音乐情感识别研究综述

音乐是表达情感的重要载体,音乐情感识别广泛应用于各个领域.当前音乐情感研究中,存在音乐情感数据集稀缺、情感量化难度大、情感识别精准度有限等诸多问题,如何借助人工智能方法对音乐的情感趋向进行有效的、高质量的识别成为当前研究的热点与难点.总结目前音乐情感识别的研究现状,从音乐情感数据集、音乐情感模型、音乐情感分类方法三方面...     

基于情感音乐模板的音乐检索系统研究

传统的基于文本信息描述的音乐检索技术已经无法满足人们对检索智能化的需求,于是产生了基于内容的音乐检索方法.在此基础上将情感需求引入到检索中,对基于情感的音乐检索方法及模型进行了相关研究.首先构建了音乐情感空间来获得用户的情感描述;然后通过对情感音乐模型进行定义提出了情感音乐模板库,以得到满足用户情感需求的匹配模板;最后,在此基础上提出了基于情感音乐模板的音乐检索系统模型,力求探讨出一种基于情感的有效检索方法.     

一种分段式音乐情感识别方法

为了体现音乐情感跌宕起伏的变化,本文将乐曲划分为音符、小节和乐段,并提出一种分段式音乐情感识别方法.该方法从MIDI文件中提取音符特征,根据音符特征提取小节特征,并根据若干相邻小节的相似性将乐曲划分成若干独立的乐段,在提取乐段特征后利用BP神经网络识别乐段情感,最终获得整首乐曲的情感.实验结果表明,本文提出的音乐情感识别方法具有较好的识别效果.     

基于兴趣情感算法的音乐推荐系统设计

当前在线使用的音乐平台大多数根据用户的播放历史来进行音乐推荐,这种推荐模式虽然在大多数环境下能够符合用户的需求,却无法根据用户心情的变化对推荐音乐的类别进行相应的调整.所设计的音乐推荐平台将用户的情绪状态融入到音乐推荐规则之中,在进行双向情感分析的基础上通过两种并行模式实现平台功能.通过实验证明,所设计的音乐推荐平台感...     

音乐信息中情感特征的提取和研究

本文围绕音乐信息中的情感识别问题,着重论述如何对音乐中的情感信息进行处理,准确的从音乐信息中提取情感特征向量．从而为后一步所采用的各种情感识别方法提供可靠的依据。     

基于统计的音乐摘要研究

随着Internet的发展．大量音乐资源在网络上涌现,对音乐资源进行有效检索标识是很有必要的．而摘要的意义显得尤为重要。音乐摘要是文本摘要研究在音乐检索领域的一个比较新的应用。综合使用统计相关原理和文本挖掘相关研究理念,对音乐资源进行一定研究．为音乐检索提供新的参考。     

基于统计的音乐摘要研究

随着Intemet的发展,大量音乐资源在网络上涌现,对音乐资源进行有效检索标识是很有必要的,而摘要的意义显得尤为重要.音乐摘要是文本摘要研究在音乐检索领域的一个比较新的应用.综合使用统计相关原理和文本挖掘相关研究理念,对音乐资源进行一定研究,为音乐检索提供新的参考.     

音乐粗情感域中的软切割及分类方法

针对音乐灯光表演控制系统无法自动获取其控制所需的音乐特征信息,结合传统的Arousal-Valence模型提出了一种可用于音乐灯光表演的音乐粗情感模型。针对此模型,通过小波分析中的Mallat算法提取比较项并采用强度、节奏比值判断法,对音乐片段进行两次“软切割”,再根据相应的产生式专家系统规则便能够很好地对其进行粗情感域中的分类及特征量提取。仿真结果表明,该方法能够有效地按音乐情感将音乐片段分类,同时能够提取出满足音乐灯光表演控制系统时域上对音乐分段时间节点的高精度要求。     

基于内容的音频与音乐分析综述

机器听觉包括三大研究领域:语音信号处理与识别、一般音频信号分析、基于内容的音乐信号分析.其中,语音信号处理与识别早已成为一个传统的研究热点.随着信息科学与技术的迅速发展,基于内容的音频与音乐信号分析也逐渐成为一个新的研究热点,近几年来取得了大量研究成果.文章将对1990年以后该领域上所取得的研究成果进行综述,包括基于内容的音频或音乐信号自动分类、分割、检索以及音乐作品自动分析等内容.     

音乐评论的情感挖掘研究

在当今数字化与网络化时代,图像、音频和视频等多媒体内容已成为互联网信息高速公路上传送数据的重要部分,基于音乐情感的检索技术应运而生.音乐的群众情感则由大量的普通用户通过各种形式表达出来,其中论坛中带交互性的音乐评论则为典型的音乐情感表达形式.从海量Web文本信息中得到音乐的情感等属性并将结果合理地返回给依据情感搜索的用户是一个创新性的解决方案.实践结果证明,音乐论坛主题挖掘系统相对于目前的基于音乐本身的研究具有更大的合理性,对用户的搜索返回具有更强的说服力.     

Affective content analysis of music emotion through EEG

Emotion recognition of music objects is a promising and important research issues in the field of music information retrieval. Usually, music emotion recognition could be considered as a training/classification problem. However, even given a benchmark (a training data with ground truth) and using effective classification algorithms, music emotion recognition remains a challenging problem. Most previous relevant work focuses only on acoustic music content without considering individual difference (i.e., personalization issues). In addition, assessment of emotions is usually self-reported (e.g., emotion tags) which might introduce inaccuracy and inconsistency. Electroencephalography (EEG) is a non-invasive brain-machine interface which allows external machines to sense neurophysiological signals from the brain without surgery. Such unintrusive EEG signals, captured from the central nervous system, have been utilized for exploring emotions. This paper proposes an evidence-based and personalized model for music emotion recognition. In the training phase for model construction and personalized adaption, based on the IADS (the International Affective Digitized Sound system, a set of acoustic emotional stimuli for experimental investigations of emotion and attention), we construct two predictive and generic models \(AN\!N_1\) (“EEG recordings of standardized group vs. emotions”) and \(AN\!N_2\) (“music audio content vs. emotion”). Both models are trained by an artificial neural network. We then collect a subject’s EEG recordings when listening the selected IADS samples, and apply the \(AN\!N_1\) to determine the subject’s emotion vector. With the generic model and the corresponding individual differences, we construct the personalized model H by the projective transformation. In the testing phase, given a music object, the processing steps are: (1) to extract features from the music audio content, (2) to apply \(AN\!N_2\) to calculate the vector in the arousal-valence emotion space, and (3) to apply the transformation matrix H to determine the personalized emotion vector. Moreover, with respect to a moderate music object, we apply a sliding window on the music object to obtain a sequence of personalized emotion vectors, in which those predicted vectors will be fitted and organized as an emotion trail for revealing dynamics in the affective content of music object. Experimental results suggest the proposed approach is effective.     

A survey of music emotion recognition

Music is the language of emotions. In recent years, music emotion recognition has attracted widespread attention in the academic and industrial community since it can be widely used in fields like recommendation systems, automatic music composing, psychotherapy, music visualization, and so on. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. This paper gives a detailed survey of music emotion recognition. Starting with some preliminary knowledge of music emotion recognition, this paper first introduces some commonly used evaluation metrics. Then a three-part research framework is put forward. Based on this three-part research framework, the knowledge and algorithms involved in each part are introduced with detailed analysis, including some commonly used datasets, emotion models, feature extraction, and emotion recognition algorithms. After that, the challenging problems and development trends of music emotion recognition technology are proposed, and finally, the whole paper is summarized.     

语音情感识别研究进展*

首先介绍了语音情感识别系统的组成,重点对情感特征和识别算法的研究现状进行了综述,分析了主要的语音情感特征,阐述了代表性的语音情感识别算法以及混合模型,并对其进行了分析比较。最后,指出了语音情感识别技术的可能发展趋势。     

基于内容的音乐检索研究

对音乐的特征表示进行了详细的阐述,详细介绍了乐曲相似性度量方法,并字符串编码方法对音乐特征进行编码,然后使用蛋白质序列局部比对方法对2239首中国乐曲和3960首西方乐曲的音乐库进行大量的检索实验。获取了大量的实验数据,对实验结果进行分析,并取得了较好的检索性能。最后针对音乐检索过程中的每次比对都相互无关的特点,具有可以很好地进行并行化特性,对算法进行并行化,实现在4台高档微机构成的群集上进行了并行检索实验,其检索时间约为串行检索时间的1／4,有很高的加速比。     

Instrument recognition in polyphonic music based on automatic taxonomies

We propose a new approach to instrument recognition in the context of real music orchestrations ranging from solos to quartets. The strength of our approach is that it does not require prior musical source separation. Thanks to a hierarchical clustering algorithm exploiting robust probabilistic distances, we obtain a taxonomy of musical ensembles which is used to efficiently classify possible combinations of instruments played simultaneously. Moreover, a wide set of acoustic features is studied including some new proposals. In particular, signal to mask ratios are found to be useful features for audio classification. This study focuses on a single music genre (i.e., jazz) but combines a variety of instruments among which are percussion and singing voice. Using a varied database of sound excerpts from commercial recordings, we show that the segmentation of music with respect to the instruments played can be achieved with an average accuracy of 53%.     

An approach of genetic programming for music emotion classification

In this paper, we suggest a new approach of genetic programming for music emotion classification. Our approach is based on Thayer’s arousal-valence plane which is one of representative human emotion models. Thayer’s plane which says human emotions is determined by the psychological arousal and valence. We map music pieces onto the arousal-valence plane, and classify the music emotion in that space. We extract 85 acoustic features from music signals, rank those by the information gain and choose the top k best features in the feature selection process. In order to map music pieces in the feature space onto the arousal-valence space, we apply genetic programming. The genetic programming is designed for finding an optimal formula which maps given music pieces to the arousal-valence space so that music emotions are effectively classified. k-NN and SVM methods which are widely used in classification are used for the classification of music emotions in the arousal-valence space. For verifying our method, we compare with other six existing methods on the same music data set. With this experiment, we confirm the proposed method is superior to others.     

基于生成对抗网络的智能音乐制作综述

如何借助计算机算法进行音乐的自动或半自动化生成工作一直是人工智能领域的一个研究热点。近年来,随着深度学习技术的深入发展,使用基于神经网络并契合乐理先验知识的方法来生成高质量、多样性智能音乐的任务也引起了研究者的重视。其中,引入生成对抗机制以提升生成效果的工作取得了一定成果,同时也具备极大的提升空间。为了更好地推进后续研究工作,对相关领域的现有成果进行全面而系统的梳理、分析、总结具有比较重要的意义。首先对机器作曲的发展过程进行了回顾,对音乐领域常用的GAN相关重要模型进行了简要归纳介绍,对引入了生成对抗训练机制的音乐生成方法进行了重点分析,最后对该领域的现状进行了总结,并进一步展望了未来的发展方向。     

数据库物理自调优研究技术综述

数据库物理自调优是自管理自调优数据库中一项重要的内容,为厘清这一领域的重要研究工作,需要对关系数据库物理自调优的背景、问题界定、经典技术和研究的新问题等方面进行综述。总结了数据库物理自调优的经典技术,并从优化程度、可扩展性、可用性可管理性及测试基准四个角度重点阐述了现今数据库物理自调优的关键技术和作为研究开发热点的一些问题,指出不同的物理自调优工具在技术上的共同基础,分析评价了这些关键技术及其方法的各自特点。最后还总结了该领域近几年出现的新的研究问题,并展望了未来的研究方向。     

End-to-end music emotion variation detection using iteratively reconstructed deep features

Multimedia Tools and Applications - Automatic music emotion recognition (MER) has received increased attention in areas of music information retrieval and user interface development. Music emotion...