Segmentation and classification of broadcast news audio
人工智能英文参考文献(最新120个)
人工智能是一门新兴的具有挑战力的学科。
自人工智能诞生以来,发展迅速,产生了许多分支。
诸如强化学习、模拟环境、智能硬件、机器学习等。
但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。
下面是搜索整理的人工智能英文参考文献的分享，供大家借鉴参考。
人工智能英文参考文献一：[1]Lars Egevad,Peter Str?m,Kimmo Kartasalo,Henrik Olsson,Hemamali Samaratunga,Brett Delahunt,Martin Eklund. The utility of artificial intelligence in the assessment of prostate pathology[J]. Histopathology,2020,76(6).[2]Rudy van Belkom. The Impact of Artificial Intelligence on the Activities ofa Futurist[J]. World Futures Review,2020,12(2).[3]Reza Hafezi. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments[J]. World Futures Review,2020,12(2).[4]Alejandro Díaz-Domínguez. How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence[J]. World Futures Review,2020,12(2).[5]Russell T. Warne,Jared Z. Burton. Beliefs About Human Intelligence in a Sample of Teachers and Nonteachers[J]. Journal for the Education of the Gifted,2020,43(2).[6]Russell Belk,Mariam Humayun,Ahir Gopaldas. Artificial Life[J]. Journal of Macromarketing,2020,40(2).[7]Walter Kehl,Mike Jackson,Alessandro Fergnani. Natural Language Processing and Futures Studies[J]. World Futures Review,2020,12(2).[8]Anne Boysen. Mine the Gap: Augmenting Foresight Methodologies with Data Analytics[J]. World Futures Review,2020,12(2).[9]Marco Bevolo,Filiberto Amati. The Potential Role of AI in Anticipating Futures from a Design Process Perspective: From the Reflexive Description of “Design” to a Discussion of Influences by the Inclusion of AI in the Futures Research Process[J]. World Futures Review,2020,12(2).[10]Lan Xu,Paul Tu,Qian Tang,Dan Seli?teanu. Contract Design for Cloud Logistics (CL) Based on Blockchain Technology (BT)[J]. Complexity,2020,2020.[11]L. Grant,X. Xue,Z. Vajihi,A. Azuelos,S. Rosenthal,D. Hopkins,R. Aroutiunian,B. Unger,A. Guttman,M. Afilalo. LO32: Artificial intelligence to predict disposition to improve flow in the emergency department[J]. CJEM,2020,22(S1).[12]A. Kirubarajan,A. Taher,S. Khan,S. Masood. P071: Artificial intelligence in emergency medicine: A scoping review[J]. CJEM,2020,22(S1).[13]L. Grant,P. Joo,B. Eng,A. Carrington,M. Nemnom,V. Thiruganasambandamoorthy. LO22: Risk-stratification of emergency department syncope by artificial intelligence using machine learning: human, statistics or machine[J]. CJEM,2020,22(S1).[14]Riva Giuseppe,Riva Eleonora. OS for Ind Robots: Manufacturing Robots Get Smarter Thanks to Artificial Intelligence.[J]. Cyberpsychology, behavior and social networking,2020,23(5).[15]Markus M. Obmann,Aurelio Cosentino,Joshy Cyriac,Verena Hofmann,Bram Stieltjes,Daniel T. Boll,Benjamin M. Yeh,Matthias R. Benz. Quantitative enhancement thresholds and machine learning algorithms for the evaluation of renal lesions using single-phase split-filter dual-energy CT[J]. Abdominal Radiology,2020,45(1).[16]Haytham H. Elmousalami,Mahmoud Elaskary. Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence[J]. Journal of Petroleum Exploration and Production Technology,2020,10(10).[17]Rüdiger Schulz-Wendtland,Karin Bock. Bildgebung in der Mammadiagnostik –Ein Ausblick <trans-title xml:lang="en">Imaging in breast diagnostics—an outlook [J]. Der Gyn?kologe,2020,53(6).</trans-title>[18]Nowakowski Piotr,Szwarc Krzysztof,Boryczka Urszula. Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection[J]. Science of the Total Environment,2020,730.[19]Wang Huaizhi,Liu Yangyang,Zhou Bin,Li Canbing,Cao Guangzhong,Voropai Nikolai,Barakhtenko Evgeny. Taxonomy research of artificial intelligence for deterministic solar power forecasting[J]. Energy Conversion and Management,2020,214.[20]Kagemoto Hiroshi. Forecasting a water-surface wave train with artificial intelligence- A case study[J]. Ocean Engineering,2020,207.[21]Tomonori Aoki,Atsuo Yamada,Kazuharu Aoyama,Hiroaki Saito,Gota Fujisawa,Nariaki Odawara,Ryo Kondo,Akiyoshi Tsuboi,Rei Ishibashi,Ayako Nakada,Ryota Niikura,Mitsuhiro Fujishiro,Shiro Oka,Soichiro Ishihara,Tomoki Matsuda,Masato Nakahori,Shinji Tanaka,Kazuhiko Koike,Tomohiro Tada. Clinical usefulness of a deep learning‐based system as the first screening on small‐bowel capsule endoscopy reading[J]. Digestive Endoscopy,2020,32(4).[22]Masashi Fujii,Hajime Isomoto. Next generation of endoscopy: Harmony with artificial intelligence and robotic‐assisted devices[J]. Digestive Endoscopy,2020,32(4).[23]Roberto Verganti,Luca Vendraminelli,Marco Iansiti. Innovation and Design in the Age of Artificial Intelligence[J]. Journal of Product Innovation Management,2020,37(3).[24]Yuval Elbaz,David Furman,Maytal Caspary Toroker. Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence[J]. Advanced Functional Materials,2020,30(18).[25]Dinesh Visva Gunasekeran,Tien Yin Wong. Artificial Intelligence in Ophthalmology in 2020: A Technology on the Cusp for Translation and Implementation[J]. Asia-Pacific Journal of Ophthalmology,2020,9(2).[26]Fu-Neng Jiang,Li-Jun Dai,Yong-Ding Wu,Sheng-Bang Yang,Yu-Xiang Liang,Xin Zhang,Cui-Yun Zou,Ren-Qiang He,Xiao-Ming Xu,Wei-De Zhong. The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks[J]. Journal of the Chinese Medical Association,2020,83(5).[27]Matheus Calil Faleiros,Marcello Henrique Nogueira-Barbosa,Vitor Faeda Dalto,JoséRaniery Ferreira Júnior,Ariane Priscilla Magalh?es Tenório,Rodrigo Luppino-Assad,Paulo Louzada-Junior,Rangaraj Mandayam Rangayyan,Paulo Mazzoncini de Azevedo-Marques. Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging[J]. Advances in Rheumatology,2020,60(1078).[28]Balamurugan Balakreshnan,Grant Richards,Gaurav Nanda,Huachao Mao,Ragu Athinarayanan,Joseph Zaccaria. PPE Compliance Detection using Artificial Intelligence in Learning Factories[J]. Procedia Manufacturing,2020,45.[29]M. Stévenin,V. Avisse,N. Ducarme,A. de Broca. Qui est responsable si un robot autonome vient à entra?ner un dommage ?[J]. Ethique et Santé,2020.[30]Fatemeh Barzegari Banadkooki,Mohammad Ehteram,Fatemeh Panahi,Saad Sh. Sammen,Faridah Binti Othman,Ahmed EL-Shafie. Estimation of Total Dissolved Solids (TDS) using New Hybrid Machine Learning Models[J]. Journal of Hydrology,2020.[31]Adam J. Schwartz,Henry D. Clarke,Mark J. Spangehl,Joshua S. Bingham,DavidA. Etzioni,Matthew R. Neville. Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons?[J]. The Journal of Arthroplasty,2020.[32]Ivana Nizetic Kosovic,Toni Mastelic,Damir Ivankovic. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis[J]. Journal of Cleaner Production,2020.[33]Lauren Fried,Andrea Tan,Shirin Bajaj,Tracey N. Liebman,David Polsky,Jennifer A. Stein. Technological advances for the detection of melanoma: Part I. Advances in diagnostic techniques[J]. Journal of the American Academy of Dermatology,2020.[34]Mohammed Amoon,Torki Altameem,Ayman Altameem. Internet of things Sensor Assisted Security and Quality Analysis for Health Care Data Sets Using Artificial Intelligent Based Heuristic Health Management System[J]. Measurement,2020.[35]E. Lotan,C. Tschider,D.K. Sodickson,A. Caplan,M. Bruno,B. Zhang,Yvonne W. Lui. Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future[J]. Journal of the American College of Radiology,2020.[36]Fabien Lareyre,Cédric Adam,Marion Carrier,Juliette Raffort. Artificial Intelligence in Vascular Surgery: moving from Big Data to Smart Data[J]. Annals of Vascular Surgery,2020.[37]Ilesanmi Daniyan,Khumbulani Mpofu,Moses Oyesola,Boitumelo Ramatsetse,Adefemi Adeodu. Artificial intelligence for predictive maintenance in the railcar learning factories[J]. Procedia Manufacturing,2020,45.[38]Janet L. McCauley,Anthony E. Swartz. Reframing Telehealth[J]. Obstetrics and Gynecology Clinics of North America,2020.[39]Jean-Emmanuel Bibault,Lei Xing. Screening for chronic obstructive pulmonary disease with artificial intelligence[J]. The Lancet Digital Health,2020,2(5).[40]Andrea Laghi. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence[J]. The Lancet Digital Health,2020,2(5).人工智能英文参考文献二：[41]K. Orhan,I. S. Bayrakdar,M. Ezhov,A. Kravtsov,T. ?zyürek. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans[J]. International Endodontic Journal,2020,53(5).[42]Avila A M,Mezi? I. Data-driven analysis and forecasting of highway traffic dynamics.[J]. Nature communications,2020,11(1).[43]Neri Emanuele,Miele Vittorio,Coppola Francesca,Grassi Roberto. Use of CT andartificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology.[J]. La Radiologia medica,2020.[44]Tau Noam,Stundzia Audrius,Yasufuku Kazuhiro,Hussey Douglas,Metser Ur. Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.[J]. AJR. American journal of roentgenology,2020.[45]Coppola Francesca,Faggioni Lorenzo,Regge Daniele,Giovagnoni Andrea,Golfieri Rita,Bibbolino Corrado,Miele Vittorio,Neri Emanuele,Grassi Roberto. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.[J]. La Radiologia medica,2020.[46]?. ? ? ? ? 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Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.[J]. Korean journal of radiology,2020,21(5).[55]Mateen Bilal A,David Anna L,Denaxas Spiros. Electronic Health Records to Predict Gestational Diabetes Risk.[J]. Trends in pharmacological sciences,2020,41(5).[56]Yao Xiang,Mao Ling,Lv Shunli,Ren Zhenghong,Li Wentao,Ren Ke. CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time.[J]. Journal of the neurological sciences,2020,412.[57]van Assen Marly,Banerjee Imon,De Cecco Carlo N. Beyond the Artificial Intelligence Hype: What Lies Behind the Algorithms and What We Can Achieve.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[58]Guzik Tomasz J,Fuster Valentin. Leaders in Cardiovascular Research: Valentin Fuster.[J]. Cardiovascular research,2020,116(6).[59]Fischer Andreas M,Eid Marwen,De Cecco Carlo N,Gulsun Mehmet A,van Assen Marly,Nance John W,Sahbaee Pooyan,De Santis Domenico,Bauer Maximilian J,Jacobs Brian E,Varga-Szemes Akos,Kabakus Ismail M,Sharma Puneet,Jackson Logan J,Schoepf U Joseph. Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[60]Ghosh Adarsh,Kandasamy Devasenathipathy. Interpretable Artificial Intelligence: Why and When.[J]. AJR. American journal of roentgenology,2020,214(5).[61]M.Rosario González-Rodríguez,M.Carmen Díaz-Fernández,Carmen Pacheco Gómez. Facial-expression recognition: An emergent approach to the measurement of tourist satisfaction through emotions[J]. Telematics and Informatics,2020,51.[62]Ru-Xi Ding,Iván Palomares,Xueqing Wang,Guo-Rui Yang,Bingsheng Liu,Yucheng Dong,Enrique Herrera-Viedma,Francisco Herrera. Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective[J]. Information Fusion,2020,59.[63]Abdulrhman H. Al-Jebrni,Brendan Chwyl,Xiao Yu Wang,Alexander Wong,Bechara J. Saab. AI-enabled remote and objective quantification of stress at scale[J]. Biomedical Signal Processing and Control,2020,59.[64]Gillian Thomas,Elizabeth Eisenhauer,Robert G. Bristow,Cai Grau,Coen Hurkmans,Piet Ost,Matthias Guckenberger,Eric Deutsch,Denis Lacombe,Damien C. Weber. The European Organisation for Research and Treatment of Cancer, State of Science in radiation oncology and priorities for clinical trials meeting report[J]. European Journal of Cancer,2020,131.[65]Muhammad Asif. Are QM models aligned with Industry 4.0? A perspective on current practices[J]. Journal of Cleaner Production,2020,258.[66]Siva Teja Kakileti,Himanshu J. Madhu,Geetha Manjunath,Leonard Wee,Andre Dekker,Sudhakar Sampangi. Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics[J]. Artificial Intelligence In Medicine,2020,105.[67]. Evaluation of Payer Budget Impact Associated with the Use of Artificial Intelligence in Vitro Diagnostic, Kidneyintelx, to Modify DKD Progression:[J]. American Journal of Kidney Diseases,2020,75(5).[68]Rohit Nishant,Mike Kennedy,Jacqueline Corbett. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda[J]. International Journal of Information Management,2020,53.[69]Hoang Nguyen,Xuan-Nam Bui. Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach[J]. Applied Soft Computing Journal,2020,92.[70]Benjamin S. Hopkins,Aditya Mazmudar,Conor Driscoll,Mark Svet,Jack Goergen,Max Kelsten,Nathan A. 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Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM)[J]. Postharvest Biology and Technology,2020,166.[78]Wafaa Wardah,Abdollah Dehzangi,Ghazaleh Taherzadeh,Mahmood A. Rashid,M.G.M. Khan,Tatsuhiko Tsunoda,Alok Sharma. Predicting protein-peptide binding sites with a deep convolutional neural network[J]. Journal of Theoretical Biology,2020,496.[79]Francisco F.X. Vasconcelos,Róger M. Sarmento,Pedro P. Rebou?as Filho,Victor Hugo C. de Albuquerque. Artificial intelligence techniques empowered edge-cloud architecture for brain CT image analysis[J]. Engineering Applications of Artificial Intelligence,2020,91.[80]Masaaki Konishi. Bioethanol production estimated from volatile compositions in hydrolysates of lignocellulosic biomass by deep learning[J]. Journal of Bioscience and Bioengineering,2020,129(6).人工智能英文参考文献三：[81]J. Kwon,K. Kim. 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A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting[J]. Engineering Applications of Artificial Intelligence,2020,92.[118]Qingsong Ruan,Zilin Wang,Yaping Zhou,Dayong Lv. A new investor sentiment indicator ( ISI ) based on artificial intelligence: A powerful return predictor in China[J]. Economic Modelling,2020,88.[119]Mohamed Abdel-Basset,Weiping Ding,Laila Abdel-Fatah. The fusion of Internet of Intelligent Things (IoIT) in remote diagnosis of obstructive Sleep Apnea: A survey and a new model[J]. Information Fusion,2020,61.[120]Federico Caobelli. Artificial intelligence in medical imaging: Game over for radiologists?[J]. European Journal of Radiology,2020,126.以上就是关于人工智能参考文献的分享，希望对你有所帮助。
结合注意力机制和因果卷积网络的维吾尔语方言识别
第39卷第6期声学技术Vol.39, No.6引用格式：孙杰, 王宏, 吾守尔·斯拉木. 结合注意力机制和因果卷积网络的维吾尔语方言识别[J]. 声学技术, 2020, 39(6): 697-703. [SUN Jie, W ANG Hong, Wushouer Silamu. The Uyghur dialect recognition based on attention mechanism and causal convolution networks[J]. Technical Acoustics, 39(6): 697-703.] DOI: 10.16300/ki.1000-3630.2020.06.008结合注意力机制和因果卷积网络的维吾尔语方言识别孙杰1,2，王宏2，吾守尔·斯拉木1,2(1. 新疆大学信息科学与工程学院，新疆乌鲁木齐830046；2.昌吉学院，新疆昌吉831100)摘要：针对传统x-vector模型生成方言语音段级表示时，未考虑不同帧级特征对方言辨识作用不一致的问题，以及维吾尔语的黏着性特点，提出结合注意力机制和因果卷积网络的维吾尔语方言识别方法。
首先使用多层因果卷网络实现方言语音序列建模，然后采用空洞卷积核增大感受野扩展采样范围，最后使用注意力池化获取方言语音段级特征。
维吾尔语方言识别实验结果表明，所提方法较标准x-vector模型方言识别的识别准确率提升了23.19个百分点。
关键词：注意力机制；因果卷积网络；空洞卷积；维吾尔语方言；识别中图分类号：H107 文献标识码：A 文章编号：1000-3630(2020)-06-0697-07The Uyghur dialect recognition based on attention mechanismand causal convolution networksSUN Jie1,2, W ANG Hong2, Wushouer Silamu1,2(1. College of Information Science and Engineering, Xinjiang University, Urumqi 830046, Xinjiang, China;2. Changji University, Changji 831100, Xinjiang, China)Abstract:Considering that different frame features have different effects on dialect recognition when the traditional x-vector model is used to generate segment representation of dialect speech, and that Uighur language is an agglutinative language, a recognition method of Uighur dialect based on attention mechanism and causal convolution network is proposed. First, the multi-layer causal volume network is used to model the speech sequence, then the dilated convolu-tion kernel is used to expand the sampling range of the receptive field, and finally the attention pooling is used to obtain the speech segment features. The experimental results of Uyghur dialect recognition show that the accuracy of the proposed method is 23.19 percentage higher than that of the standard x-vector model.Key words: attention mechanism; causal convolution networks; dilated convolution; Uyghur dialect; recognition0 引言方言识别亦称方言分类，属于语种识别的范畴。
2024_2025学年新教材高中英语单元检测卷四外研版必修第二册
单元检测卷(四)选择题部分第一部分听力(共两节，满分30分)第一节(共5小题；每小题1.5分，满分7.5分)听下面5段对话。
每段对话后有一个小题，从题中所给的A、B、C三个选项中选出最佳选项。
听完每段对话后，你都有10秒钟的时间来回答有关小题和阅读下一小题。
每段对话仅读一遍。
1．What do we know about Mr White?A．He has much money.B．He is a good teacher.C．He is very lucky.2．What is the woman busy with?A．A computer game. B．An email.C．A paper.3．How much is John's electricity bill?A．50 dollars. B．36 dollars.C．30 dollars.4．What's wrong with Jane?A．She has a toothache.B．She has a headache.C．She has a stomachache.5．Where will Sam work during the summer vacation?A．In a restaurant.B．In an amusement park.C．In a school.其次节(共15小题；每小题1.5分，满分22.5分)听下面5段对话或独白。
每段对话或独白后有几个小题，从题中所给的A、B、C三个选项中选出最佳选项。
听每段对话或独白前，你将有时间阅读各个小题，每小题5秒钟；听完后，各小题将给出5秒钟的作答时间。
每段对话或独白读两遍。
听第6段材料，回答第6、7题。
6．Where are the two speakers talking?A．At a bus stop. B．In a zoo.C．On a bus.7．What will the woman do next?A．Take the No.36 bus.B．Walk to the next street.C．Take the No.310 bus.听第7段材料，回答第8、9题。
基于深度学习的声信号分类识别方法
敬请登录网站在线投稿(t o u ga o .m e s n e t .c o m.c n )2021年第1期23基于深度学习的声信号分类识别方法*王鹏程1,崔敏1,王彦博1,李剑1,赵欣2(1.中北大学信息探测与处理山西省重点实验室,太原030051;2.北方科技信息研究所)*基金项目:国家自然基金青年科学基金(61901419);山西省面上青年资金(201801D 221205);山西省高校创新项目(201802083);装备预研兵器工业联合基金(6141B 012895);装备预研兵器装备联合基金(6141B 021301);山西省高等学校科技成果转换培育项目(2020C G 038);中北大学科学研究基金(N O .X J J 201803)㊂摘要:提出了一种基于深度学习的声信号分类识别方法,将声场环境中声源目标的识别等效为声场信号 特定声源的端到端学习过程,建立一种以l o g me l 能量为声信号特征的预提取方法,以深度残差网络作为特征自动提取及分类的声信号分类识别模型㊂在两个大型数据集上对模型性能进行了验证,实验结果表明,本文提出的深度残差网络模型在D C A S E 2019数据集和U r b a n S o u n d 8K 数据集上能够实现80.2%和76.4%的识别精度,在声源探测领域具有一定的应用价值㊂关键词:声源探测;声信号分类识别;深度学习;深度残差网络;时频域分析中图分类号:T P 39 文献标识码:AA c o u s t i c S i g n a l C l a s s i f i c a t i o n a n d R e c o g n i t i o n M e t h o dB a s e d o n D e e p L e a r n i n gW a n g P e n g c h e n g 1,C u i M i n 1,W a n g Ya nb o 1,L i J i a n 1,Z h a o X i n 2(1.S h a n x i P r o v i n c e K e y L a b o r a t o r y o f I n f o r m a t i o n D e t e c t i o n a n d P r o c e s s i n g ,N o r t h U n i v e r s i t y of C h i n a ,T a i y u a n 030051,C h i n a ;2.N o r t h I n s t i t u t e o f S c i e n c e a n d T e c h n o l og y In f o r m a t i o n )A b s t r a c t :I n t h e p a p e r ,a d e e p l e a r n i n g -b a s e d s o u n d s i g n a l c l a s s i f i c a t i o n a n d r e c o g n i t i o n m e t h o d i s p r o p o s e d .T h e s o u n d s o u r c e t a r ge t r e c -o g n i t i o n i n t h e s o u n df i e l d e n v i r o n m e n t i s e q u i v a l e n t t o t h e s o u n d f i e l d s ig n a l -e n d -t o -e n d o f a s p e c i f i c s o u n d s o u r c e .D u r i n g th e l e a r ni n gp r o c e s s ,a s o u n d s i g n a l c l a s s i f i c a t i o n a n d r e c o g n i t i o n m o d e l w i t h l o g -m e l e n e r g y a s t h e p r e -e x t r a c t i o n m e t h o d o f a c o u s t i c s i gn a l f e a t u r e s a n d d e e pr e s i d u a l n e t w o r k a s t h e f e a t u r e a u t o m a t i c e x t r a c t i o n a n d c l a s s i f i c a t i o n i s e s t a b l i s h e d .T h e p e r f o r m a n c e o f t h e m o d e l i s v e r i f i e d o n t w o l a r g e d a t a s e t s .T h e e x p e r i m e n t r e s u l t s s h o w t h a t t h e d e e p r e s i d u a l n e t w o r k m o d e l p r o p o s e d i n t h i s p a pe r c a n a c h i e v e 80.2%a n d 76.4%r e c o g n i t i o n a c c u r a c y o n t h e D C A S E 2019d a t a s e t a n d U r b a n S o u n d 8K d a t a s e t .T h e d e t e c t i o nf i e l d h a s c e r t a i n a p p l i c a t i o n v a l u e .K e y w o r d s :s o u n d s o u r c e d e t e c t i o n ;a c o u s t i c s ig n a l c l a s s i f i c a t i o n a n d r e c o g n i t i o n ;d e e p l e a r n i n g ;d e e p r e s i d u a l n e t w o r k ;t i m e -f r e q u e n c y d o -m a i n a n a l ys i s 0 引 言声源识别作为一个热点问题已经在许多应用领域内被广泛研究[1],不仅在对军事目标(如坦克㊁轮式战车㊁武装直升机和战斗机)的准确识别等现代军用场景中有着重要价值[2-3],而且在海洋动物识别[4]㊁民航飞机[5]㊁噪声源识别[6]等民用场景中,也是实现目标识别的重要手段㊂声信号分类识别是实现声源识别的主要方法,指的是将目标标签与声数据相关联的任务,可使用经典分类器(如高斯混合模型[7])支持向量机[8]㊁隐马尔可夫模型[9]等手动提取特征方法,或是深度学习这种自动提取特征的方式来进行声源识别㊂其中,深度学习是近年来的研究热点[10-12],在文字㊁图像和声音等数据的解析方面有很大的应用价值,能够学习样本数据的内在规律和表示层次,解决了很多复杂的模式识别难题,使得人工智能相关技术取得了很大进步㊂P i r o t t a [13]等使用无监督学习的方法进行声信号分类,通过无监督方法根据数据之间特征的相似性进行分组(集群算法),但由于未利用先验知识,分类的结果不能确定是有用的种类㊂G o e a u 与M a c A o d h a 研究了未带特征预处理的卷积神经网络技术在声源分类上的应用,识别特征直接从谱图数据中学习,避开了噪声敏感的特征提取阶段,但模型容易过拟合,需要大量的数据集作训练且模型也需要设计㊂针对上述问题,本文开展了一种基于深度学习的声信号目标分类模型,在特征提取部分,采用l o g me l 能量作为声音数据的特征预提取方法;在神经网络分类器部分,24M i c r o c o n t r o l l e r s &E m b e d d e d S ys t e m s 2021年第1期w w w .m e s n e t .c o m .c n采用R e s n e t 网络结构进行特征深度提取和分类处理,实现声源的高精度㊁高准确率探测识别㊂深度卷积网络应用于声信号分类框图如图1所示㊂图1 深度卷积网络应用于声信号分类框图1 深度学习应用于声信号分析声音数据是一种多通道的波形数据,转化为张量数据时体现为(t i m e s t e p s ,f e a t u r e s )这种形式的二维时间序列信息(见图2),但这只包含了声音的时域信息,未对其频域信息进行分析,因此需要对声音数据作时频域分析,将数据转化为声谱图的形式(见图3),将其作为图像来处理就可以在声谱图上训练深度卷积神经网络,利用卷积网络的特征提取能力对声信号进行特征提取,最终实现声音数据分类,建立声音数据分类模型㊂图2 声信号波形图通过多层卷积对声谱图(数据形式为(f r e q u e n c y,t i m -e s t e p s ,c h a n n e l ))中的信息进行表征学习,随着卷积层数增加,网络模型从声谱图中学习到的特征信息越来越抽象,最终得到声谱图到分类标签之间的映射关系,这些映射关系体现在深度学习网络结构模型中的各个参数中,与深度学习网络结构共同组成了声信息分类网络模型,实现了声信息的分类识别㊂随着卷积神经网络的不断堆叠,网络越来越深,不仅可以提高卷积网络对数据信息的表征能力,而且随着网络深度增加,过拟合的问题也能得到改善㊂但是,更深层的图3 声信号频谱图神经网络参数量巨大,相应需要的训练样本量也特别多,在可训练样本量较小的应用场景中,许多网络模型都无法提供良好的性能,因此需要在特征提取和声信息数据预处理及网络结构方面作进一步优化来改善声信息分类模型的性能㊂图4 残差块示意图2 深度神经网络的设计方法简单地用叠加层的方式来增加网络深度往往会由于梯度消失而导致网络训练非常困难,模型性能会趋于饱和甚至开始下降㊂基于此问题,R e s n e t(残差网络)模块被提出来解决梯度消失的问题,残差块示意图如图4所示㊂R e s n e t 基于这样一种单一的假设:x 到H (x)的直接映射是难以学习的㊂因此,一种修正方法是:不再学习x 到H (x)的基本映射关系,而是学习两者之间的差异,也就是残差(r e s i d u a l )㊂假设残差为F (x )=H (x )-x,那么网络不会直接学习H (x )了,而是学习F (x )+x ,这就是图5残差块的网络学习思想㊂在使用R e s n e t 之前,深度神经网络常常会有梯度消失的困扰,即来自误差函数的梯度信号会在反向传播回更早的层时指数级地下降,本质上讲即误差信号回到更早的层时,会变得非常小以至于网络无法进行学习优化㊂而R e s n e t 的梯度信号可以直接通过捷径连接回到更早的层,在网络深度非常深时,网络模型的表现依然良好㊂基于图5中的残差网络结构,本文提出了应用于声信敬请登录网站在线投稿(t o u ga o .m e s n e t .c o m.c n )2021年第1期25图5 直连卷积网络与残差网络结构对比号分类识别的深度残差网络A u d i o R e s n e t,如图6所示㊂图6 深度残差网络结构l o g m e l 声谱图数据的f r e q u e n c y 轴为128维,考虑到声信号的高频和低频存在的特征不同,在A u d i o R e s n e t 中分出了两条并行的数据训练路径,将声数据分割为低频段和高频段,这样,第0~63维由一个有11个卷积层的残差网络处理,第64~127维由另一个相同结构的残差网络处理,残差网络中的这些卷积的卷积核大小都是3ˑ3,p a d d i n g 设为 s a m e ,两个通道被连接起来,回复到128个频率维度㊂然后由两个1ˑ1的卷积层操作㊂第二层减少为类的数量,然后是批处理规范化层㊁全局平均池化层和s o f t m a x ㊂最后的两个1ˑ1的卷积层有效充当了一个两层非卷积神经网络,对每个通道的贡献进行加权,对声信号进行分类㊂3 实验验证为了检验A u d i o R e s n e t 的性能,分别在D C A S E 2019数据集和U r b a n S o u n d 8K 数据集上训练网络,两个大型公开数据集被广泛用于验证环境声分类问题解决方案的质量㊂D C A SE 2019数据集由14400个短(10s )的8种不同场景的现场录音组成㊂U r b a n S o u n d 8K 数据集由8732个短(不到4s)的城市场景音,这些数据是从免费在线声音中提取的㊂利用l i b r o s a 包中的l o g me l 方法对声音数据作特征预提取,并使用二阶差分对预提取特征进行处理,增加声信号动态信息,一阶差分计算公式如式下(计算两次即为二阶差分):d t =ðNn =1n (c t +n -c t -n )2ðNn =1n2(1) 最终,经过预处理后得到D C A S E 2019数据集为(14400,128,461,6),U r b a n S o u n d 8K 数据集为(8732,128,180,6)㊂将数据集按照7ʒ3的比例分为训练集和测试集㊂在训练中使用了随机梯度下降,批处理量为32,损失函数为c a t e g o r i c a l _c r o s s e n t r o p y ,优化器使用S G D ,e po c h 次数设为500次,使用学习率重置调度方法,在3㊁8㊁18㊁38㊁128和256次迭代后将学习率重置为最大值0.1,然后按照余弦函数方式衰减到0.00001,防止模型陷入局部最优解㊂模型训练情况如图7~图10所示,可以看出,在D C A S E 2019训练集上训练A u d i o R e s n e t,其训练精准度最终稳定在0.90,损失值减小到0.4,在300次迭代时收敛;在U r b a n S o u n d 8K 训练集上训练A u d i o R e s n e t,分类精准度达到0.88,损失值减小到0.24,在250次迭代时收敛㊂图7 训练损失曲线图(D C A S E 2019数据集)图8 训练精度曲线图(D C A S E 2019数据集)图9 训练损失曲线图(U r b a n S o u n d 8K 数据集)图10 训练精度曲线图(U r b a n S o u n d 8K 数据集)26M i c r o c o n t r o l l e r s &E m b e d d e d S ys t e m s 2021年第1期w w w .m e s n e t .c o m .c n在D C A S E 2019测试集和U r b a n S o u n d 8K 测试集上,模型的识别精度如表1和表2所列,并且与一些最新的方法进行了对比㊂表1 精度比较(D C A S E 2019)模 型测试精度(D C A S E )A u d i o R e s n e t80.2%R e s N e t l i k e m o d e l [14]76.6%B a s e l i n e s ys t e m [15]62.5%表2 与其他方法的精度比较模 型测试精度(U r b a n S o u n d 8K )A u d i o R e s n e t76.4%U n s u p e r v i s e d f e a t u r e l e a r n i n g[16]73.6%B a s e l i n e s ys t e m [17]68%从表中可以看到,与其他先进的分类方法相比,本文提出的A u d i o R e s ne t 模型在分类精度上有较大提高㊂4 结 语本文针对声源目标分类中小样本训练时分类模型性能不佳的问题,使用深度学习方法对不同声源发出的声音数据进行分类,使用l o g me l 声谱图特征作为特征预提取方法,采用基于R e s n e t 网络结构的分类模型对预提取特征数据进行分类处理,建立了识别效果良好的深度学习声信号分类模型A u d i o R e s n e t ㊂该模型性能在D C A S E 2019和U r b a n S o u n d 8K 数据集上得到了验证,实现了良好的效果,在声源探测领域具有一定的工程应用价值㊂参考文献[1]I S N A R D V ,C HA S T R E S V ,V I A U DD E L MO N I ,e t a l .T h e t i m e c o u r s e o f a u d i t o r y r e c o g n i t i o n m e a s u r e d w i t h r a pi d s e q u e n c e s o f s h o r t n a t u r a l s o u n d s [J ].S c i e n t i f i c R e po r t s ,2019,9(1):8005.[2]樊新海,石文雷,张传清.基于V M D 多尺度熵和A B C S V M 的装甲车辆识别[J ].装甲兵工程学院学报,2018,32(6):6873.[3]孙国强,樊新海,石文雷.基于M F C C 和支持向量机的装甲车辆识别研究[J ].国外电子测量技术,2017,36(10):3135.[4]R U B E N G O N Z A L E ZH E R N A N D E Z F ,P A S T O RS A N C H E Z F E R N A N D E Z L ,S U A R E Z G U E R R A S ,e ta l .M a r i n e m a mm a l s o u n d c l a s s i f i c a t i o nb a s e d o n a p a r a l l e lr e c o g n i t i o n m o d e l a n d o c t a v e a n a l y s i s [J ].A p pl i e d A c o u s t i c s ,2017(119):1728.[5]S A N C H E Z F E R N A N D E Z L P ,S A N C H E Z P E R E Z L A ,C A R B A J A L H E R N A NDE Z J J ,e t a l .A i r c r a f t C l a s s i f i c a t i o na n d A c o u s t i c I m pa c t E s t i m a t i o n B a s e d o n R e a l T i m e T a k e o f f N o i s e M e a s u r e m e n t s [J ].N e u r a l P r o c e s s i n g Le t t e r s ,2013,38(2):239259.[6]陈心昭.噪声源识别技术的进展[J ].合肥工业大学学报(自然科学版),2009,32(5):609614.[7]竺乐庆,张真.基于M F C C 和GMM 的昆虫声音自动识别[J ].昆虫学报,2012,55(4):466471.[8]石文雷,樊新海,张传清.基于频谱动态特征和C S S VM 的装甲车辆识别[J ].计算机应用,2018,38(S 1):4447,72.[9]董语诗,时浏艺,丁一坤.基于HMM 的声场景分类[J ].信息化研究,2018,44(3):3947,52.[10]中兴通讯采用英特尔F P G A 在深度学习上取得性能突破[J ].单片机与嵌入式系统应用,2017,17(3):88.[11]庞悦,赵威,张雅楠,等.基于深度学习的L S T M 的交通流量预测[J ].单片机与嵌入式系统应用,2019,19(3):7275.[12]王枫,陈小平.C N N 深度学习的验证码识别及A n d r o i d 平台移植[J ].单片机与嵌入式系统应用,2019,19(7):2022,73.[13]P I R O T T A E ,M E R C HA N T N D ,T HOM P S O N P M ,e t a l .Q u a n t i f y i n g th e e f f e c t o f b o a t d i s t u r b a n c e o n b o t t l e n o s e d o l -p h i n f o r a g i n g a c t i v i t y[J ].B i o l C o n s e r v ,2015(181):8289.[14]N g u y e n T ,P e r n k o pf F .A c o u s t i c S c e n e C l a s s i f i c a t i o n w i t h M i s m a t c h e d R e c o r d i ng D e v i c e s U s i n g M i x t u r e o f E x pe r t s L a ye r [C ]//2019I E E E I n t e r n a t i o n a l C o nf e r e n c e o n M u l t i -m e d i a a n d E x po (I C M E ),2019.[15]M e s a r o s A ,H e i t t o l a T ,V i r t a n e n T.A m u l t i d e v i c e d a t a s e tf o r u r b a n a c o u s t i c s c e n e c l a s s i f i c a t i o n [J ].2018,a r X i v :1807.09840.[16]S a l a m o n J ,J a c o b y C ,B e l l o J P .A D a t a s e t a n d T a x o n o m yf o r U r b a n S o u n d R e s e a r c h [C ]//a c m I n t e r n a t i o n a l C o n f e r e n c eo n M u l t i m e d i a ,2014.[17]S A L AMO N J ,J A C O B Y C ,B E L L O J P .A D a t a s e t a n d T a x -o n o m y f o r U r b a n S o u n d R e se a r c h [C ]//p r o c e e d i n gs o f t h e a c m I n t e r n a t i o n a l C o n f e r e n c e o n M u l t i m e d i a ,2014.王鹏程(硕士研究生),主要研究方向为信息探测与处理㊂(责任编辑:薛士然 收稿日期:2020-07-20) [2]胡振波.R I S C V 架构与嵌入式开发快速入门[M ].北京:人民邮电出版社,2019.[3]何小庆.3种物联网操作系统分析与比较[J ].微纳电子与智能制造,2020(3).[4]D A V I D P A T T E R S O N ,A N D R E W W A T E R M A N.R I S CV手册:一本开源指令集的指南,2018.[5]U s i n g Fr e e R T O S o n R I S C V M i c r o c o n t r o l l e r s [E B /O L ].[202009].h t t p s ://w w w.f r e e r t o s .o r g /U s i n g Fr e e R T O S o n R I S C V.h t m l .[6]J i m C o o l i n g .R e a l t i m e O p e r a t i n g S ys t e m s B o o k 2T h e P r a c t i c e [M ].M a r k f i e l d :L i n d e n t r e e A s s o c i a t e s ,2017.(责任编辑:芦潇静 收稿日期:2020-09-10)。
盲人智能避障眼镜的设计与开发
第37卷 第4期 福 建 电 脑 Vol.37 No.42021年4月Journal of Fujian ComputerApr. 2021———————————————本文得到宁波职业技术学院人才引进科研项目(No. RC201808)资助。
但雨芳(通信作者)，女，1984年生，主要研究领域为模式识别和深度学习。
E-mail: ****************.cn 。
史凯凯，男，2000年生，主要研究领域为人工智能。
李伟仁，男，1998年生，主要研究领域为人工智能。
王添烽，男，2000年生，主要研究领域为人工智能。
朱亨，男，2000年生，主要研究领域为人工智能。
盲人智能避障眼镜的设计与开发但雨芳 史凯凯 李伟仁 王添烽 朱亨(宁波职业技术学院电子信息工程学院 浙江 宁波 315800)摘 要 现今的导盲仪与导盲犬因存在诸多未知的安全因素，仍不被接收。
因此，本文设计并开发了一种采用深度学习模型YOLO-v3的盲人智能避障眼镜，利用opencv 获取截取图片，利用改进的YOLO-v3模型进行学习和捕捉需要检测的目标，采取3D 重构原理将截取图片进行分割处理、重映射、灰度化等一系列操作，并结合双目视觉进行目标测距，以语音告知盲人具体目标和距离。
实验结果表明，该方法比最新的目标检测与分类准确率更高，比市面上的盲人避障眼镜更实用于大众。
关键词 目标检测；目标分类；盲人眼镜；模糊聚类中图法分类号 TP183 DOI:10.16707/ki.fjpc.2021.04.026Design and Development of Blind Intelligent Obstacle Avoidance GlassesDAN Y ufang, SHI Kaikai, LI Weiren , WANG Tianfeng , ZHU Heng(School of Electronics and Information Engineering, Ningbo Polytechnic, Ningbo, China, 315800)Abstract Today's guide device and guide dog are still not accepted because of many unknown safety factors. Therefore, this paper designs and develops a kind of blind intelligent obstacle avoidance glasses based on the deep learning model Yolo V3, and uses OpenCV to obtain the captured pictures to improve Yolo V3 model, which is used to learn and capture the target that needs to be detected. Based on the principle of 3D reconstruction, a series of operations such as segmentation, remapping and graying of the intercepted image are carried out. Combined with binocular vision, target ranging is carried out, and voice broadcast is used to inform the blind of the specific target and distance. Experimental results show that this method has higher accuracy than the latest target detection and classification, and is more practical for the public than some other blind obstacle avoidance glasses on the market.Keywords Object Detection; Object Classification; Blind Glasses; Fuzzy Clustering1 引言据全国残疾人抽样调查显示，截至2019年，我国约有1700万盲人，占全球盲人总数的五分之一，是世界上盲人数量最多的国家。
基于多级残差网络的环境声音分类方法
ISSN1004⁃9037，CODENSCYCE4JournalofDataAcquisitionandProcessingVol.36，No.5，Sep.2021，pp.960-968DOI：10.16337/j.1004⁃9037.2021.05.011Ⓒ2021byJournalofDataAcquisitionandProcessinghttp：//sjcj.nuaa.edu.cnE⁃mail：sjcj@nuaa.edu.cnTel/Fax：+86⁃025⁃84892742
基于多级残差网络的环境声音分类方法曾金芳，李友明，杨恢先，张钰，胡雅欣（湘潭大学物理与光电工程学院，湘潭411105）摘要：为了对环境声音进行更好的识别和分类，提出了基于多级残差网络（Multilevelresidualnetwork，Mul⁃EnvResNet）的环境声音分类方法。对声音事件进行时标和基频压扩之后，提取其梅尔频
率倒谱系数（Mel⁃frequencycepstralcoefficients，MFCCs），以及它们的差分作为特征参数送入Mul⁃EnvResNet对声音事件进行分类。实验数据集采用ESC⁃50，将Mul⁃EnvResNet模型与端到端的卷积神
经网络（EnvNet）、基于注意力机制的循环神经网络（Attentionbasedconvolutionalrecurrentneuralnetwork，ACRNN），以及受限卷积玻尔兹曼机的无监督滤波器组模型（ConvolutionalrestrictedBoltzmannmachine，ConvRBM）进行对比实验。实验结果表明，Mul⁃EnvResNet取得了89.32%的最佳分类准确率，相较上述3种模型在分类准确率上分别有18.32%、3.22%、2.82%的提升，相较于其他的声音分类方法也均有明显的优势。关键词：环境声音分类；多级残差网络；时标压扩；基频压扩中图分类号：TN912文献标志码：A
基于数据增广的声学场景分类
Ｍｅｌ 声谱图如图 ２ 所示，由图 ２ 可以看出，各类别的 Ｍｅｌ
声谱图呈现不同特点。
图 ３ Ｍｅｌ 声谱 Ｔ⁃ＳＮＥ 降维分布
２ 数据增广技术
在没有足够训练数据的情况下，数据增广技术可
以起扩充数据集的作用，缓解模型易于发生过度拟合
的同时，最大限度地利用样本中的有效信息。 在音频
（１． 海装重大专项装备项目管理中心， 北京 １０００７１；２． 江苏自动化研究所， 江苏 连云港 ２２２０６１）
摘 要：声学场景分类是计算机听觉领域的热点方向之一，相比计算机视觉，特定场景下音频数据的收集和标注成
本相对较高，如何利用有限的声学场景音频获得较高的分类准确率成为当前研究的重点内容。 利用深度学习技术，
２． Ｊｉａｎｇｓｕ Ａｕｔｏｍａｔｉｏｎ Ｒｅｓｅａｒｃｈ Ｉｎｓｔｉｔｕｔｅ， Ｌｉａｎｙｕｎｇａｎｇ ２２２０６１， Ｃｈｉｎａ）
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ａｎｄ ｄｉｓｔｒｉｂｕｔｉｏｎ ｏｂｓｅｒｖａｔｉｏｎ ｏｆ Ｍｅｌ Ｓｐｅｃｔｒｏｇｒａｍ．
Ａｃｏｕｓｔｉｃ Ｓｃｅｎｅ Ｃｌａｓｓｉｆｉｃａｔｉｏｎ Ｂａｓｅｄ ｏｎ Ｄａｔａ Ａｕｇｍｅｎｔ Ｔｅｃｈｎｏｌｏｇｙ
专业英语八级考试-TEM
专业英语八级考试:TEM专业英语八级考试:TEM-8Exercise6专业英语八级考试:TEM-8Exercise6part one listening comprehension(40 min.)in section a, b and c you will hear everything only once. listen carefully and then answer the questions that follow. mark the correct response to each question on the colored answer sheet.section a talkquestion 1 to 5 refer to the talk in this section. at the end of the talk you will be given 15 seconds to answer each of the following five questions.now listen to the talk.1. what is the percentage that the mediterranean has of the world s sea surface?a. 1.5%b. 1%c. 2%d. 3%正确答案是2. which parts of the mediterranean are the worst?a. the coast between barcelona and greek.b. the tyrrhenrian sea between sardinia, sicily and the west italian coast.c. the israeli/lebanon coast.d. cannes and tel aviv.正确答案是3. according to the speaker, the dirtiest rivers are ____a. the llobregat in spain.b. the adige and the tiber in italy.c. the nile.d. the po and the phone.正确答案是4. in the next twenty years, the number of holiday-makers is expected to be ____a. 100 million.b. 150 million.c. 200 million.d. 300 million.正确答案是5. the purpose of the article is ____a. to warn that the pollution of the mediterranean is hardly inevitable.b. to provide specific information about the pollution of the mediterranean.c. to warn holiday-makers of the risks they will run if they tour the mediterranean shores.d. to show that the mediterranean has become another dead sea.正确答案是section b interviewquestion 6 to 10 are based on an interview. at the end of the interview you will be given 15 seconds to answer each of the following question.now listen to the interview.6. who are the speakers?a. salesmen.b. editors.c. cooks.d. advertising agents.正确答案是7. what products are they talking about?a. kitchen.b. deep-freezer.c. mobility units.d. cake mixer.正确答案是8. what is the relationship between the two speakers?a. employer and employee.b. salesman and customer.c. advertiser and customer.d. colleagues.正确答案是9. how is the kitchen different from all other kitchens on the market?a. it is easier to clean and repair.b. it is non-fixed and flexible.c. all its units are of the same height.d. its chopping board is nearer to the sink.正确答案是10. what can you infer from the conversation?a. terry knows less about kitchen than joyce.b. joyce knows more about kitchen than terry.c. terry knows more about kitchen than joyce.d. terry knows as much about the kitchen as joyce.正确答案是section c news broadcastquestion 11 is based on the following news. at the end of the news item, you will be given 15 seconds to answer the question.now listen to the news.11. why did the united nations ask for a week-longcease-fire?a. to arrange peace talks.b. to get out the injured civilians.c. to offer medicines.d. to withdraw peace keepers.正确答案是questions 12 and 13 are based on the following news. at the end of the news item you will be given 30 seconds to the questions.now listen to the news.12. what has president clinton strongly criticized?a. haitian police force s incapability.b. the violence carried out by haitian police.c. the haitian people s in-cooperation.d. the haiti s military government.正确答案是13. which of the following statements is not true?a. haiti s police forces carried out violence in haiti.b. american military will replace haiti police in haiti.c. thousands of american military police are being deployed in haiti.d. no further clashes were reported in haiti wednesday.正确答案是questions 14 and 15 are based on the following news. at the end of the news item you will be given 30 seconds to answer the questions.now listen to the news.14. how much money will mozambique be provided by the undp?a. 190,000,000.b. 19,000,000.c. 93,000,000.d. 930,000,000.正确答案是15. how many cases of the disease cholera were reported in six months?a. 900,000.b. 90,000.c. 19,000.d. 9,000.正确答案是section d note-taking gap-fillingin this section you will hear a mini-lecture. you will hear the lecture only once. while listening to the lecture, take notes on the important points. your notes will not be marked, but you will need them to complete a 15-minute gap-filling task on answer sheet one after the mini lecture. use the blank sheet for note-taking.answer sheet onefill in each of the gaps with one suitable word. you may refer to your notes. make sure the word you fill in is both grammatically and semantically acceptable.sleepwalkingthe strange behavior of sleepwalkers have puzzled police, perplexed scientist and fascinated writers for centuries. thereis an early (16) record of a somnambulist who wrote a novel in his sleep. the world s (17) sleepwalker was supposed to have been an indian, who walked 16 miles along a dangerous road. sleepwalking is a (18) reality. what is certain about sleepwalking is that it is a symptom of (19), which is a usually the (20) result of guilt, nervousness, worry or some other emotional (21).one of the most common beliefs of sleepwalking is that it is dangerous or even (22) to waken the sleepwalkers. but this is one of the two mistaken beliefs. the other is that sleepwalkers are (23) to injury. authorities on sleepwalking think that people will not do anything against their own moral (24). they also think sleepwalking itself is nothing to become alarmed about, but what may be very serious are the (25) that causes it.part ii proofreading error correction (15 min.)the following passage contains ten errors .each line contains a maximum of one error. in each case only one wordis involved. you should proofread the passage and correct it in the following way:for a wrong word, underline the wrong word and write the correct one in the blank provided at the end ry museum wants an exhibition, it must often build it.(3)exhibitthe german poet and polymath johann wolfgang von goethepondered the question of how organisms develop in his scientificstudies of form and structure immature plants and animals, a field hefound and named morphology. his search for a single basic body plan(26)across all life-forms led him to think about the prevalence of repeating(27)segments in body structures. the spinal columns of fish, reptiles,(28)birds and mammals, for instance, all are made of long strings of(29)repeated vertebrae. among invertebrates the growth of virtuallyidentical segments is how striking: in earthworms, for example, even(30)internal organs are repeated in serial segments. likewise, theabdomen of flies and other insects are segmented, as are the(31)successive wormlike articulations in crabs, shrimps and othercrustaceans. to goethe the evidence suggested that nature takes abuilding-block approach to generate life, repeating a basic element(32)again and again to arrive at a complicated organism. theonly glaring(33)hole he could see in the theory was the apparent lack of sort of(34)segmentation in the vertebrate heads. in 1790 he hypothesized that(35)spinal vertebrate is modified during the development to form the skull.part iii reading comprehension (40 min.)section a: reading comprehension (30 min.)in this section there are four reading passages followed by fifteen multiple-choice questions. read the passages and then mark your answers on your answer sheet.text ait is now june 1567. two months previously the explosion to kirk o field, which awakened edinburgh, startled courts as far away as rome. in the flash of gunpowder, england, france, and the holy see received a pin-sharp picture of scotlandwhich shook even the hardened nerves of the sixteenth century. the queen s consort murdered. the queen implicated. the earl of bothwell more than implicated. talk of love between them. no one minded murder in the sixteenth century; it was a good old scottish custom, and elsewhere it was recognized as a political expedient. no one regretted the end of the miserable darnley, a poor drunken coward; but what stirred the conscience of the age was the news that the queen of scotland was ready to bring her husband s murderer not to the gallows but to her bed. even elizabeth, who was not mary s best friend, became human and wrote to her "dear cousin" imploring her to see justice done. but no: mary queen of scots was fated to think the cup of sorrow to the very end.has any woman lived more violently, yet more mysteriously -- for we shall never know her heart -- than mary in the last six months before carberry hill? there is the amazing evening in edinburgh, when, surrounded by armed men, the lords of scotland sign bothwell s document naming himself the queen s suitor. there is the astonishing holdup outside edinburgh with the queen. what can we make of it? was she his victim or did he fly to his brutality as to a stronghold? there isthe silent ten-day honeymoon at holyrood palace in edinburgh; the angry murmur of the common people. then, as if the drama had not been exhausted, we see mary in flight, riding through the night disguised as a boy. she and her strong man ride out to meet her nobles at carberry hill. there is no battle; bothwell offers to fight any man of equal rank in the opposing army. even hang fire.marry will not hear of bothwell s fighting. why? surely because she loves him? she learns that the nobles are resolved on his death. her heart is set on securing his escape. they say farewell, in great pain and anguish and with many long kisses.the lords escort her to edinburgh, where a man cries out for her death. there is a terrible glimpse of her at a window, her hair about her shoulders, crying and appealing to the crowds to save her. the next day she is taken to loch leven, to a castle on an island. mary s long captivity had begun.36. mary s husband, lord darnley, had been ____a. killed in the explosion at kirk o field.b. told to wake up all the people of edingburgh.c. startled by the explosion at kirk o field.d. stabbed by the people of edingburgh.正确答案是37. it was reported all over europe that the queen of scotland ____a. knew nothing about the murder but wanted to marry bothwell.b. knew about the murder, which bothewell had organized.c. had carried the gunpowder, because she hated her husband.d. had been asked by bothwell to murder darnley.正确答案是38. the author says that we shall never understand ____a. why mary was such an unlucky and unhappy woman.b. why mary was violent and mysterious.c. mary s motives for her action.d. the reason why mary fell in love with bothwell.正确答案是39. mary was taken back to edinburgh by the nobles and____a. put to death by her own people.b. rescued by the people of edinburgh.c. thrown straight into prison.d. later taken to a very secure prison.正确答案是text b"scotland yard s top fingerprint expert, detective chief superintendent gerald lambourne had a request from the british museum s prehistoric department to force his magnifying glass on a mystery somewhat outside my usual beat."this was not a question of whodunit, but who was it. the blunt instruments he pored over were the antlers of red deer, dated by radio-carbon examination as being up to 5 000 years old. they were used as mining picks by neolithic man to hack flints and chalk, and the fingerprints he was looking for were of our remote ancestors who had last wielded them.the antlers were unearthed in july during the british museum s five-year-long excavation at grime s graves, neartherford, norfolk, a 93-acre site containing more than 600 vertical shafts in the chalk some 40 feet deep. from artifacts found in many parts of britain it is evident that flint was extensively used by neolithic man as he slowly learned how to farm land in the period from 3 000 to 1 500 b.c.flint was especially used for ax-heads to clear forests for agriculture, and the quality of the flint on the norfolk site suggests that the miners there were kept busy with many orders.what excited mr. g. de g. sieveking, the museum s deputy director of the excavations, was the dried mud still sticking to some of them. "our deduction is that the miners coated the base of the antlers with mud so that they could get a better grip," he says. "the exciting possibility was that fingerprints left in this mud might at last identify as individuals as people who have left few relics, who could not read or write, but who may have had much more intelligence than had been supposed in the past."chief superintendent lambourne, who four years age had "assisted" the british museum by taking the fingerprints of a 4000-year-old egyptian mummy, spent two hours last week examining about 50 antlers. on some he found minutes marks indicating a human hand--that part of the hand just below the fingers where most pressure would be brought to bear the wielding of a pick.after 25 years specialization in the yard s fingerprints department, chief superintendent lambourne knows all about ridge structures--technically known as the "tri-radiate section".it was his identification of that part of the hand that helped to incriminate some of the great train robbers. in 1995 he discovered similar handprints on a bloodstained tee-maker on a golf-course where a woman had been brutally murdered. they eventually led to the killer, after 4 065 handprints had been taken.chief superintendent lamboure had agreed to visit the norfolk site during further excavations next summer, when it is hoped that further hand-marked antlers will come to light. but he is cautious about the historic significance of his findings."finger prints and hand prints are unique to eachindividual but they can tell nothing about the age, physical characteristics, even sex of the person who left them," he says. "even the finger prints of gorilla could be mistaken for those of a man. but if a number of imprinted antlers are recovered from given shafts on this site i could at least determine which antlers were handled by the same man, and from there might be deduced the number of miners employed in a team.""as as indication of intelligence i might determine which way up the miners held the antlers and how they wielded them."to mr. sieveking and his museum colleagues any such findings will added to their dossier of what might appear to the layman as trivial and unrelated facts but from which might emerge one day an impressive new image of our remote ancestors.40. what was the aim of the investigation referred to in the passage?a. to provide some kind of identification of a few neolithic men.b. to find out more about the period when the antlerswere used.c. to discover more about the purpose of the antlers.d. to learn more about the types of men who used them.正确答案是41. what had been the principal use of the antlers?a. to obtain the material for useful tools.b. to prepare the fields for cultivation.c. to help in removing trees and bushes so that land could be cultivated.d. to make many objects useful in everyday life.正确答案是42. the idea that mud was applied to the antlers deliberately was ____a. the result of an inspired guess.b. a possibility based on reasoning from facts.c. an obvious conclusion.d. a conclusion based on other similar cases.正确答案是43. the museum s deputy director is very interested in theprints because ____a. useful facts about this remote period can be learned from them.b. they are valuable records of intelligent but illiterate people.c. very few objects of this remote period have been found.d. the antlers serve as a link with actual people who lived at that time.正确答案是text cthe conflict between good and evil is a common theme running through the great literature and drama of the world, from the time of ancient greeks to all the present. the principle that conflict is the heart of dramatic action when illustrated by concrete examples, almost always turn up some aspect of the struggle between good and evil.the idea that there is neither good not evil -- in any absolute moral or religious sense -- is widespread in our times. there are various relativistic and behaviorist standards of ethics. if these standards even admit the distinction between good and evil, it is as a relative matter and not as whirlwind ofchoices that lies at the center of living. in any such state of mind, conflict can at best, be only a petty matter, lacking true university. the acts of the evildoer and of the virtuous man alike become dramatically neutralized. imagine the reduced effect of crime and punishment or the brothers karamazoc had dostoevsky thought that good and evil, as portrayed in those books, were wholly relative, and if he had had no conviction about them.you can t have a vital literature if you ignore or shun evil. what you get then is the world of pollyanna, goody-goody in place of the good. cry, the beloved country is a great and dramatic novel because alan paton, in addition to being a skilled workman, sees with clear eyes both good and evil, differentiates them, pitches them into conflict with each other, and takes sides. he sees that the native boy absalom kumalo, who has murdered, cannot be judged justly without taking into account the environment that has had part in shaping him. but paton sees, too, that absalom the individual, not society the abstraction, committed the act and is responsible for it. mr. paton understand mercy. he knows that this precious thing is not evoked by sentimental impulse, but by a searching examination of the realities of human action. mercy follows ajudgment; it does not precede it.one of the novels by the talented paul bowles, let it down is full of motion, full of sensational depravities, and is a crashing bore. the book recognizes no good, admits no evil, and is coldly indifferent to the moral behavior of its characters. it is a long shrug. such a view of life is non-dramatic and negates the vital essence of drama.44. in our age, according to the author, a standpoint often taken in the area of ethics is the ____a. relativistic view of morals.b. greater concern with religion.c. emphasis on evil.d. greater concern with universals.正确答案是45. the author believes that in great literature, as in life, food ad evil are ____a. relative terms.b. to be ignored.c. constantly in conflict.d. dramatically neutralized.正确答案是46. when the author uses the expression "it is a long shrug" in referring to bowles s book, he is commenting on the ____a. length of the novel.b. indifference to the moral behavior of the characters.c. monotony of the story.d. sensational depravities of the book.正确答案是47. in the opinion of the author, cry, the beloved country isa great and dramatic novel because of paton s ____a. insight into human behavior.b. behavioristic beliefs.c. treatment of good and evil as abstractions.d. willingness to make moral judgments.正确答案是text dalthough boud and i had fought and quarreled unceasingly throughout childhood, by the time she waseighteen and i was fifteen we had, surprisingly, become good friends. boud had grown from a giant-sized schoolgirl into a huge and rather alarming debutante. she was generally out to shock, and in this she succeeded. i applauded her outrages, roared when she stole some writing paper from buckingham palace and wrote to all her friends on it, cheered when she took her pet rat to dances.but she was bored and restless. she was casting about for something more exciting, more intriguing than the london season offered -- something forbidden by our parents.diana s house seemed like a good beginning, for we had been forbidden to visit her when, after a few years of marriage, she and bryan were divorced. we had been excluded from the dreadful row that followed their separation; we knew only that unutterable shame and disgrace had been brought by diana on the family. needless to say, this only made diana more glamorous in our eyes.bound began to visit diana and at her house she met sir oswald mosley, whom diana later married. mosley s career had led him through the conservative party, the labor party and the new party, a venture that had lasted only a year despitebacking by the daily mail. he was now busily engaged in organizing the british union of fascists, which boud immediately joined."don t you long to join too, decca? it s such fun," she begged, waving her brand new black shirt at me."shouldn t think of it. i hate the beastly fascists. if you are going to be one, i m going to be a communist, so there!"in fact, this declaration was something more than a mere automatic taking of opposite sides to boud. the little i knew about the fascists repelled me. i took out a subscription to the daily worker, bought volumes of communist literature and literature i supposed to be communist, put up some home-made hammer and sickle flags and bought a small bust of lenin for a shilling in a second-hand shop. my communist library was catholic indeed, and many of the authors would no doubt have been amazed to find themselves included. it included not only works by lenin stalin but also by bertrand russell, the webbs and george bernard shaw. the result of all this was that i greatly increased my knowledge of modernenglish literature and progressive thought.we divided our room down the middle, and each decorated her own side with flags and photographs, sometimes having pitched battles with books and records until nanny came in to tell us to stop the noise. yet, once, we teamed up in our own version of the united front; we each stole five founds from the conservative father to send to our respective parties.48. when her sister shocked people, the author was ____a. horrified and told her to stop.b. jealous of her sister s anger and theft.c. an approving and encouraging audience.d. very anxious to do the same sort of thing.正确答案是49. the author decided to be a communist because she ____a. only wanted to annoy her sister, who had joined the fascists.b. was already fully in sympathy with revolutionary view.c. did not like what little she knew about fascism.d. already felt a sympathy with its ideas and was now pushed into declaring them.正确答案是50. the two sisters ____a. hated each other because they disagreed on politics.b. still fought often but had moments of forgetting politics.c. came to physical blows over their different politics.d. submerged their personal differences in their political quarrels.正确答案是section b skimming and scanning (10 min.)in this section there are seven passage followed by ten multiple-choice questions. skim or scan them as required and then mark your answers on your answer sheet.text efirst read the question.51. with what topic is the passage primarily concerned?a. the founding of congress.b. the congressional process of making laws.c. the division of power in congress.d. the factors involved in the election of congressional members.正确答案是now go though text e quickly to answer question 51.the constitutional requirements for holding congressional office in the united states are few and simple. they include age (twenty-five years of age for the house of representatives, thirty for the senate); citizenship (seven years for the house, nine years for the senate); and residency in the state from which the officeholder is elected. thus, the constitutional gateways to congressional office holding are fairly wide.even these minimal requirements, however, sometimes arouse controversy. during the 1960 s and 1970 s, when people of the post-second war "baby boom" reached maturity and the twenty-sixth amendment (permitting eighteen year olds to vote) was ratified, unsuccessful efforts were made to lower the eligible age for senators and representatives.because of americas geographic mobility, residency sometimes is an issue. voters normally prefer candidates with long-standing ties to their states of districts. in his 1978 reelection campaign, for instance, texas senator john tower effectively accused his opponent, representative robert krueger, of having spent most of his life "overseas or in the east" studying or teaching -- a charge taken seriously in texas. well-known candidates sometimes succeed without such ties. new york voters elected to the senate robert f. kennedy (1965-1968) and daniel patrick moynihan (1977) even though each had spent much of his life elsewhere. while members of the house of representatives are not bound to live in the district from which they are elected, most do so prior to their election.in the seat, the "one person, one vote" rule does not apply. article i of the constitution assures each state, regardless of population, two senate seats, and article v guarantees that this equal representation cannot be taken away without the state s consent. the founders stipulated that senators be designed by their respective state legislatures rather than by the voters themselves. thus, the senate was designed to add stability,wisdom, and forbearance to the action of the popularly elected house. this distinction between the two houses was eroded by the seventeenth amendment (1913), which provided for the direct population election of senators.text ffirst read the questions.52. which of the following is the best title for this passage?a. a long flight.b. women in aviation history.c. dangers faced by pilots.d. women spectators.正确答案是now go though text f quickly and answer question 52.the sooner had the first intrepid male aviator safely returned to earth, it seemed that women, too, were smitten by an urge to fly. from mere spectators they became willing passengers and finally pilots in their own right, plotting their skills and daring line against the hazards of the air and the skepticism of their male counterparts. in doing so, theyenlarged the traditional bounds of a women s world, won for their sex a new sense of competence and achievement, and contributed handsomely to the progress of aviation.but recognition of their abilities did not come easily. "men do not believe us capable." the famed aviator amelia earhart once remarked to friend "because we are women, seldom are we trusted to do an efficient job." indeed old attitudes died hard: when charles lindbergh visited the soviet union in 1938 with his wife, anne -- herself a pilot and gifted proponent of aviation -- was astonished to discover both men and women flying in the soviet air force.such conventional wisdom made it difficult for women to raise money for the up-to-date equipment they needed to compete on an equal basis with men. yet compete they did, and often they triumphed dandily despite the odds.ruth law, whose 590-mile flight from chicago to hornell, new york, set a new nonstop distance record in 1918, exemplified the resourcefulness and grit demanded of any woman who wanted to fly. and when she addressed the aeroclub of america after completing her historic journey, her plainspoken words testified to a universal human motivation that was unaffected by gender: "my flight was done with no expectation of reward," she declared, "just for the love of accomplishment."text gfirst read the following question.53. what is the main idea of the passage?a. bees communicate with each other by dancing.b. animals have internal steering devices.c. the sun is necessary for animal navigation.d. the earth s magnetic fields guide pigeons home.正确答案是now go through text g quickly and answer question 53.researchers have found that migrating animals use a variety of inner compasses to help them navigate. some steer by the position of the sun. others navigate by the stars. some use the sun as their guide during the day, and then switch to star navigation by night. one study shows that the homing pigeon uses the earth s magnetic fields as a guide in finding。
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DiscardedMusic SegmentsDiscardedSilence SegmentsM,MS,S,TAudio TypeClassificationAudio StreamCodingRelabelling
Discard Music
Gender dependent
Phone Recognition
Discard Silence
Relabelling
Final Segments
Splitting and
Clustering
Clustering
Smoothing and
Adapt Models
Using MLLRTagged Segments