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...     

情感计算与理解研究发展概述

情感在感知、决策、逻辑推理和社交等一系列智能活动中起到核心作用,是实现人机交互和机器智能的重要元素。近年来,随着多媒体数据爆发式增长及人工智能的快速发展,情感计算与理解引发了广泛关注。情感计算与理解旨在赋予计算机系统识别、理解、表达和适应人的情感的能力来建立和谐人机环境,并使计算机具有更高、更全面的智能。根据输入信号的不同,情感计算与理解包含不同的研究方向。本文全面回顾了多模态情感识别、孤独症情感识别、情感图像内容分析以及面部表情识别等不同情感计算与理解方向在过去几十年的研究进展并对未来的发展趋势进行展望。对于每个研究方向,首先介绍了研究背景、问题定义和研究意义;其次从不同角度分别介绍了国际和国内研究现状,包括情感数据标注、特征提取、学习算法、部分代表性方法的性能比较和分析以及代表性研究团队等;然后对国内外研究进行了系统比较,分析了国内研究的优势和不足;最后讨论了目前研究存在的问题及未来的发展趋势与展望,例如考虑个体情感表达差异问题和用户隐私问题等。     

Emotion-based music recommendation by affinity discovery from film music

With the growth of digital music, the development of music recommendation is helpful for users to pick desirable music pieces from a huge repository of music. The existing music recommendation approaches are based on a user’s preference on music. However, sometimes, it might better meet users’ requirement to recommend music pieces according to emotions. In this paper, we propose a novel framework for emotion-based music recommendation. The core of the recommendation framework is the construction of the music emotion model by affinity discovery from film music, which plays an important role in conveying emotions in film. We investigate the music feature extraction and propose the Music Affinity Graph and Music Affinity Graph-Plus algorithms for the construction of music emotion model. Experimental result shows the proposed emotion-based music recommendation achieves 85% accuracy in average.     

人脸表情识别综述

人脸表情识别是人类情感识别的基础,是近年来模式识别与人工智能领域研究的热点问题。本文首先总结了人脸表情识别的发展过程,主要包括传统的表情特征提取、表情分类方法与基于深度学习的表情识别方法,并对各种算法的识别率与性能进行了分析与比较。然后介绍了表情识别常用的数据集及各数据集的优势与存在的问题,并针对这些问题归纳分析了生成对抗网络等用于数据增强的技术与方法。最后,总结了表情识别领域目前存在的问题并展望了未来可能的发展。     

音乐信息检索下的乐器识别综述

高效精准的乐器识别技术可以有效地推动声源分离、音乐识谱、音乐流派分类等研究的深入发展,可广泛应用于播放列表生成、声学环境分类、乐器智能教学和交互式多媒体等众多领域。近年来,随着乐器识别研究的不断推进,乐器识别系统在性能上有了大幅提高,但依旧存在着部分乐器难以识别、乐器音频特征提取较为困难、复音乐器识别精准度较低等诸多问题,如何借助人工智能技术对乐器进行高效精准的识别成为当前研究的热点和难点。针对当前研究现状,从乐器识别常用音频特征、乐器识别模型及方法和常用数据集三个方面进行综述,并对当前研究中存在的局限性和未来发展趋势进行总结,为乐器识别研究提供一定的借鉴参考。     

语音情感识别研究综述

语音是人们传递信息内容的同时又表达情感态度的媒介,语音情感识别是人机交互的重要组成部分。由语音情感识别的概念和历史发展进程入手,从6个角度逐步展开对语音情感识别研究体系进行综述。分析常用的情感描述模型,归纳常用的情感语音数据库和不同类型数据库的特点,研究语音情感特征的提取技术。通过比对3种语音情感识别方法的众多学者的多方面研究,得出语音情感识别方法可期望应用场景的态势,展望语音情感识别技术的挑战和发展趋势。     

在线学习环境中基于眼动特征情感识别研究

在e-Learning环境中,学习普遍存在“情感缺失”问题,该问题会导致学习效果和学习体验下降。在学习过程中及时识别学习者的情感状态是解决“情感缺失”的首要问题,情感识别技术在人机交互教学得到了广泛的应用,但仍然存在不少问题和挑战。随着眼动追踪技术的发展,将眼动信号引入情感识别成为研究的热点。针对当前国内外在e-Learning环境中基于眼动特征的相关研究进行综述,对相关研究中采用的眼动特征、机器学习方法以及涉及的学习过程进行分类、归纳及分析,归纳了五类学习过程研究中常用的眼动特征和识别算法。通过对应用在疲劳检测、健康医疗以及人机交互等相关领域中的眼动特征进行分析,对可借鉴至MOOC学习环境下情感识别的眼动特征进行汇总,并为下一步如何采用眼动特征在MOOC环境下进行情感识别研究提出建议。     

融合CNN-BiLSTM和自注意力模型的音乐情感识别

随着音乐科技研究的不断深入,音乐情感识别已被广泛实践和应用在音乐推荐、音乐心理治疗、声光场景构建等方面。模拟人类感受音乐表现情感的过程,针对音乐情感识别中长短时记忆神经网络的长距离依赖和训练效率低的问题,提出一种新的网络模型CBSA（CNN BiLSTM self attention）,应用于长距离音乐情感识别回归训练。模型使用二维卷积神经网络获取音乐情感局部关键特征,采用双向长短时记忆神经网络从获取的局部关键特征中提取序列化音乐情感信息,利用自注意力模型对获取的序列化信息进行动态权重调整,突出音乐情感全局关键点。实验结果表明,CBSA模型可缩短分析音乐情感信息中数据规律的训练时间,有效地提高音乐情感识别精确度。     

语音情感识别综述

人类的语音情感变化是一个抽象的动态过程,难以使用静态信息对其情感交互进行描述,而人工智能的兴起为语音情感识别的发展带来了新的契机。从语音情感识别的概念和在国内外发展的历史进程入手,分别从5个方面对近些年关于语音情感识别的研究成果进行了归纳总结。介绍了语音情感特征,归纳总结了各种语音特征参数对语音情感识别的意义。分别对语音情感数据库的分类及特点、语音情感识别算法的分类及优缺点、语音情感识别的应用以及语音情感识别现阶段所遇到的挑战进行了详细的阐述。立足于研究现状对语音情感识别的未来研究及其发展进行了展望。     

语音情感识别研究综述

针对语音情感识别研究体系进行综述。这一体系包括情感描述模型、情感语音数据库、特征提取与降维、情感分类与回归算法4个方面的内容。本文总结离散情感模型、维度情感模型和两模型间单向映射的情感描述方法；归纳出情感语音数据库选择的依据；细化了语音情感特征分类并列出了常用特征提取工具；最后对特征提取和情感分类与回归的常用算法特点进行凝练并总结深度学习研究进展，并提出情感语音识别领域需要解决的新问题、预测了发展趋势。     

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.     

语音情感识别中特征参数的研究进展

语音情感识别是近年来新兴的研究课题之一,特征参数的提取直接影响到最终的识别效率,特征降维可以提取出最能区分不同情感的特征参数。提出了特征参数在语音情感识别中的重要性,介绍了语音情感识别系统的基本组成,重点对特征参数的研究现状进行了综述,阐述了目前应用于情感识别的特征降维常用方法,并对其进行了分析比较。展望了语音情感识别的可能发展趋势。     

音视频双模态情感识别融合框架研究

针对双模态情感识别框架识别率低、可靠性差的问题,对情感识别最重要的两个模态语音和面部表情进行了双模态情感识别特征层融合的研究。采用基于先验知识的特征提取方法和VGGNet-19网络分别对预处理后的音视频信号进行特征提取,以直接级联的方式并通过PCA进行降维来达到特征融合的目的,使用BLSTM网络进行模型构建以完成情感识别。将该框架应用到AViD-Corpus和SEMAINE数据库上进行测试,并和传统情感识别特征层融合框架以及基于VGGNet-19或BLSTM的框架进行了对比。实验结果表明,情感识别的均方根误差（RMSE）得到降低,皮尔逊相关系数（PCC）得到提高,验证了文中提出方法的有效性。     

Implementing situation-aware and user-adaptive music recommendation service in semantic web and real-time multimedia computing environment

With the advent of the ubiquitous era, many studies have been devoted to various situation-aware services in the semantic web environment. One of the most challenging studies involves implementing a situation-aware personalized music recommendation service which considers the user’s situation and preferences. Situation-aware music recommendation requires multidisciplinary efforts including low-level feature extraction and analysis, music mood classification and human emotion prediction. In this paper, we propose a new scheme for a situation-aware/user-adaptive music recommendation service in the semantic web environment. To do this, we first discuss utilizing knowledge for analyzing and retrieving music contents semantically, and a user adaptive music recommendation scheme based on semantic web technologies that facilitates the development of domain knowledge and a rule set. Based on this discussion, we describe our Context-based Music Recommendation (COMUS) ontology for modeling the user’s musical preferences and contexts, and supporting reasoning about the user’s desired emotions and preferences. Basically, COMUS defines an upper music ontology that captures concepts on the general properties of music such as titles, artists and genres. In addition, it provides functionality for adding domain-specific ontologies, such as music features, moods and situations, in a hierarchical manner, for extensibility. Using this context ontology, we believe that logical reasoning rules can be inferred based on high-level (implicit) knowledge such as situations from low-level (explicit) knowledge. As an innovation, our ontology can express detailed and complicated relations among music clips, moods and situations, which enables users to find appropriate music. We present some of the experiments we performed as a case-study for music recommendation.     

Multimodal mood classification of Hindi and Western songs

Music mood classification is one of the most interesting research areas in music information retrieval, and it has many real-world applications. Many experiments have been performed in mood classification or emotion recognition of Western music; however, research on mood classification of Indian music is still at initial stage due to scarcity of digitalized resources. In the present work, a mood taxonomy is proposed for Hindi and Western songs; both audio and lyrics were annotated using the proposed mood taxonomy. Differences in mood were observed during the annotation of the audio and lyrics for Hindi songs only. The detailed studies on mood classification of Hindi and Western music are presented for the requirement of the recommendation system. LibSVM and Feed-forward neural networks have been used to develop mood classification systems based on audio, lyrics, and a combination of them. The multimodal mood classification systems using Feed-forward neural networks for Hindi and Western songs obtained the maximum F-measures of 0.751 and 0.835, respectively.     

基于音乐情感识别的舞台灯光控制方法研究

为了实现音乐情感识别的舞台灯光自动控制,需对音乐文件进行情感标记。针对人工情感标记效率低、速度慢的问题,开展了基于音乐情感识别的舞台灯光控制方法研究,提出了一种基于支持向量机和粒子群优化的音乐情感特征提取、分类和识别算法。首先以231首MIDI音乐文件为例,对平均音高、平均音强、旋律的方向等7种音乐基本特征进行提取并进行标准化处理;之后组成音乐情感特征向量输入支持向量机（SVM）多分类器,并利用改进的粒子群算法（PSO）优化分类器参数,建立标准音乐分类模型;最后设计灯光动作模型,将新的音乐文件通过离散情感模型与灯光动作相匹配,生成舞台灯光控制方法。实验结果表明了情感识别模型的有效性,与传统SVM多分类模型相比,明显提高了音乐情感的识别率,减少了测试时间,从而为舞台灯光设计人员提供合理参考。     

Bridging the semantic gap in multimedia emotion/mood recognition for ubiquitous computing environment

With the advent of the ubiquitous era, multimedia emotion/mood could be used as an important clue in multimedia understanding, retrieval, recommendation, and some other multimedia applications. Many issues for multimedia emotion recognition have been addressed by different disciplines such as physiology, psychology, cognitive science, and musicology. Recently, many researchers have tried to uncover the relationship between multimedia contents such as image or music and emotion in many applications. In this paper, we introduce the existing emotion models and acoustic features. We also present a comparison of different emotion/mood recognition methods.     

人脸表情识别综述

人脸表情识别作为情感计算的一个研究方向,构成了情感理解的基础,是实现人机交互智能的前提。人脸表情的极度细腻化消耗了大量的计算时间,影响了人机交互的时效性和体验感,所以人脸表情特征提取成为人脸表情识别的重要研究课题。总结了国内外近五年的人脸表情识别的稳固框架和新进展,主要针对人脸表情特征提取和表情分类方法进行了归纳,详细介绍了这两方面的主要算法及改进,并分析比较了各种算法的优势与不足。通过对国内外人脸表情识别应用中实际问题进行研究,给出了人脸表情识别方面仍然存在的挑战及不足。     

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。     

信息融合技术在情绪识别领域的研究展望

简要介绍目前几种基于不同数据源的情绪识别方法和信息融合技术基础, 为工程技术人员提供一定的理论背景。对多源信息融合领域的情绪识别现状进行了分类介绍, 说明和分析了基于多源信息融合的情感识别存在的问题, 简述了其在情绪识别领域的应用前景。