Detrended Fluctuation Analysis of the Human EEG during Listening to Emotional Music

A nonlinear method named detrended fluctuation analysis （DFA） was utilized to investigate the scaling behavior of the human electroencephalogram （EEG） in three emotional music conditions （fear, happiness, sadness） and a rest condition （eyes-closed）. The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides α band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the ＇approach-withdrawal＇ model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.     

Practical algorithms for transposition-invariant string-matching

We consider the problems of (1) longest common subsequence (LCS) of two given strings in the case where the first may be shifted by some constant (that is, transposed) to match the second, and (2) transposition-invariant text searching using indel distance. These problems have applications in music comparison and retrieval. We introduce two novel techniques to solve these problems efficiently. The first is based on the branch and bound method, the second on bit-parallelism. Our branch and bound algorithm computes the longest common transposition-invariant subsequence (LCTS) in time O((m²+loglogσ)logσ) in the best case and O((m²+logσ)σ) in the worst case, where m and σ, respectively, are the length of the strings and the size of the alphabet. On the other hand, we show that the same problem can be solved by using bit-parallelism and thus obtain a speedup of O(w/logm) over the classical algorithms, where the computer word has w bits. The advantage of this latter algorithm over the present bit-parallel ones is that it allows the use of more complex distances, including general integer weights. Since our branch and bound method is very flexible, it can be further improved by combining it with other efficient algorithms such as our novel bit-parallel algorithm. We experiment on several combination possibilities and discuss which are the best settings for each of those combinations. Our algorithms are easily extended to other musically relevant cases, such as δ-matching and polyphony (where there are several parallel texts to be considered). We also show how our bit-parallel algorithm is adapted to text searching and illustrate its effectiveness in complex cases where the only known competing method is the use of brute force.     

利用Windows计划任务设计广播站自动化播出

研究利用Windows的计划任务来解决扩音机和电脑的自动开关及建立相关音乐曲库、自动播放任务，以此实现广播系统的自动化播出。     

音乐物联网的设计与研究

音乐物联网(Internet of musical things,IoMusT)是一个涉及多学科、多专业的综合工程,以往的研究都是单一的从某一个方面着手,缺乏一个统一的研究框架,完整的研究体系还不健全。研究重点是从工程的角度定义音乐事件,并结合当下互联网各个环节的技术特性,从工程的角度定义音乐事件,构造一个小范围的IoMusT生态系统,从软硬件方面测试IoMusT性能,进而验证IoMusT设计架构效。     

谱曲软件的开发与应用

随着科学技术的飞速发展,以及人民物质精神文化生活质量的逐渐提高,人们对于音乐的追求也越来越高。大型的演唱会和各类综合文艺晚会不仅要求演员艺术水平高超,对音乐效果和音乐表现力也有了较高需求。由此,MID（I乐器数字接口）应运而生。通过MIDI技术可以将不同乐器的优势集中到一起,形成其他乐器无法创作比拟的特殊音乐效果,但是,MIDI技术无法实现对音乐作品乐谱的分析。因此,迫切需要一种能够实现乐谱分析的软件程序对MIDI乐谱文件进行解析,实时翻译成乐谱来满足演奏者的需求。本文基于以上背景,提出了一种解析MIDI文件的谱曲软件分析工具开发方案,可以实现对MIDI乐谱文件的分析,将其翻译成对应的乐谱,使乐谱能够直观展示在演奏者面前。     

基于样本的流行歌曲关键段分割方法

流行歌曲的关键段为歌曲中最能打动人、给人印象最深刻的一个完整片段.将它分割出来可用于音乐试听和基于内容的音乐分类、检索、管理.通过对人工截取的样本进行分析,本文提出了一种流行歌曲关键段自动分割方法.实验结果表明,此方法可以比较准确和有效地分割出流行歌曲中的关键段.     

多媒体技术对民族民间音乐的推动与保护作用--以中国民族民间音乐为例

民间音乐是我国古老文化中的一颗璀璨夺目的明珠，文章从民族民间音乐的定义和地位出发，重点分析了民族音乐的具体特征。现代互联网技术，将这些特征进行更好的发扬，在保证了传统民间音乐的精华的同时，给民间音乐注入了更多新的活力，目前，多媒体技术，将民族音乐带入了课堂、带入了影视行业，为我们民族音乐的更好发展提供了更为宽广的途径。     

Correlations in a Mozart's music score (K-73x) with palindromic and upside-down structure

In this work, we study long-range correlations in a “Scherzo-Duetto di Mozart” score (K-73x) for two violins. This is a fascinating piece, as the second violin part is upside down on the same sheet below the first violin, and some parts are like a palindrome. Given such ingenious structure, it is expected the existence of long-range correlations in the score structure. In order to quantify long-range correlations, we considered the music score as a sequence of integer numbers, each of them corresponding to last common denominator units of note. By using detrended fluctuation analysis (DFA), correlations are quantified by means of the scaling exponent that reflects the type of correlations for a given distance between neighbors note. The following conclusions can be drawn from the analysis: (a) For about 10-25 neighbor note distances, correlations are similar to 1/f-noise. This is an interesting finding since it has been shown that pleasant sounds for humans display a behavior similar to 1/f noise. (b) As the neighbor note distance increases, the long-range correlations decays continuously. For some score sections, the music score behaves like non-correlated (i.e., purely random) noise. Summing up, the results show that the studied Mozart's score contains a certain degree of correlation for relatively small note distances, and becomes close to non-correlated behavior for long note distances. We considered also the sequence constructed by considering the distance between the simultaneously played notes of the two violins. Interestingly, for relatively small neighbor note distances, a scaling behavior similar to that found for individual violins is also displayed. In some sense, this is an expression of the specific structure (palindromes plus upside down construction) used by Mozart in the composition of this music score. Although we focused on a particular high-art music score, our results suggest that modern methods borrowed from statistical physics can be useful for the systematic study of music composition techniques.     

New Interfaces and Approaches to Machine Learning When Classifying Gestures within Music

Interactive music uses wearable sensors (i.e., gestural interfaces—GIs) and biometric datasets to reinvent traditional human–computer interaction and enhance music composition. In recent years, machine learning (ML) has been important for the artform. This is because ML helps process complex biometric datasets from GIs when predicting musical actions (termed performance gestures). ML allows musicians to create novel interactions with digital media. Wekinator is a popular ML software amongst artists, allowing users to train models through demonstration. It is built on the Waikato Environment for Knowledge Analysis (WEKA) framework, which is used to build supervised predictive models. Previous research has used biometric data from GIs to train specific ML models. However, previous research does not inform optimum ML model choice, within music, or compare model performance. Wekinator offers several ML models. Thus, we used Wekinator and the Myo armband GI and study three performance gestures for piano practice to solve this problem. Using these, we trained all models in Wekinator and investigated their accuracy, how gesture representation affects model accuracy and if optimisation can arise. Results show that neural networks are the strongest continuous classifiers, mapping behaviour differs amongst continuous models, optimisation can occur and gesture representation disparately affects model mapping behaviour; impacting music practice.