . . . . . . . . . . 36 2.5.4 Stability of the Resulting All-Pole Filter . . . . . . . . . . . 39 2.6 Discussion and Summary . . . . . . . . . . . . . . . . . . . . . . . . 40 3 Noise Compensation LP Method Based on Pitch Synchronous Analysis 42 3.1 Problem Description of Noise Compensation and Related Works . . 43 3.2 Noise Estimation Based on Pitch Synchronous Analysis . . . . . . . 45 3.2.1 Pitch Synchronization . . . . . . . . . . . . . . . . . . . . . 46 3.2.2 Enhanced Speech and Modified Noise Signals . . . . . . . . 47 3.2.3 Proposed Noise Estimator . . . . . . . . . . . . . . . . . . . 49 3.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3.1 Simulation for Verifying the Proposed Noise Estimator . . . 52 3.3.2 Results using Synthetic Speech . . . . . . . . . . . . . . . . 53 3.3.3 Results using Real Speech . . . . . . . . . . . . . . . . . . . 55 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4 Pink Noise Whitening Method for Noise Compensation LP Method Based on Pitch Synchronous Analysis 62 4.1 Constraint Problem of Noise Compensation . . . . . . . . . . . . . 62 4.2 Proposed Prediction Whitening Filter . . . . . . . . . . . . . . . . . 63 4.3 Improved PSAS Method . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.4.1 Results for Synthetic Speech . . . . . . . . . . . . . . . . . . 71 4.4.2 Results for Real Speech . . . . . . . . . . . . . . . . . . . . . 74 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5 Linear Prediction Analysis of Crosscorrelation Sequence for Voiced Speech 78 5.1 Problem Description of LP Analysis of Autocorrelation Sequence . . 79 5.2 LP Analysis of Crosscorrelation Sequence . . . . . . . . . . . . . . . 80 5.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.3.1 Results for Synthetic Speech . . . . . . . . . . . . . . . . . . 82 5.3.2 Results for Real Vowel . . . . . . . . . . . . . . . . . . . . . 83 5.4 Pitch Synchronous LP Analysis of Crosscorrelation Sequence . . . . 85 5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 6 Conclusions 91 6.1 Summary of the Work . . . . . . . . . . . . . . . . . . . . . . . . . 91 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 A Discussion of Noise Power When K is Odd 93 |
|||||||||
注記 | ||||||||||
内容記述タイプ | Other | |||||||||
内容記述 | 指導教員 : 島村徹也 | |||||||||
版 | ||||||||||
[出版社版] | ||||||||||
著者版フラグ | ||||||||||
出版タイプ | VoR | |||||||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||
資源タイプ | ||||||||||
内容記述タイプ | Other | |||||||||
内容記述 | text | |||||||||
フォーマット | ||||||||||
内容記述タイプ | Other | |||||||||
内容記述 | application/pdf | |||||||||
作成日 | ||||||||||
日付 | 2015-12-16 | |||||||||
日付タイプ | Created | |||||||||
アイテムID | ||||||||||
GD0000646 |