影话婺剧

中国戏曲历史悠久，剧种种类繁多，据不完全统计，我国各民族地区的戏曲剧种约有三百六十多种，传统剧目数以万计。比较流行的著名剧种有：京剧．昆剧、越剧、豫剧．黄梅戏、婺剧、徽剧、评剧、秦腔、河北梆子等五十多个剧种。 喜欢中国文化声里韵里的您，是否在茶余饭后还会来上两嗓子呢？这期开始，我们以摄影师拍摄的个案为准，以图片的形式开始将各式剧种轮播“上演”。六月暑夏，观演请自备一盏茗茶。 好戏开场了!     

“互联网+”背景下传统戏曲进高校的策略研究

随着社会的变革与外来文化艺术的冲击，我国的戏曲艺术面临着重大危机，许多剧种正逐渐淡出大众的视野，人们对戏曲的兴趣也不断下降。本文采用问卷调查法对大学生戏曲认知情况进行研究，在分析传统戏曲进校园的难点后，尝试开发云戏曲——“戏言”网站来提高大学生对传统戏曲文化的需求，助推传统戏曲在高校的传承与发展。     

一种用于文本分类的特征选择方法

文本分类是文本信息处理领域一个非常重要的研究方向，为了节省文本分类处理中所需的存储空间和运算时间，在分类之前用高效的算法减少所需分析的数据是非常必要的。该文介绍了一种文本分类中特征降维的方法。和传统的方法不同，该文所涉及的特征是从句子中提取的不同长度的词组，然后用比数比来对其进行特征选择。实验结果表明，该文提出的方法与传统方法相比，提高了文本分类的准确率。     

特征序列分析方法在文本分类中的应用

把计算生物学中DNA序列分析的一种方法应用到文本分类中，通过分析文档集所产生的可描述类别内在特征的特征序列，给出了一种文本分类方法SSAM，并在Reuters21578数据集上和其它几种常见分类方法的分类效果进行了比较，实验结果显示SSAM的分类效果优于传统的贝叶斯方法，而且具有较快的分类速度。     

一种快速的判别公共向量分类方法

在传统DCV的基础上,提出了一种改进的快速DCV分类方法。该方法与传统的DCV分类方法相比,在保证识别率相同的情况下具有较快的分类速率。传统的DCV分类方法通过计算特征向量之间的距离来进行分类,而所提快速DCV分类方法则通过标量计算完成分类。理论分析及复杂度计算表明,快速DCV分类方法的分类速率是传统DCV分类方法的2倍左右,在Yale、ORL和PIE 3种人脸数据库得到的对比仿真实验结果验证了该算法的有效性。     

食材数据库统计与对比实验性能分析

目的 计算机智能分析用户的饮食是一项有意义的研究课题。传统的分析方法侧重于分析食物类型,可是中国是个美食之国,食物类型在各个区域间表现出极大的多样性,造成很难实现通用的食物自动分类方法。为此尝试针对食物的原材料,即食材进行自动分析收集并建立了一个真实环境下的食材图像数据库（FOOD-SCUT）,此数据库包括目前中国市面上常见的70种食材类别,共8 015幅图片。方法 基于此数据库,本文尝试性地利用不同的传统特征和分类方法,对此食材图像数据库进行自动分类,以此来分析对比各种特征和分类方法的性能。对比性实验中所选用的特征包括：SIFT特征、颜色直方图特征、梯度直方图、SURF特征、LBP特征和Gabor特征等。除颜色直方图外其他特征都会利用词袋模型进行特征编码,而所选用分类方法包括：支持向量机（SVM）、朴素贝叶斯、随机森林、K-最近邻（KNN）算法。另外本文还尝试采用最近流行的深度神经网络方法对数据库进行特征学习和分类。结果 通过实验验证基于各种传统特征分类方法的实验性能,其中各种特征包括单特征和多特征组合两种方式,通过不断调整不同特征组合和分类识别算法及其参数,得到基于传统特征分类方法的最好分类性能。同时通过实验验证深度卷积神经网络模型的实验性能,深度卷积神经网络模型使用直接训练和预训练两种不同训练模式,并调整不同的网络层数和权重初始化方法后获得最好的分类识别性能。本食材数据库基于传统特征分类方法的最好分类准确率为88.98%,而基于深度神经网络分类方法上可以获得最佳的实验性能,即95.7%,这个准确率比基于传统特征分类方法高出6.72%。结论 数据库的统计结果表明此食材图像数据库类内数据具有极大的差异,可以作为分析食材的一个基础数据库。此数据库具有极高的应用价值,可以为后续各种基于食材分析应用提供相关分析数据,并且本文实验分析结果,对于后续用户开发相关的各种相关应用中,提供了模型和参数选择的建议,节省了用户选择模型和调参的实验过程。     

从戏曲之维到动画之度：枣庄柳琴戏的活态传承与教学实践

文章基于青少年角度对传统地方戏曲的接受程度,研究动画艺术在传统地方戏曲枣庄柳琴戏的活态传承中的跨界创新应用,利用数字化语言、技术、媒介并整合相关资源回归中国传统文化,能够对戏曲起到数字化保护作用。动漫艺术作为数字化手段之一,对推进地方戏曲活态传承研究维度、打造地方文化名片有重要意义;戏曲动画创作有助于全民参与文化建设,促进当地戏曲的传播与发展。     

脸谱艺术的视觉表现形式研究

脸谱艺术的现代化是中国传统文化传承和发展的必然结果,这种传统与现代结合的艺术表现形式正在深化。文章主要简述戏曲脸谱的含义和分类,以及脸谱艺术在视觉表达中的主要形式,为脸谱艺术的进一步传承提出建议。     

一种基于信息增益的特征优化选择方法

特征选择是文本分类的一个重要环节,它可以有效提高分类精度和效率。在研究文本分类特征选择方法的基础上,分析了信息增益方法的不足,将频度、集中度、分散度应用到信息增益方法上,提出了一种基于信息增益的特征优化选择方法。实验表明,该方法在分类效果与性能上都优于传统方法。     

深度学习对不同分辨率影像冬小麦识别的适用性研究

定量分析遥感影像尺度与分类精度之间的关系是进行土地覆盖分类的基础。深度学习具有从底层到高层特征非监督学习的能力，解决了传统分类模型中需要人工选择特征的问题。这种新型的分类方法的分类精度是否受到不同分辨率尺度影响，有待研究。利用深度卷积神经网络（Deep Convolutional Neural Network, DCNN）——金字塔场景分析网络（Pyramid Scene Parsing Network, PSPNet）进行4种分辨率（8、3.2、2和0.8 m）的米级、亚米级影像冬小麦分类。实验结果表明: PSPNet能够有效地进行大样本的学习训练，非监督提取出空间特征信息，实现“端—端”的冬小麦自动化分类。不同于传统分类器分类精度与分类尺度之间的关系，随着影像分辨率的逐步增高，地物表达特征越来越清晰，PSPNet识别的冬小麦精度会逐步增高，识别地块结果也越来越规整，不受分辨率尺度的影响。这对于选择甚高亚米级影像提高作物分类精度提供了实验基础。     

京剧中典型唱腔和伴奏的自动分类研究

论文使用音频分析技术和模式识别技术相结合的方法对传统京剧中的3种典型角色(生、旦、净)的唱腔和2种典型的纯伴奏形式(文场和武场)进行了基于内容的自动分类研究。实验测试数据包括266个片段,来自于许多著名京剧演员如梅兰芳、袁世海、于魁智等人的演出。实验结果表明,对于5类分类问题可以达到88.7%的平均分类正确率,对于有伴奏下的唱腔和纯伴奏之间的2类分类问题可以取得高达96.6%的平均分类正确率。这一结果对京剧的进一步研究有着重要意义。     

典型的音频分类算法

音频分类是提取音频结构和内容语义的重要手段,是基于内容的音频检索和分析的基础.本文对几种常用的音频分类算法作了综述,介绍了最小距离法、神经网络、支持向量机、决策树方法、隐马尔可夫模型等典型算法的特征,并对它们的优缺点进行了比较.     

A general audio classifier based on human perception motivated model

The audio channel conveys rich clues for content-based multimedia indexing. Interesting audio analysis includes, besides widely known speech recognition and speaker identification problems, speech/music segmentation, speaker gender detection, special effect recognition such as gun shots or car pursuit, and so on. All these problems can be considered as an audio classification problem which needs to generate a label from low audio signal analysis. While most audio analysis techniques in the literature are problem specific, we propose in this paper a general framework for audio classification. The proposed technique uses a perceptually motivated model of the human perception of audio classes in the sense that it makes a judicious use of certain psychophysical results and relies on a neural network for classification. In order to assess the effectiveness of the proposed approach, large experiments on several audio classification problems have been carried out, including speech/music discrimination in Radio/TV programs, gender recognition on a subset of the switchboard database, highlights detection in sports videos, and musical genre recognition. The classification accuracies of the proposed technique are comparable to those obtained by problem specific techniques while offering the basis of a general approach for audio classification.

Music classification as a new approach for malware detection

Each year, a huge number of malicious programs are released which causes malware detection to become a critical task in computer security. Antiviruses use various methods for detecting malware, such as signature-based and heuristic-based techniques. Polymorphic and metamorphic malwares employ obfuscation techniques to bypass traditional detection methods used by antiviruses. Recently, the number of these malware has increased dramatically. Most of the previously proposed methods to detect malware are based on high-level features such as opcodes, function calls or program’s control flow graph (CFG). Due to new obfuscation techniques, extracting high-level features is tough, fallible and time-consuming; hence approaches using program’s bytes are quicker and more accurate. In this paper, a novel byte-level method for detecting malware by audio signal processing techniques is presented. In our proposed method, program’s bytes are converted to a meaningful audio signal, then Music Information Retrieval (MIR) techniques are employed to construct a machine learning music classification model from audio signals to detect new and unseen instances. Experiments evaluate the influence of different strategies converting bytes to audio signals and the effectiveness of the method.

     

Rough Set Based Automatic Classification of Musical Instrument Sounds

Automatic classification of audio data arose increasing interest recently. This paper addresses the problem of automatic recognition of musical instrument sounds, applying rough set based techniques as a tool of classification. Instruments representing wind and string families were used in the experiments. Since the main problem in case of audio data is the proper parameterization, we also investigated issues regarding various parameterization methods. Fourier transform and wavelet analysis were applied as parameterization tools. The obtained feature vectors were tested using rough set tools. The analyzed data represent singular sounds of full musical range of 11 musical instruments, played with various articulation techniques. Results of experiments are presented and discussed in this paper. We summarize our paper with conclusions on musical signal representation for timbre classification purposes.     

Regional classification of Chinese folk songs based on CRF model

Music regional classification, which is an important branch of music automatic classification, aims at classifying folk songs according to different regional style. Chinese folk songs have developed various regional musical styles in the process of its evolution. Regional classification of Chinese folk songs can promote the development of music recommendation systems which recommending proper style of music to users and improve the efficiency of the music retrieval system. However, the accuracy of existing music regional classification systems is not high enough, because most methods do not consider temporal characteristics of music for both features extraction and classification. In this paper, we proposed an approach based on conditional random field (CRF) which can fully take advantage of the temporal characteristics of musical audio features for music regional classification. Considering the continuity, high dimensionality and large size of the audio feature data, we employed two ways to calculate the label sequence of musical audio features in CRF, which are Gaussian Mixture Model (GMM) and Restricted Boltzmann Machine (RBM). The experimental results demonstrated that the proposed method based on CRF-RBM outperforms other existing music regional classifiers with the best accuracy of 84.71% on Chinese folk songs datasets. Besides, when the proposed methods were applied to the Greek folk songs dataset, the CRF-RBM model also performs the best.

     

Audio-visual sports highlights extraction using Coupled Hidden Markov Models

We present our studies on the application of Coupled Hidden Markov Models(CHMMs) to sports highlights extraction from broadcast video using both audio and video information. First, we generate audio labels using audio classification via Gaussian mixture models, and video labels using quantization of the average motion vector magnitudes. Then, we model sports highlights using discrete-observations CHMMs on audio and video labels classified from a large training set of broadcast sports highlights. Our experimental results on unseen golf and soccer content show that CHMMs outperform Hidden Markov Models(HMMs) trained on audio-only or video-only observations. Next, we study how the coupling between the two single-modality HMMs offers improvement on modelling capability by making refinements on the states of the models. We also show that the number of states optimized in this fashion also gives better classification results than other number of states. We conclude that CHMMs provide a promising tool for information fusion techniques in the sports domain for audio-visual event detection and analysis.     

多媒体数据库中基于内容的音频分类综述

根据不同的应用背景和分类对象,分别概述了多媒体数据库中基于内容的音频分类的一些关键技术,如特征提取和分类器设计,并分析了各种基于内容的音频分类方法的优缺点,讨论了存在的问题,指出了未来的研究方向。     

Content-based audio classification and segmentation by using support vector machines

Content-based audio classification and segmentation is a basis for further audio/video analysis. In this paper, we present our work on audio segmentation and classification which employs support vector machines (SVMs). Five audio classes are considered in this paper: silence, music, background sound, pure speech, and non- pure speech which includes speech over music and speech over noise. A sound stream is segmented by classifying each sub-segment into one of these five classes. We have evaluated the performance of SVM on different audio type-pairs classification with testing unit of different- length and compared the performance of SVM, K-Nearest Neighbor (KNN), and Gaussian Mixture Model (GMM). We also evaluated the effectiveness of some new proposed features. Experiments on a database composed of about 4- hour audio data show that the proposed classifier is very efficient on audio classification and segmentation. It also shows the accuracy of the SVM-based method is much better than the method based on KNN and GMM.