基于双全正弦的计算机乐器音色建模

提出了一种基于双全正弦的计算机乐器音色模型,以达到通过调节参数来改变计算机乐器音色的目的.该模型包括振动子模型和振幅包络子模型.保持了振幅包络参数取值不变,定义了调节音色的参数,分别为振幅系数、周期系数和方差系数.在VC++环境下,利用一维离散余弦变换获取该模型在不同参数下的频谱.通过分析音色参数对频谱的影响,找到这些参数对乐器音色的作用规律.实验表明,在振幅系数和周期系数都相等时,可将音高整体提高一个8度.在振幅系数和周期系数其中之一不等时,方差的值越大,音色的频谱就越丰富,乐器音色就越响亮;反之音色就越暗淡.对计算机乐器音色的合成和音乐的计算机生成具有一定的科学意义和实用价值.该模型简单,便于推广.     

一种可控虚拟乐器音色模型

为了构建虚拟乐器,提出了一种可控音色模型.该模型包括频谱子模型和振幅包络子模型.保持振幅包络子模型参数不变,调节频谱子模型参数,对音色效果进行分析,找到这些参数对乐器音色的作用规律.分析和实验表明,该模型可提供对音量、衰减快慢、泛音宽度和泛音位置等方面的控制.与以往硬件合成音色的方法相比,该软件方法可节省大量的成本,同时实现音色的可控性.对软件乐器音色的构建和算法作曲具有一定的理论价值和基础意义.     

悲伤型乐器的音色建模技术研究

提出了一种悲伤型乐器的音色模型,为基于情感的算法作曲提供音响基础。采集擅长表达悲伤情感的乐器的乐音样本,利用离散傅里叶变换获取频谱特征,通过对乐器波形的分析计算获取包络特性,最终构建了一种悲伤型乐器的音色模型。通过实验验证了由该模型产生的悲伤型音色及模型本身具有良好的可调节性。与传统的录制形式相比,该方法成本低,灵活性强,对计算机音乐技术的发展和算法作曲的情感化也起着重要的推动作用。     

学前儿童乐曲的计算机生成研究

对学前儿童乐曲的计算机生成方法进行了研究,将乐谱转化为音频文件并构建软件乐器.首先建立振动模型,在此基础上,建立音色模型,包括振幅包络子模型和频谱子模型.对乐谱中的信息,包括音高、调号等进行编码,建立表示绝对音高与相对音高、调号之间关系的音高模型.利用音高模型和十二平均律完成相对音高到振动频率的映射,再利用音色模型生成WAV音频文件.与传统的演奏结合录制生成音乐的模式相比,该方法可节省演奏的人力成本和硬件乐器的物力成本,提高音质和产出效率,并享受众多的创意,更能满足学前儿童的需求.     

病毒传播与级联故障相互作用过程的研究

为研究聚类系数对病毒传播与级联故障相互作用的影响,提出一种改进的病毒传播与级联故障相互作用模型。通过改变平均度和三角连接概率调节网络聚类系数,以此观察病毒传播与级联故障相互作用过程。当不考虑三角连接概率时,平均度越小,网络抵制相互作用的能力越强,且区分度越明显,但也会增强级联故障子过程的破坏力。当平均度较小时,三角连接概率越大,网络抵制相互作用的能力越弱。当平均度较大时,不同三角连接概率对应的网络抵制相互作用的效果相似。仿真结果表明,对比单一病毒传播模型,相互作用模型在同一时刻的破坏力更大。     

共轴非对称球面光学系统仿真模型设计

为了优化共轴非对称球面光学系统的设计,对共轴非对称球面光学系统进行了数字化模型的开发,设计了基于计算机仿真的数学模型。在此基础上运用模块化程序设计思想,设计了仿真模型的模块化结构,利用交互技术解决了实验参数的人机交互,并开发了仿真模型核心模块的程序代码,建立了完整的基于计算机系统的共轴非对称球面光学系统仿真模型。该仿真模型实现了系统参数变化时成像效果的自动跟踪模拟,可以为共轴非对称球面光学系统的优化设计提供效果仿真。     

基于GMM的歌曲Morphing算法

针对业余歌手模仿专业歌手唱歌过程中音色不变的问题,提出一种基于高斯混合模型(GMM)的中文歌曲Morphing算法,采用GMM对语音频谱建模,并通过混合业余歌手和专业歌手的语音频谱,实现歌曲的音色转换。结果显示,混合比例因子k=0或1时,ABX测试正确率均为100%,0相似文献   

三角域上曲面模型的正交重构方法

为了在数字几何信息处理中采用信号处理中常用的频谱分析方法,需要选择恰当的正交系.通过给出三角域上一类正交系(称为三角域上二次V-系统)的精确数学表达,作为这类新的正交系的一个具体应用,提出一种曲面模型的频谱分析方法.该方法将由分片二次曲面构成的几何模型在三角域二次V-系统下展开,得到相应的频谱,利用这些频谱对几何信息进行精确重构,实现了几何对象的正交表达.与已有方法相比,采用文中提出的用二次V-系统表达的几何模型可使数据量大大减少.最后通过实验结果表明该方法是切实可行的.     

广义Logistic模型的Hopf分支与计算机仿真

研究了一类含时滞且含干扰和收获率的广义Logistic模型的Hopf分支周期解。得到该模型正平衡态存在唯一的充要条件,利用特征值理论得到该模型产生Hopf分支的条件;利用周期函数正交性方法得到其近似周期解的表达式;运用计算机仿真,给出了参数取不同数值时的曲线拟合图,讨论了参数对周期解的周期、振幅及正平衡态的影响。     

聚类系数和度相关性均可调的HK扩展模型

现有的社交网络增长演化模型的度相关性大多为负值。针对这种情况,以HK（Holme和Kim）模型为基础,考虑社交网络中度的正相关特性以及高聚类系数的特征,提出一种适用于构造社交网络的演化增长模型。首先,对现实中的社交网络拓扑结构进行分析,获取真实社交网络的一些重要拓扑参数;然后,通过引入改进的三角连接机制,对HK模型进行改进以实现网络的聚类系数和相关性均可调的目的,称其为聚类系数和度相关性均可调的HK扩展模型（HK-TDC&C）,通过该模型可以构造各种拓扑结构的网络。最后,利用平均场理论对该模型的度分布进行分析,并采用Matlab进行数值仿真,计算网络的其他拓扑参数。实验结果表明：通过调节择优参数和连接概率,用HK-TDC&C构造的社交网络可以满足社交网络的基本特性：无标度特性、小世界特性、高聚类系数特性、度正相关特性,其拓扑结构更接近真实社交网络。     

A Modeling of Singing Voice Robust to Accompaniment Sounds and Its Application to Singer Identification and Vocal-Timbre-Similarity-Based Music Information Retrieval

This paper describes a method of modeling the characteristics of a singing voice from polyphonic musical audio signals including sounds of various musical instruments. Because singing voices play an important role in musical pieces with vocals, such representation is useful for music information retrieval systems. The main problem in modeling the characteristics of a singing voice is the negative influences caused by accompaniment sounds. To solve this problem, we developed two methods, accompaniment sound reduction and reliable frame selection. The former makes it possible to calculate feature vectors that represent a spectral envelope of a singing voice after reducing accompaniment sounds. It first extracts the harmonic components of the predominant melody from sound mixtures and then resynthesizes the melody by using a sinusoidal model driven by these components. The latter method then estimates the reliability of frame of the obtained melody (i.e., the influence of accompaniment sound) by using two Gaussian mixture models (GMMs) for vocal and nonvocal frames to select the reliable vocal portions of musical pieces. Finally, each song is represented by its GMM consisting of the reliable frames. This new representation of the singing voice is demonstrated to improve the performance of an automatic singer identification system and to achieve an MIR system based on vocal timbre similarity.      

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.     

Automatic Recognition of Isolated Monophonic Musical Instrument Sounds using kNNC

The instrument recognition system described in this paper classifies isolated monophonic musical instrument sounds using six features: cepstral coefficients, constant Q transform frequency spectrum, multidimensional scaling analysis trajectories, RMS amplitude envelope, spectral centroid and vibrato. Sounds from nineteen instruments of definite pitch, covering the note range C3–C6 and representing the major musical instrument families and subfamilies were used to test the system. Nearest neighbor classification was utilised and results were evaluated in terms of accuracy and reliability. Using the leave-one-out test strategy yielded an accuracy of 93% in instrument recognition, 97% in instrument family recognition, and 100% for sustain/impulsive instruments.     

Identification of a dominating instrument in polytimbral same-pitch mixes using SVM classifiers with non-linear kernel

In this paper we deal with the problem of identification of the dominating musical instrument in a recording containing simultaneous sounds of the same pitch. Sustained harmonic sounds from one octave of twelve instruments were considered. The training data set contains isolated sounds of two forms, one from selected musical instruments, and the other from the same mixed with artificial harmonic and noise sounds of lower amplitude. The test data set contains mixes of musical instrument sounds. A Support Vector Machine classifier was used for training and testing experiments, using a non-linear kernel. Additionally, we performed tests on data based on different recordings of instruments than those used in the training procedure described above. Results of these experiments are presented and discussed.     

The influence of polyphony on the dynamical modelling of musical timbre

This letter addresses the problem of pattern recognition of polyphonic musical timbre. Frame-level dynamics of audio features are particularly difficult to model, although they have been identified as crucial perceptive dimensions of timbre perception. Recent studies seem to indicate that traditional means to model data dynamics, such as delta-coefficients, texture windows or Markov modelling, do not provide any improvement over the best static models for real-world, complex polyphonic textures of several seconds’ length. This contradicts experimental data on the perception of individual instrument notes. This letter describes an experiment to identify the cause of this contradiction. We propose that the difficulty of modelling the dynamics of full songs results either from the complex structure of the temporal succession of notes, or from the vertical polyphonic nature of individual notes. We discriminate between both hypothesis by comparing the performance of static and dynamical algorithms on several specially designed datasets, namely monophonic individual notes, polyphonic individual notes, and polyphonic multiple-note textures. We conclude that the main cause of the difficulty of modelling dynamics of real-world polyphonic musical textures is the polyphonic nature of the data.     

A model for pattern perception with musical applications part III: The graph embedding of pitch structures

This is the third paper of a series which begins by treating the perception of pitch relations in musical contexts and the perception of timbre and speech. The preceding papers dealt with those properties of musical scales which allow them to function as reference frames which provide both for the measurement of intervals and for the identification of their elements as scale degrees. The effect of these properties upon the perceptibility of various musical relations and properties has been discussed. Here we extend the treatment to systems of different scales (as exist in many musical cultures) where a listener's recognition of any one scale in the system interacts with his ability to recognize the others. Reading of the two previous papers is required.This research was supported in part by grants and contracts AF-AFOSR 881–65, AF 49(638)-1738 and AF-AFOSR 68–1596.