【摘要】：隨著當(dāng)今社會的迅速信息化,各種多媒體信息資料飛速發(fā)展。音樂作為一門藝術(shù),已經(jīng)成為人類生活中必備的部分。一直以來,音樂都是人們表達情感的渠道,可以為歡樂而歌,可以為悲傷而唱。如今紙上的音樂已經(jīng)不能夠滿足音樂的保存、檢索以及音樂人之間的交流。隨著信息時代的到來,計算機音樂的研究成了一個新的課題。讓計算機完成我們?nèi)祟惸軌蛲瓿傻氖虑橐恢笔侨藗冊噲D努力的方向。目前,我們可以通過計算機進行音樂的播放、制作和存儲等,通過計算機對音樂的情感進行分析也漸漸興起,使計算機能夠通過“聽”音樂自動識別出音樂所表達的情感。本文就音樂情感自動分析做了深入的研究。本文的音樂情感分析模型由三個部分構(gòu)成:音樂特征向量模型、音樂情感模型和分類認知模型。音樂特征向量模型是由從音樂中提取的一些特征組成的一個八維向量。在音樂特征向量模型的部分,本文在介紹了旋律面積的概念之后,定義了音樂能量的概念,并提出了自己的方法,即利用音樂能量為音樂劃分樂段,針對每個樂段使用數(shù)字音樂特征提取技術(shù)提取樂段的速度、旋律的方向、力度、節(jié)拍、節(jié)奏變化、大三度、小三度和音色等八個特征,然后利用音樂情感模型和分類認知模型對每個樂段的情感進行分析。音樂情感模型是音樂情感的描述,本文介紹了幾種研究者常用的音樂情感模型,包括Hevner情感環(huán)、Thayer情感模型和情感語義模型等等,并對這些模型的優(yōu)缺點進行了比較。我們將Hevner情感環(huán)與情感語義模型相結(jié)合,得到了由Hevner情感環(huán)中的八大類情感描述所構(gòu)成的情感向量模型,并將該模型作為本文實驗所用的情感模型。分類認知模型是通過算法將音樂特征模型映射到情感模型,即分類認知的過程是一個模式識別的過程。在分類認知模型部分,簡單介紹了幾種模式識別方法并對它們的優(yōu)缺點進行對比之后,選用BP神經(jīng)網(wǎng)絡(luò)作為本文的認知模型。針對音樂情感分析的需求,本文對BP神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)過程做了改進,使其能夠更加符合音樂情感分析的主觀性的特點。最后,本文將上述三部分自然地結(jié)合起來,構(gòu)成了一個完整的音樂情感分析模型。之后,對該模型的功能和性能進行了實驗驗證,并將實驗結(jié)果與已有研究的實驗成果進行比較,結(jié)果顯示,使用本文所提出的方法構(gòu)建的音樂情感分析模型能夠較好地對數(shù)字音樂進行情感分析,并且與已有成果相比,具有更高的準(zhǔn)確率。
[Abstract]:With the rapid development of information, various multimedia information materials are developing rapidly. As an art, music has become an essential part of human life. Music has always been a channel for people to express their feelings, to sing for joy and to sing for sorrow. Today, the music on paper can no longer satisfy the preservation, retrieval and communication between musicians. With the arrival of the information age, the research of computer music has become a new subject. Making computers do what we humans can do has always been the direction of our efforts. At present, we can play, make and store music by computer, and analyze the emotion of music by computer, so that the computer can automatically recognize the emotion expressed by music through listening to music. This article has done the thorough research to the music emotion automatic analysis. The music emotion analysis model consists of three parts: music feature vector model, music emotion model and classified cognitive model. The music feature vector model is an eight-dimensional vector composed of some features extracted from music. In the part of the music feature vector model, after introducing the concept of melodic area, this paper defines the concept of music energy, and puts forward its own method, that is, using music energy to divide music segments. For each segment, using digital music feature extraction technology to extract eight features, such as speed, direction of melody, intensity, rhythm, rhythm change, big third degree, small third degree and timbre, etc. Then the emotion of each segment is analyzed by using musical emotion model and classified cognitive model. Music emotion model is the description of music emotion. This paper introduces several musical emotion models commonly used by researchers, including Hevner emotional loop, Thayer emotional model and emotional semantic model, and compares the advantages and disadvantages of these models. We combine the Hevner emotional loop with the affective semantic model and obtain the emotional vector model which is composed of eight kinds of affective description in the Hevner emotional loop and use this model as the emotional model used in this experiment. Classifying cognitive model is to map music feature model to affective model through algorithm, that is, the process of classifying cognition is a process of pattern recognition. In the part of classified cognitive model, several pattern recognition methods are briefly introduced and their advantages and disadvantages are compared. Then BP neural network is selected as the cognitive model in this paper. According to the demand of music emotion analysis, this paper improves the learning process of BP neural network, so that it can more accord with the subjective characteristics of music emotion analysis. Finally, the above three parts are naturally combined to form a complete musical emotional analysis model. After that, the function and performance of the model are verified by experiments, and the experimental results are compared with the existing experimental results. The results show that, Using the method proposed in this paper, the music emotion analysis model can be used to analyze the digital music emotion better, and compared with the existing results, the model has a higher accuracy.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN912.3

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相關(guān)碩士學(xué)位論文 前1條

1 鐘子岳;基于數(shù)據(jù)挖掘技術(shù)的音樂風(fēng)格分類方法的研究[D];南昌大學(xué);2013年

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本文編號：2288731