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2019 International Workshop on Multilayer Music Representation and Processing (MMRP)最新文献

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Three-Dimensional Mapping of High-Level Music Features for Music Browsing 用于音乐浏览的高级音乐特征的三维映射
Stefano Cherubin, Clara Borrelli, M. Zanoni, Michele Buccoli, A. Sarti, S. Tubaro
The increased availability of musical content comes with the need of novel paradigms for recommendation, browsing and retrieval from large music libraries. Most music players and streaming services propose a paradigm based on content listing of meta-data information, which provides little insight on the music content. In services with huge catalogs of songs, a more informative paradigm is needed. In this work we propose a framework for music browsing based on the navigation into a three-dimensional (3-D) space, where musical items are placed as a 3-D mapping of their high-level semantic descriptors. We conducted a survey to guide the design of the framework and the implementation choices. We rely on state-of-the-art techniques from Music Information Retrieval to automatically extract the high-level descriptors from a low-level representation of the musical signal. The framework is validated by means of a subjective evaluation from 33 users, who give positive feedbacks and highlight promising future developments especially in virtual reality field.
音乐内容的可用性的增加带来了对大型音乐库的推荐、浏览和检索的新范例的需求。大多数音乐播放器和流媒体服务都提出了一种基于元数据信息的内容列表的范例,这对音乐内容提供了很少的洞察力。在拥有大量歌曲目录的服务中,需要一种更具信息性的模式。在这项工作中,我们提出了一个基于导航到三维(3-D)空间的音乐浏览框架,其中音乐项目被放置为其高级语义描述符的3-D映射。我们进行了一项调查,以指导框架的设计和实现选择。我们依靠音乐信息检索的最新技术,从音乐信号的低级表示中自动提取高级描述符。该框架通过33名用户的主观评价来验证,他们给出了积极的反馈,并强调了未来的发展前景,特别是在虚拟现实领域。
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引用次数: 3
Heretic: Modeling Anthony Braxton's Language Music 异端:模仿安东尼·布拉克斯顿的语言音乐
Hunter M. Brown, M. Casey
This article presents a new system for real-time machine listening within human-machine free improvisation. Heretic uses Anthony Braxton's Language Music system as a grammatical model for contextualizing real-time audio feature data within free improvisation. Heretic hears, recognizes, and organizes unseen musical material from a human improviser into a fluid, coherent, and expressive musical language. Systems similar to Heretic often prioritize agnostic approaches to machine listening by avoiding prior musical knowledge in the system's training stage. However, prominent improvisers such as Cecil Taylor, Ornette Coleman, Joe Morris, and Anthony Braxton detail their approaches to improvisation as languages or grammatical systems. These improvisers contextualize the real-time musical materials of their band-mates by applying their formulated grammatical systems to their decision-making processes. Taylor, Coleman, Morris, and Braxton's autonomy and musical creativity are not compromised by using grammatical systems. In regards to human-machine improvisation, Heretic demonstrates that a grammatical approach to machine listening can yield idiosyncratic interactions, full machine autonomy, and novel musical output. This article details a re-imagining of Anthony Braxton's Language Music within the context of machine listening, and an implementation of Language Music within Heretic via SuperCollider's audio feature extraction functionality and Wekinator's multi-layer perceptron neural networks.
本文提出了一种人机自由即兴的实时机器监听系统。hertic使用Anthony Braxton的语言音乐系统作为语法模型,将实时音频特征数据置于自由即兴创作的语境中。异端者从人类即兴演奏者那里听到、识别并组织看不见的音乐材料,使之成为一种流畅、连贯和富有表现力的音乐语言。类似于Heretic的系统通常会通过在系统的训练阶段避免先验的音乐知识来优先考虑机器聆听的不可知论方法。然而,像塞西尔·泰勒、奥奈特·科尔曼、乔·莫里斯和安东尼·布拉克斯顿这样杰出的即兴演奏者将他们的即兴演奏方法详细描述为语言或语法系统。这些即兴演奏者将他们乐队成员的实时音乐材料语境化,将他们制定的语法系统应用于他们的决策过程。泰勒、科尔曼、莫里斯和布拉克斯顿的自主性和音乐创造力并没有因为使用语法系统而受到损害。在人机即兴演奏方面,异端证明了机器聆听的语法方法可以产生特殊的交互,完全的机器自主性和新颖的音乐输出。本文详细介绍了Anthony Braxton的语言音乐在机器聆听环境中的重新想象,以及通过SuperCollider的音频特征提取功能和Wekinator的多层感知器神经网络在Heretic中实现的语言音乐。
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引用次数: 2
2019 International Workshop on Multilayer Music Representation and Processing (MMRP) MMRP 2019 2019多层音乐表示与处理(MMRP)国际研讨会
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引用次数: 0
Multilayer Music Representation and Processing: Key Advances and Emerging Trends 多层音乐表示和处理:关键进展和新兴趋势
F. Avanzini, L. A. Ludovico
This work represents the introduction to the proceedings of the IstInternational Workshop on Multilayer Music Representation and Processing (MMRP19) authored by the Program Co-Chairs. The idea is to explain the rationale behind such a scientific initiative, describe the methodological approach used in paper selection, and provide a short overview of the workshop's accepted works, trying to highlight the thread that runs through different contributions and approaches.
这项工作代表了由项目联合主席撰写的第1届多层音乐表示和处理国际研讨会(MMRP19)会议记录的介绍。这个想法是解释这种科学倡议背后的基本原理,描述论文选择中使用的方法方法,并提供研讨会接受作品的简短概述,试图突出贯穿不同贡献和方法的线索。
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引用次数: 1
Multitask Learning for Polyphonic Piano Transcription, a Case Study 钢琴复调转录的多任务学习:个案研究
Rainer Kelz, Sebastian Böck, G. Widmer
Viewing polyphonic piano transcription as a multitask learning problem, where we need to simultaneously predict onsets, intermediate frames and offsets of notes, we investigate the performance impact of additional prediction targets, using a variety of suitable convolutional neural network architectures. We quantify performance differences of additional objectives on the larGe MAESTRO dataset.
将复调钢琴转录视为一个多任务学习问题,我们需要同时预测音符的起始、中间帧和偏移,我们使用各种合适的卷积神经网络架构研究了额外的预测目标对性能的影响。我们量化了大型MAESTRO数据集上其他目标的性能差异。
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引用次数: 7
2019 International Workshop on Multilayer Music Representation and Processing MMRP 2019 2019多层音乐表示与处理国际研讨会
A. Baratè, L. A. Ludovico, S. Ntalampiras, G. Presti
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引用次数: 1
Message from the General Chair MMRP 2019 2019年MMRP主席致辞
Mmrp
This workshop has two main goals: first, bringing together the scientific community for an up-to-date discussion about the multilayer music representation topic; secondly, hosting the kickoff meeting of the Working Group for the IEEE1599 standard revision. The latter point implies a number of activities, such as forming and introducing the team, understanding the project background, identifying the main goals to pursue, and agreeing on how to work together effectively.
本次研讨会有两个主要目标:第一,将科学界聚集在一起,就多层音乐表现主题进行最新的讨论;二是主办IEEE1599标准修订工作组启动会议。后一点暗示了许多活动,比如组建和介绍团队,了解项目背景,确定要追求的主要目标,以及就如何有效地协同工作达成一致。
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引用次数: 0
Multimodal Music Information Processing and Retrieval: Survey and Future Challenges 多模态音乐信息处理与检索:调查与未来挑战
Federico Simonetta, S. Ntalampiras, F. Avanzini
Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval, and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years.
为了提高在各种音乐信息处理任务中的表现,最近的研究利用了不同的模式来捕捉音乐的不同方面。这些形式包括录音、象征性乐谱、中级表现、动作和手势数据、录像、编辑或文化标签、歌词和专辑封面艺术。本文批判性地回顾了音乐信息处理和检索中采用的各种方法,并强调了多模态算法如何帮助音乐计算应用。首先,我们根据它们所涉及的应用对相关文献进行分类。随后,我们分析了现有的信息融合方法,并总结了音乐信息检索和声音与音乐计算研究团体在未来几年应该关注的一系列挑战。
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引用次数: 42
The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation 深层神经网络显式结构编码对符号音乐生成的影响
K. Chen, Weilin Zhang, S. Dubnov, Gus G. Xia, Wei Li
With recent breakthroughs in artificial neural networks, deep generative models have become one of the leading techniques for computational creativity. Despite very promising progress on image and short sequence generation, symbolic music generation remains a challenging problem since the structure of compositions are usually complicated. In this study, we attempt to solve the melody generation problem constrained by the given chord progression. In particular, we explore the effect of explicit architectural encoding of musical structure via comparing two sequential generative models: LSTM (a type of RNN) and WaveNet (dilated temporal-CNN). As far as we know, this is the first study of applying WaveNet to symbolic music generation, as well as the first systematic comparison between temporal-CNN and RNN for music generation. We conduct a survey for evaluation in our generations and implemented Variable Markov Oracle in music pattern discovery. Experimental results show that to encode structure more explicitly using a stack of dilated convolution layers improved the performance significantly, and a global encoding of underlying chord progression into the generation procedure gains even more.
随着近年来人工神经网络的突破,深度生成模型已经成为计算创造力的主要技术之一。尽管在图像和短序列生成方面取得了很好的进展,但由于作品结构复杂,符号音乐生成仍然是一个具有挑战性的问题。在本研究中,我们试图解决受给定和弦进行约束的旋律生成问题。特别地,我们通过比较两种顺序生成模型:LSTM(一种RNN)和WaveNet(扩展时间- cnn)来探索音乐结构的显式结构编码的效果。据我们所知,这是第一次将WaveNet应用于符号音乐生成的研究,也是第一次系统的比较了time - cnn和RNN在音乐生成中的应用。我们进行了一项调查,以评估我们这代人,并在音乐模式发现中实现了变量马尔可夫甲骨文。实验结果表明,使用扩展卷积层堆栈对结构进行更明确的编码可以显著提高性能,并且在生成过程中对底层和弦进行全局编码可以获得更大的性能。
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引用次数: 28
Automated Analysis of Postural and Movement Qualities of Violin Players 小提琴手姿势和动作素质的自动分析
Erica Volta, G. Volpe
Learning to playa music instrument is a complex task, requiring continuous practice and the development of sophisticated motor control techniques. The traditional model of music learning is based on a master-apprentice relationship, leading often to a solitary learning process, in which the time spent with the teacher is usually limited to weekly lessons and a long period of self-study is needed. Moreover, a large amount of time passes from the teacher's feedback and the student's proprioceptive perception while studying, requiring a big effort in developing an efficient and healthy technique. In this paper, we present our recent developments concerning an assistive and adaptive technology to help violin students overcoming all these difficulties, and developing their technique and repertoire properly and sefely. In particular, we focus on the multimodal corpus of violin performances which was collected for the purpose, and on the analysis of such data to compute postural and gestural features characterizing the performance under a biomechanical perspective and in terms of movement quality. Analysis is expected to provide students with feedback for reaching a physically accurate performance, maximizing efficiency and minimizing injuries.
学习演奏乐器是一项复杂的任务,需要不断的练习和复杂的运动控制技术的发展。传统的音乐学习模式是建立在师徒关系的基础上的,这往往导致一个单独的学习过程,在这个过程中,花在老师身上的时间通常仅限于每周一次的课程,需要很长时间的自学。此外,在学习过程中,老师的反馈和学生的本体感受会耗费大量的时间,需要付出很大的努力来开发一种高效健康的技术。在本文中,我们介绍了一种辅助和自适应技术的最新进展,以帮助小提琴学生克服这些困难,并正确和自主地发展他们的技术和曲目。特别地,我们专注于收集小提琴演奏的多模态语料库,并对这些数据进行分析,以计算生物力学角度和运动质量下演奏的姿势和手势特征。分析旨在为学生提供反馈,以达到准确的身体表现,最大限度地提高效率和减少伤害。
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引用次数: 5
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2019 International Workshop on Multilayer Music Representation and Processing (MMRP)
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