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EVALUATION OF MUSICAL FEATURES FOR EMOTION CLASSIFICATION

EVALUATION OF MUSICAL FEATURES FOR EMOTION CLASSIFICATION
EVALUATION OF MUSICAL FEATURES FOR EMOTION CLASSIFICATION

EV ALUATION OF MUSICAL FEATURES FOR EMOTION

CLASSIFICATION

Yading Song,Simon Dixon,Marcus Pearce

Centre for Digital Music,Queen Mary University of London

{yading.song,simon.dixon,marcus.pearce}@https://www.doczj.com/doc/339808272.html,

ABSTRACT

Because music conveys and evokes feelings,a wealth of research has been performed on music emotion recogni-tion.Previous research has shown that musical mood is linked to features based on rhythm,timbre,spectrum and lyrics.For example,sad music correlates with slow tempo, while happy music is generally faster.However,only lim-ited success has been obtained in learning automatic classi-?ers of emotion in music.In this paper,we collect a ground truth data set of2904songs that have been tagged with one of the four words“happy”,“sad”,“angry”and“relaxed”, on the Last.FM web site.An excerpt of the audio is then retrieved https://www.doczj.com/doc/339808272.html,,and various sets of audio fea-tures are extracted using standard algorithms.Two clas-si?ers are trained using support vector machines with the polynomial and radial basis function kernels,and these are tested with10-fold cross validation.Our results show that spectral features outperform those based on rhythm,dy-namics,and,to a lesser extent,harmony.We also?nd that the polynomial kernel gives better results than the radial basis function,and that the fusion of different feature sets does not always lead to improved classi?cation.

1.INTRODUCTION

In the past ten years,music emotion recognition has at-tracted increasing attention in the?eld of music informa-tion retrieval(MIR)[16].Music not only conveys emotion, but can also modulate a listener’s mood[8].People report that their primary motivation for listening to music is its emotional effect[19]and the emotional component of mu-sic has been recognised as most strongly associated with music expressivity[15].

Recommender systems for managing a large personal music collections typically use collaborative?ltering[28] (historical ratings)and metadata-and content-based?lter-ing[3](artist,genre,acoustic features similarity).Emo-tion can be easily incorporated into such systems to sub-jectively organise and search for music.Musicovery1, 1https://www.doczj.com/doc/339808272.html,/

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c 2012International Society for Music Information Retrieval.for example,has successfully use

d a dimensional model of emotion within its recommendation system.

Although music emotion has been widely studied in psy-chology,signal processing,neuroscience,musicology and machine learning,our understanding is still at an early stage. There are three common issues:1.collection of ground truth data;2.choice of emotion model;3.relationships between emotion and individual acoustic features[13].

Since2007,the annual Music Information Retrieval Eval-uation eXchange(MIREX)2has organised an evaluation campaign for MIR algorithms to facilitate?nding solu-tions to the problems of audio music classi?cation.In previous studies,signi?cant research has been carried out on emotion recognition including regressor training:us-ing multiple linear regression[6]and Support Vector Ma-chines(SVM)[23,37],feature selection[35,36],the use of lyrics[13]and advanced research including mood classi?-cation on television theme tunes[30],analysis with elec-troencephalogram(EEG)[18],music expression[32]and the relationship with genre and artist[12].Other relevant work on classi?cation suggests that feature generation can outperform approaches based on standard features in some contexts[33].

In this paper,we aim to better explain and explore the relationship between musical features and emotion.We examine the following parameters:?rst,we compare four perceptual dimensions of musical features:dynamics,spec-trum,rhythm,and harmony;second,we evaluate an SVM associated with two kernels:polynomial and radial basis functions;third,for each feature we compare the mean and standard deviation feature value.The results are trained and tested using semantic data retrieved from last.fm3and audio data from7digital4.

This paper is structured as follows.In section2,three psychological models are discussed.Section3explains the dataset collection we use in training and testing.The pro-cedure is described in section4,which includes data pre-processing(see section4.1),feature extraction(see section 4.2)and classi?cation(see section4.3).Section5explains four experiments.Finally,section6concludes the paper and presents directions for future work.

2https://www.doczj.com/doc/339808272.html,/mirex/wiki/MIREX HOME

3https://www.doczj.com/doc/339808272.html,st.fm/

4https://www.doczj.com/doc/339808272.html,/

2.PSYCHOLOGICAL EMOTION MODELS One of the dif?culties in representing emotion is to distin-guish music-induced emotion from perceived emotion be-cause the two are not always aligned[5].Different psycho-logical models of emotion have been compared in a study of perceived emotion[7].

Most music related studies are based on two popular approaches:categorical[10]and dimensional[34]mod-els of emotion.The categorical approach describes emo-tions with a limited number of innate and universal cate-gories such as happiness,sadness,anger and fear.The di-mensional model considers all affective terms arising from independent neurophysiological systems:valence(nega-tive to positive)and arousal(calm to exciting).Recently a more sophisticated model of music-induced emotion-the Geneva Emotion Music Scale(GEMS)model-consisting of9dimensions,has been proposed[42].Our results and analysis are based on the categorical model since we make our data collection through human-annotated social tags which are categorical in nature.

3.GROUND-TRUTH DATA COLLECTION

As discussed above,due to the lack of ground truth data, most researchers compile their own databases[41].Man-ual annotation is one of the most common ways to do this. However,it is expensive in terms of?nancial cost and hu-man labour.Moreover,terms used may differ between in-dividuals.Different emotions may be described using the same term by different people which would result in poor prediction[38].However,with the emergence of music discovery and recommendation websites such as last.fm which support social tags for music,we can access rich human-annotated https://www.doczj.com/doc/339808272.html,pared with the tradi-tional approach of web mining which gives noisy results, social tagging provides highly relevant information for mu-sic information retrieval(MIR)and has become an im-portant source of human-generated contextual knowledge [11].Levy[24]has also shown that social tags give a high quality source of ground truth data and can be effective in capturing music similarity[40].

The?ve mood clusters proposed by MIREX[14](such as rollicking,literate,and poignant)are not popular in so-cial tags.Therefore,we use four basic emotion classes: happy,angry,sad and relaxed,considering these four emo-tions are widely accepted across different cultures and cover the four quadrants of the2-dimensional model of emo-tion[22].These four basic emotions are used as seeds to retrieve the top30tags from last.fm.We then obtain a list of songs labelled with the retrieved tags.Table1and table 2show an example of the retrieved results.

Given the retrieved titles and the names of the singers, we use a public API to get preview?les.The results cover different types of pop music,meaning that we avoid partic-ular artist and genre effects[17].Since the purpose of this step is to?nd ground truth data,issues such as cold start, noise,hacking,and bias are not relevant[4,20].

Most datasets on music emotion recognition are quite

Happy Angry Sad Relax

happy angry sad relax happy hardcore angry music sad songs relax trance makes me happy angry metal happysad relax music happy music angry pop music sad song jazz relax happysad angry rock sad&beautiful only relax Table1.Top5tags returned by last.fm

Singer Title

Noah And The Whale5Years Time

Jason Mraz I’m Yours

Rusted Root Send Me On My Way

Royksopp Happy Up Here

Karen O and the Kids All Is Love

Table2.Top songs returned with tags from the“happy”category.

small(less than1000items),which indicates that2904 songs(see table3)for four emotions retrieved by social tags is a good size for the current experiments.The dataset will be made available5,to encourage other researchers to reproduce the results for research and evaluation.

Emotion Number of Songs

Happy753

Angry639

Sad763

Relaxed749

Overall2904

Table3.Summary of ground truth data collection

4.PROCEDURES

The experimental procedure consists of four stages:data collection,data preprocessing,feature extraction,and clas-si?cation,as shown in?gure1.

4.1Data Preprocessing

As shown in Table1,there is some noise in the data such as confusing tags and repeated songs.We manually remove data with the tag happysad which existed in both the happy and sad classes and delete the repeated songs,to make sure every song will only exist once in a single class.Moreover, we convert our dataset to standard wav format(22,050Hz sampling rate,16bit precision and mono channel).The song excerpts are either30seconds or60seconds,rep-resenting the most salient part of the song[27],therefore there is no need to truncate.At the end,we normalise the excerpts by dividing by the highest amplitude to mitigate the production effect of different recording levels.

4.2Feature Extraction

As suggested in the work of Saari and Eerola[35],two dif-ferent types of feature(mean and standard deviation)with 5The dataset can be found at https://https://www.doczj.com/doc/339808272.html,/projects-/emotion-recognition

Figure1.Procedure

a total of55features were extracted using the MIR tool-box6[21](shown in table4).The features are categorized into the following four perceptual dimensions of music lis-tening:dynamics,rhythm,spectral,and harmony.

4.3Classi?cation

The majority of music classi?cation tasks[9](genre clas-si?cation[25,39],artist identi?cation[29],and instrument recognition[31])have used k-nearest neighbour(K-NN) [26]and support vector machines(SVM)[2].In the case of audio input features,the SVM has been shown to per-form best[1].

In this paper,therefore,we choose support vector ma-chines as our classi?er,using the implementation of the se-quential minimal optimisation algorithm in the Weka data mining toolkit7.SVMs are trained using polynomial and radial basis function(RBF)kernels.We set the cost factor C=1.0,and leave other parameters unchanged.An in-ternal10-fold cross validation is applied.To better under-stand and compare features in four perceptual dimensions, our experiments are divided into four tasks.

Experiment1:we compare the performance of the two kernels(polynomial and RBF)using various features.

Experiment2:four classes(perceptual dimensions)of features are tested separately,and we compare the results to?nd a dominant class.

Experiment3:two types of feature descriptor,mean and standard deviation,are calculated.The purpose is to com-pare values for further feature selection and dimensionality reduction.

6Version1.3.3:https://www.jyu.?/music/coe/materials/mirtoolbox 7https://www.doczj.com/doc/339808272.html,/ml/weka/

Dimen.No.Features Acronyms Dynamics1-2RMS energy RMSm,RMSstd 3-4Slope Ss,Sstd

5-6Attack As,Astd

7Low energy LEm Rhythm1-2Tempo Ts,Tstd

3-4Fluctuation peak(pos,mag)FPm,FMm

5Fluctuation centroid FCm Spec.1-2Spectrum centroid SCm,SCstd

3-4Brightness BRm,BRstd

5-6Spread SPm,SPstd

7-8Skewness SKm,SKstd

9-10Kurtosis Km,Kstd

11-12Rolloff95R95s,R95std

13-14Rolloff85R85s,R85std

15-16Spectral Entrophy SEm,SEstd

17-18Flatness Fm,Fstd

19-20Roughness Rm,Rstd

21-22Irregularity IRm.IRstd

23-24Zero crossing rate ZCRm,ZCRstd

25-26Spectral?ux SPm,SPstd

27-28MFCC MFm,MFstd

29-30DMFCC DMFm,DMFstd

31-32DDMFCC DDm,DDstd Harmony1-2Chromagram peak CPm,CPstd

3-4Chromagram centroid CCm,CCstd

5-6Key clarity KCm,KCstd

7-8Key mode KMm,KMstd

9-10HCDF Hm,Hstd Table4.The feature set used in this work;m=mean,std =standard deviation.

Experiment4:different combinations of feature classes (e.g.,spectral with dynamics)are evaluated in order to de-termine the best-performing model.

5.RESULTS

5.1Experiment1

In experiment1,SVMs trained with two different kernels are compared.Previous studies[23]have found in the case of audio input that the SVM performs better than other classi?ers(Logistic Regression,Random Forest,GMM, K-NN and Decision Trees).To our knowledge,no work has been reported explicitly comparing different kernels for SVMs.In emotion recognition,the radial basis func-tion kernel is a common choice because of its robustness and accuracy in other similar recognition tasks[1].

Polynomial RBF

Feature Class Accuracy Time Accuracy Time No.

Dynamics37.20.4426.332.57

Rhythm37.50.4434.523.25

Harmony47.50.4136.627.410

Spectral51.90.4048.114.332 Table5.Experiment1results:time=model building time, No.=number of features in each class

The results in table5show however that regardless of the features used,the polynomial kernel always achieved the higher accuracy.Moreover,the model construction times for each kernel are dramatically different.The av-erage construction time for the polynomial kernel is0.4 seconds,while the average time for the RBF kernel is24.2

seconds,around60times more than the polynomial ker-nel.The following experiments also show similar results. This shows that polynomial kernel outperforms RBF in the task of emotion recognition at least for the parameter val-ues used here.

5.2Experiment2

In experiment2,we compare the emotion prediction re-sults for the following perceptual dimensions:dynamics, rhythm,harmony,and spectral.Results are shown in?g-ure2).Dynamics and rhythm features yield similar re-sults,with harmony features providing better results,but the spectral class with32features achieves the highest ac-curacy of51.9%.This experiment provides a baseline model, and further exploration of multiple dimensions is performed in experiment

4.

https://www.doczj.com/doc/339808272.html,parison of classi?cation results for the four classes of features.

5.3Experiment3

In this experiment,we evaluate different types of feature descriptors,mean value and standard deviation for each feature across all feature classes,for predicting the emotion in music.The results in table6show that the use of both mean and standard deviation values gives the best results in each case.However,the processing time increased,so choosing the optimal descriptor for each feature is highly desirable.For example,choosing only the mean value in the harmony class,we lose2%of accuracy but increase the speed while the choice of standard deviation results in around10%accuracy loss.As the number of features in-creases,the difference between using mean and standard deviation will be reduced.However,more experiments are needed to explain why the mean in harmony and spectral features,and standard deviation values of dynamics and rhythm features have higher accuracy scores.

5.4Experiment4

In order to choose the best model,the?nal experiment fuses different perceptual features.As presented in table7, optimal accuracy is not produced by the combination of all features.Instead,the use of spectral,rhythm and harmony (but not dynamic)features produces the highest accuracy.

Features Class Polynomial No.features

Dynamics all37.27

Dynamics mean29.73

Dynamics std33.83

Rhythm all37.55

Rhythm mean28.71

Rhythm std34.21

Harmony all47.510

Harmony mean45.35

Harmony std38.35

Spectral all51.932

Spectral mean49.616

Spectral std47.516

Spec+Dyn all52.339

Spec+Dyn mean50.519

Spec+Dyn std48.719

Spec+Rhy all52.337

Spec+Rhy mean49.817

Spec+Rhy std47.817

Spec+Har all53.342

Spec+Har mean51.321

Spec+Har std50.321

Har+Rhy all49.115

Har+Rhy mean45.66

Har+Rhy std41.26

Har+Dyn all48.817

Har+Dyn mean46.98

Har+Dyn std42.48

Rhy+Dyn all41.712

Rhy+Dyn mean32.04

Rhy+Dyn std38.84

https://www.doczj.com/doc/339808272.html,parison of mean and standard deviation(std) features.

Features Accuracy No.features

Spec+Dyn52.339

Spec+Rhy52.337

Spec+Har53.342

Har+Rhy49.115

Har+Dyn48.817

Rhy+Dyn41.712

Spec+Dyn+Rhy52.444

Spec+Dyn+Har53.849

Spec+Rhy+Har54.047

Dyn+Rhy+Har49.722

All Features53.654

Table7.Classi?cation results for combinations of feature sets.

6.CONCLUSION AND FUTURE WORK

In this paper,we collected ground truth data on the emo-tion associated with2904pop songs from last.fm tags.Au-dio features were extracted and grouped into four percep-tual dimensions for training and validation.Four experi-ments were conducted to predict emotion labels.The re-sults suggest that,instead of the conventional approach us-ing SVMs trained with a RBF kernel,a polynomial ker-nel yields higher accuracy.Since no single dominant fea-tures have been found in emotion recognition,we explored the performance of different perceptual classes of feature for predicting emotion in music.Experiment3found that dimensionality reduction can be achieved through remov-ing either mean or standard deviation values,halving the number of features used,with,in some cases,only2%ac-curacy loss.The last experiment found that inclusion of dynamics features with the other classes actually impaired

the performance of the classi?er while the combination of spectral,rhythmic and harmonic features yielded optimal performance.

In future work,we will expand this research both in depth and breadth,to?nd features and classes of features which best represent emotion in music.We will examine higher-level dimensions such as temporal evolution fea-tures,as well as investigating the use of auditory https://www.doczj.com/doc/339808272.html,ing the datasets retrieved from Last.fm,we will compare the practicability of social tags with other human-annotated datasets in emotion recognition.Through these studies of subjective emotion,we will develop methods for incorporating other empirical psychological data in a sub-jective music recommender system.

7.ACKNOWLEDGEMENTS

We acknowledge the support of the Queen Mary University of London Postgraduate Research Fund(QMPGRF)and the China Scholarship Council.We would like to thank the reviewers and Emmanouil Benetos for their advice and comments.

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的、地、得的用法和区别

“的、地、得”的用法和区别 导入(进入美妙的世界啦~) “的、地、得”口诀儿歌 的地得,不一样,用法分别记心上, 左边白,右边勺,名词跟在后面跑。 美丽的花儿绽笑脸,青青的草儿弯下腰, 清清的河水向东流,蓝蓝的天上白云飘, 暖暖的风儿轻轻吹,绿绿的树叶把头摇, 小小的鱼儿水中游,红红的太阳当空照, 左边土,右边也,地字站在动词前, 认真地做操不马虎,专心地上课不大意, 大声地朗读不害羞,从容地走路不着急, 痛快地玩耍来放松,用心地思考解难题, 勤奋地学习要积极,辛勤地劳动花力气, 左边两人双人得,形容词前要用得, 兔子兔子跑得快,乌龟乌龟爬得慢, 青青竹子长得快,参天大树长得慢, 清晨锻炼起得早,加班加点睡得晚, 欢乐时光过得快,考试题目出得难。 知识典例(注意咯,下面可是黄金部分!) 的、地、得 “的”、“地”、“得”的用法区别本是中小学语文教学中最基本的常识,但在使用中也最容易发生混淆,再加上一段时间里,中学课本中曾将这三个词的用法统一为“的”,因此造成了很多人对它们的用法含混不清进而乱用一通的现象。

一、“的、地、得”的基本概念 1、“的、地、得”的相同之处。 “的、地、得”是现代汉语中高频度使用的三个结构助词,都起着连接作用;它们在普通话中都读轻声“de”,没有语音上的区别。 2、“的、地、得”的不同之处。 吕叔湘、朱德熙所著《语法修辞讲话》认为“的”兼职过多,负担过重,而力主“的、地、得”严格分工。50 年代以来的诸多现代汉语论著和教材,一般也持这一主张。从书面语中的使用情况看,“的”与“地”、“得”的分工日趋明确,特别是在逻辑性很强的论述性、说明性语言中,如法律条款、学术论著、外文译著、教科书等,更是将“的”与“地”、“得”分用。 “的、地、得”在普通话里都读轻声“de”,但在书面语中有必要写成三个不同的字:在定语后面写作“的”,在状语后面写作“地”,在补语前写作“得”。这样做的好处,就是可使书面语言精确化。 二、“的、地、得”的用法 1、的——定语的标记,一般用在主语和宾语的前面。“的”前面的词语一般用来修饰、限制“的”后面的事物,说明“的”后面的事物怎么样。结构形式一般为:形容词、名词(代词)+的+名词。如: ①颐和园(名词)的湖光山色(主语)美不胜收。 ②她是一位性格开朗的女子(名词,宾语)。 2、地——状语的标记,一般用在谓语(动词、形容词)前面。“地”前面的词语一般用来形容“地”后面的动作,说明“地”后面的动作怎么样。结构方式一般为:形容词(副词)+地+动词(形容词)。如: ③她愉快(形容词)地接受(动词,谓语)了这件礼物。 ④天渐渐(时间副词)地冷(形容词,谓语)起来。 3、得——补语的标记,一般用在谓语后面。“得”后面的词语一般用来补充说明“得”前面的动作怎么样,结构形式一般为:动词(形容词)+得+副词。如: ⑤他们玩(动词,谓语)得真痛快(补语)。

常用介词的用法

分考点1 表示时间的介词 Point 1 at, in, on 的用法 (1)at 的用法 At 表示时间点,用于具体的时刻(几点,正午,午夜,黎明,拂晓,日出,日落等),或把某一时间看作某一时刻的词之前以及某些节假日的词之前。 at 6:00 在6点钟 At noon 在中午 At daybreak 在拂晓 At down 在黎明 At Christmas 在圣诞节 【特别注意】在以下的时间短语中,at 表示时间段。 At dinner time 在(吃)晚饭时 At weekends/ the weekend 在周末 (2)in 的用法 ①表示时间段,与表示较长一段时间的词搭配,如年份,月份,季节,世纪,朝代,还可以用于泛指的上午、下午、傍晚等时间段的词前。 In 2009 在2009年 In April 在四月 In the 1990s 在20世纪90年代 In Tang Dynasty 在唐朝 In the morning在上午 ②后接时间段,用于将来时,表示“在一段时间之后”。 The film will begin in an hour. 电影将于一个小时之后开始。 【特别注意】当时间名词前有this,that,last,next,every,each,some等词修饰时,通常不用任何介词。 This morning 今天上午last year 去年 (3)on 的用法 ①表示在特定的日子、具体的日期、星期几、具体的某一天或某些日子。 On September the first 在9月1号 On National Day 在国庆节 We left the dock on a beautiful afternoon. 我们在一个明媚的下午离开了码头。 ②表示在具体的某一天的上午、下午或晚上(常有前置定语或后置定语修饰)。 On Sunday morning 在星期日的早上 On the night of October 1 在10月1号的晚上 【特别注意】“on +名词或动名词”表示“一...就...”. On my arrival home/ arriving home, I discovered they had gone. 我一到家就发现他们已经离开了。 Point 2 in,after 的用法 In 和after都可以接时间段,表示“在...之后”,但in 常与将来时连用,after 常与过去时连用。 We will meet again in two weeks.

英语介词用法大全

英语介词用法大全 TTA standardization office【TTA 5AB- TTAK 08- TTA 2C】

介词(The Preposition)又叫做前置词,通常置于名词之前。它是一种虚词,不需要重读,在句中不单独作任何句子成分,只表示其后的名词或相当于名词的词语与其他句子成分的关系。中国学生在使用英语进行书面或口头表达时,往往会出现遗漏介词或误用介词的错误,因此各类考试语法的结构部分均有这方面的测试内容。 1. 介词的种类 英语中最常用的介词,按照不同的分类标准可分为以下几类: (1). 简单介词、复合介词和短语介词 ①.简单介词是指单一介词。如: at , in ,of ,by , about , for, from , except , since, near, with 等。②. 复合介词是指由两个简单介词组成的介词。如: Inside, outside , onto, into , throughout, without , as to as for , unpon, except for 等。 ③. 短语介词是指由短语构成的介词。如: In front of , by means o f, on behalf of, in spite of , by way of , in favor of , in regard to 等。 (2). 按词义分类 {1} 表地点(包括动向)的介词。如: About ,above, across, after, along , among, around , at, before, behind, below, beneath, beside, between , beyond ,by, down, from, in, into , near, off, on, over, through, throught, to, towards,, under, up, unpon, with, within , without 等。 {2} 表时间的介词。如: About, after, around , as , at, before , behind , between , by, during, for, from, in, into, of, on, over, past, since, through, throughout, till(until) , to, towards , within 等。 {3} 表除去的介词。如: beside , but, except等。 {4} 表比较的介词。如: As, like, above, over等。 {5} 表反对的介词。如: againt ,with 等。 {6} 表原因、目的的介词。如: for, with, from 等。 {7} 表结果的介词。如: to, with , without 等。 {8} 表手段、方式的介词。如: by, in ,with 等。 {9} 表所属的介词。如: of , with 等。 {10} 表条件的介词。如:

英语单词惯用法集锦解析

英语单词惯用法集锦 习惯接动词不定式的动词(V to inf) adore(vi极喜欢) dread (vt.不愿做,厌恶)plan 计划 afford(+to,vt有条件,能承担)endeavour (vt,竭力做到,试图或力图)prefer(vt.宁可;宁愿(选择);更喜欢)agree 同意endure(忍受.cannot ~ to) prepare准备 aim (vi[口语]打算:) engage (vi.保证,担保;) presume(vt.冒昧;敢于[用于第一人称时为客套话]:) appear (vi.似乎;显得) essay(vt.尝试,试图) pretend(vt.自命;自称;敢于;妄为) apply (申请)expect(期望,希望)proceed(开始,着手,)arrange (vi.做安排,(事先)筹划)fail (vt.未做…;疏忽)promise(许诺,保证做 ask (要求)forget (vt. 忘记)purpose (vt.决心,打算) beg (vt.正式场合的礼貌用语]请(原谅),请(允许):I beg to differ.恕我不能赞同)guarantee(保证,担保)refuse(拒绝)bear 承受,忍受hate([口语]不喜欢;不愿意;)regret (vt. 抱歉;遗憾)begin help (有助于,促进)remember(记住) bother (vi.通常用于否定句]麻烦,费心)hesitate(vi.犹豫;有疑虑,不愿)scheme(策划做)care (vt.想要;希望;欲望[后接不定式,常用于否定、疑问及条件句中])hope (vt.希望,盼望,期待)seek(vt.谋求,图谋[后接不定式]) cease (停止; 不再(做某事)[正式] intend (打算;想要)seem(似乎,好像[后接不定式或从句];觉得像是,以为[ choose (意愿;选定;决定)itch start开始claim (vt. 主张;断言;宣称) continue (继续)like 喜欢swear(vt.起誓保证;立誓要做(或遵守) dare (vt.敢,敢于,勇于,胆敢)long(vi.渴望;热望;极想) decline(vt.拒绝,拒不(做、进入、考虑等) manage(设法完成某事)threaten(vt.威胁,恐吓,恫吓)deign (屈尊做)mean(有意[不用进行时)trouble(vi.费心,费神;麻烦)demand(vi.要求,请求:)need (需要)try(设法做) deserve (应得) neglect (疏忽) undertake(承诺,答应,保证) desire (希望渴望)offer(表示愿意(做某事),自愿;)venture(冒险(做某事))determine(vi.决心,决意,决定,)omit (疏忽,忘记)want 想要 die (誓死做)pine (渴望)wish (希望) 习惯接“疑问词+动词不定式”的动词(有时也包括VN wh-+to do) advise 建议explain 解释perceive 觉察,发觉 answer 答复find 得知,察觉persuade 说服,劝说;使某人相信 ask 询问,问forget 忘记phone 打电话 assure 保证guess 臆测,猜度pray 祈祷 beg 请求,恳求hear 小心聆听(法庭案件)promise 允诺 conceive 想象,设想imagine 以为,假象remember记得 consider 考虑,思考indicate 暗示remind 提醒,使想起 convince 使相信inform告知通知instruct告知,教导 see 看看,考虑,注意decide 解决,决定know 学得,得知 show 给人解释;示范;叙述;discover发现;知道learn 得知,获悉 signal以信号表示doubt 怀疑,不相信look 察看;检查;探明 strike 使想起;使突然想到;使认为suggest 提议,建议tell 显示,表明;看出,晓得;warn 警告,告诫think 想出;记忆,回忆;想出,明白wonder 纳闷,想知道 wire 打电报telegraph 打电报 习惯接动名词的动词(包括v+one’s/one+v+ing) acknowledge 认知,承认…之事实escape免除,避免omit疏忽,忽略 admit 承认,供认excuse 原谅overlook 放任,宽容,忽视adore (非正式)极为喜欢fancy 构想,幻想,想想postpone 延期,搁置 advise 劝告,建议finish完成prefer较喜欢 appreciate 为…表示感激(或感谢)forbid 不许,禁止prevent预防 avoid 逃避forget 忘记prohibit 禁止,妨碍

“的、地、得”的用法和区别

的、地、得的用法和区别 的、地、得的用法和区别老班教育 一、的、地、得的基本概念 1、的、地、得的相同之处。 的、地、得是现代汉语中高频度使用的三个结构助词,都起着连接作用;它们在普通话中都读轻声de,没有语音上的区别。 2、的、地、得的不同之处。 吕叔湘、朱德熙所著《语法修辞讲话》认为的兼职过多,负担过重,而力主的、地、得严格分工。50 年代以来的诸多现代汉语论著和教材,一般也持这一主张。从书面语中的使用情况看,的与地、得的分工日趋明确,特别是在逻辑性很强的论述性、说明性语言中,如法律条款、学术论著、外文译著、教科书等,更是将的与地、得分用。 的、地、得在普通话里都读轻声de,但在书面语中有必要写成三个不同的字:在定语后面写作的,在状语后面写作地,在补语前写作得。这样做的好处,就是可使书面语言精确化。 二、的、地、得的用法 (一)、用法 1、的——定语的标记,一般用在主语和宾语的前面。的前面的词语一般用来修饰、限制的后面的事物,说明的后面的事物怎么样。 结构形式一般为:形容词、名词(代词)+的+名词。如: 颐和园(名词)的湖光山色(主语)美不胜收。 她是一位性格开朗的女子(名词,宾语)。 2、地——状语的标记,一般用在谓语(动词、形容词)前面。地前面的词语一般用来形容地后面的动作,说明地后面的动作怎么样。 结构方式一般为:形容词(副词)+地+动词(形容词)。如: 她愉快(形容词)地接受(动词,谓语)了这件礼物。 天渐渐(时间副词)地冷(形容词,谓语)起来。 3、得——补语的标记,一般用在谓语后面。得后面的词语一般用来补充说明得前面的动作怎么样。 结构形式一般为:动词(形容词)+得+副词。如: 他们玩(动词,谓语)得真痛快(补语)。 她红(形容词,谓语)得发紫(补语)。 (二)、例说 的,一般用在名词和形容词的后面,用在描述或限制人物、事物时,形容的词语与被形容的词语之间,表示一种描述的结果。如:漂亮的衣服、辽阔的土地、高大的山脉。结构一般为名词(代词或形容词)+的+名词。如,我的书、你的衣服、他的孩子,美丽的景色、动听的歌曲、灿烂的笑容。 地,用法简单些,用在描述或限制一种运动性质、状态时,形容的词语与被形容的词语之间。结构通常是形容词+地+动词。前面的词语一般用来形容后面的动作。一般地的后面只跟动词。比如高兴地跳、兴奋地叫喊、温和地说、飞快地跑;匆匆地离开;慢慢地移动......... 得,用在说明动作的情况或结果的程度时,说明的词语与被说明的词语之间,后面的词语一般用来补充和说明前面的情况。比如。跑得飞快、跳得很高、显得高雅、显得很壮、馋得直流口水、跑得快、飞得高、走得慢、红得很……得通常用在动词和形容词(动词之间)。

英语介词用法详解

英语常用介词用法与辨析 ■表示方位的介词:in, to, on 1. in 表示在某地范围之内。如: Shanghai is/lies in the east of China. 上海在中国的东部。 2. to 表示在某地范围之外。如: Japan is/lies to the east of China. 日本位于中国的东面。 3. on 表示与某地相邻或接壤。如: Mongolia is/lies on the north of China. 蒙古国位于中国北边。 ■表示计量的介词:at, for, by 1. at表示“以……速度”“以……价格”。如: It flies at about 900 kilometers a hour. 它以每小时900公里的速度飞行。 I sold my car at a high price. 我以高价出售了我的汽车。 2. for表示“用……交换,以……为代价”。如: He sold his car for 500 dollars. 他以五百元把车卖了。 注意:at表示单价(price) ,for表示总钱数。 3. by表示“以……计”,后跟度量单位。如: They paid him by the month. 他们按月给他计酬。 Here eggs are sold by weight. 在这里鸡蛋是按重量卖的。 ■表示材料的介词:of, from, in 1. of成品仍可看出原料。如: This box is made of paper. 这个盒子是纸做的。 2. from成品已看不出原料。如: Wine is made from grapes. 葡萄酒是葡萄酿成的。 3. in表示用某种材料或语言。如: Please fill in the form in pencil first. 请先用铅笔填写这个表格。 They talk in English. 他们用英语交谈(from 。 注意:in指用材料,不用冠词;而with指用工具,要用冠词。请比较:draw in penc il/draw with a pencil。 ■表示工具或手段的介词:by, with, on 1. by用某种方式,多用于交通。如by bus乘公共汽车,by e-mail. 通过电子邮件。

with的用法大全

with的用法大全----四级专项训练with结构是许多英语复合结构中最常用的一种。学好它对学好复合宾语结构、不定式复合结构、动名词复合结构和独立主格结构均能起很重要的作用。本文就此的构成、特点及用法等作一较全面阐述,以帮助同学们掌握这一重要的语法知识。 一、 with结构的构成 它是由介词with或without+复合结构构成,复合结构作介词with或without的复合宾语,复合宾语中第一部分宾语由名词或代词充当,第二部分补足语由形容词、副词、介词短语、动词不定式或分词充当,分词可以是现在分词,也可以是过去分词。With结构构成方式如下: 1. with或without-名词/代词+形容词; 2. with或without-名词/代词+副词; 3. with或without-名词/代词+介词短语; 4. with或without-名词/代词+动词不定式; 5. with或without-名词/代词+分词。 下面分别举例:

1、 She came into the room,with her nose red because of cold.(with+名词+形容词,作伴随状语) 2、 With the meal over , we all went home.(with+名词+副词,作时间状语) 3、The master was walking up and down with the ruler under his arm。(with+名词+介词短语,作伴随状语。) The teacher entered the classroom with a book in his hand. 4、He lay in the dark empty house,with not a man ,woman or child to say he was kind to me.(with+名词+不定式,作伴随状语) He could not finish it without me to help him.(without+代词 +不定式,作条件状语) 5、She fell asleep with the light burning.(with+名词+现在分词,作伴随状语) 6、Without anything left in the cupboard, she went out to get something to eat.(without+代词+过去分词,作为原因状语) 二、with结构的用法 在句子中with结构多数充当状语,表示行为方式,伴随情况、时间、原因或条件(详见上述例句)。

常见动词用法

1、keep ①keep + 形容词表示“保持” Please keep quite. 请保持安静。 ②keep + 宾语+ 形容词(或介词短语)表示“把……保持在某一状态” We must do everything we can to keep the air clean. 我们必须尽一切所能保持空气清洁。 ③keep sb doing sth 表示“让某人做某事” ——只能用现在分词作宾语补足语,不能用不定式。 He kept us waiting for two hours. 他让我们等了两个小时。 He kept us to wait for two hours. (错误) ④keep on doing sth和keep doing sth 表示“继续做某事,反复做某事”,可换用。 但keep on doing 更强调动作的反复性或做事人的决心。 He keeps on phoning me, but I don’t want to talk to him. Though he failed 3 times, he kept on trying. 他老是给我打电话,但我不想同他讲话。虽然他已失败了3次,但他仍继续干下去。 keep doing sth 经常用于静态动词。 He kept lying in bed all day long. 他整天都躺在床上。 ⑤keep …from doing sth 表示“阻止,使免于” He kept them from fishing in the lake. 他不让他们在那个湖里捕鱼。 2、may not / mustn’t / needn’t / wouldn’t ①may not be 是may be的否定式,意为“可能不是,也许不是” He may be there.他可能在那里。He may not be there.他可能不在那里。 ②must 意为“必须”,mustn’t 意为“千万不可,绝对不可” 所以Must we/I ……?的否定回答要用needn’t—意为“不必” -Must we get there before 11 o’clock? -No, we needn’t. ③wouldn’t = would not 意为“不会,不愿” I wouldn’t say no. 3、do ①do表示“做”,做某事,常指某种不具体的活动;make表示“制作”,指做出某种具体的东西。

的地得的用法和区分

《“的、地、得”的用法》语文微课教案 一、教学背景 在语言文字规范化大背景下,帮助学生解决应用“的地得”的疑惑与困难。 二、设计思路 针对学生对于“的地得”的误用与忽视展开教学,规范结构助词“的地得”的使用。按照“问题的提出、问题的分析、问题的解决”的思路展开教学,总结归纳优化的方式方法。 三、教学目标 1、知道“怎么样的什么、怎么样地干什么、干得怎么样”三种固定搭配。 2、掌握“的、地、得”的区别与联系。 3、运用小儿歌“动前土、名前白、行动后面双人来”的口诀帮助正确使用“的、地、得”。 四、教学重难点 1、知道“的、地、得”的区别。 2、在实际情境中正确运用“的、地、得”。 五、教学时间 8分钟微课堂 六、教学适用对象 义务教育九年制内的学生 七、教学准备

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欢乐(地)歌唱满意(地)点头满意(的)作品 (2)类别区分 1)跑(得)飞快飞快(地)跑 2)愉快(的)旅行旅行(得)愉快 3)强烈(的)渴望强烈(地)渴望 (3)综合杂糅 小雏鹰飞到大树的上方,高兴地喊起来:“我真的会飞啦!而且飞(得)很高呢!” 小结:能填对这个句子的你肯定就已经学会它们的用法了! 4、特殊情况 质疑:假如遇到特殊情况怎么办呢? 我从书包里拿出书交给她们,她们高兴得.围着我跳起舞来。(出自二年级上册《日记两则》) (1)质疑:为什么这里要使用“得”呢? (2)释疑:原来这里强调的是心情,动词在后,形容词在前,相当于后置,“得”修饰“跳舞”而非“围”。现在你明白了吧? 5、小结归纳: 怎么样,你们学会了吗?为了让同学们能够更快的记住它们的用法,老师送给大家一首口诀来帮助你们熟记三个“的”的正确使用方法:动前土、名前白、行动后面双人来。

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with用法归纳

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介词with的用法大全

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she pretended to have finished the homeworkwhen she went out and played.当她出门玩的时候她假装自己已经完成了家庭作业。(假装做作业这个动作已经在出门玩之前做完了)(JasoOon的回答)以及怀陌的回答:When the teacher came in,he pretended to havefinished the homework.当老师进来的时候他假装自己已经完成家庭作业了,两者有异曲同工之妙。 3. pretendtobe doing sth 这个短语的意思是假装正在做某事,强调动作的一个进行时态。 举例:They pretend to be reading books when the teacher sneakingly stands at the back door.当老师偷偷地站在后门的时候他们假装正在读书(读书与老师站在后门都是过去进行时 态)(JasoOon的回答) Asmanypeople do,youoftenpretend to be doingwork when actuallyyou arejust wasting time online.像很多人一样,你经常假装正在工作,其实是在上网。 群主补充:昨天和今天已提交作业的同学,做得都很好,全部授予小红花。希望你们再接再厉,不要松懈哟。所以下周一出题者为所有已提交作业的同学或者你们选出的代表。

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3、说明名词, 表示事物的附属部分或所具有的性质]具有; 带有; 加上; 包括...在内 tea with sugar 加糖的茶水 a country with a long history 历史悠久的国家4 4、表示一致]在...一边, 与...一致; 拥护, 有利于 vote with sb. 投票赞成某人 with的复合结构作独立主格,表示伴随情况时,既可用分词的独立结构,也可用with的复合结构: with +名词(代词)+现在分词/过去分词/形容词/副词/不定式/介词短语。例如: He stood there, his hand raised. = He stood there, with his hand raise.他举手着站在那儿。 典型例题 The murderer was brought in, with his hands ___ behind his back A. being tied B. having tied C. to be tied D. tied 答案D. with +名词(代词)+分词+介词短语结构。当分词表示伴随状况时,其主语常常用

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