Some music-domain visual programming languages (VPLs) have been shown to be Turing complete. The common lack of built-in flow control structures can obstruct using VPLs to implement general-purpose algorithms, however, which harms the direct use of algorithms and algorithm theory in art-creation processes using VPLs. In this article, we show how to systematically implement general-purpose algorithms in music-domain visual languages by using the computation model known as a finite state machine with data path. The results expose a finite state machine and a set of internal state variables that traverse paths whose speed can be controlled using metronome ticks and whose path depends on the initial conditions of the algorithm. These elements can be further mapped to music elements according to the musician's intentions. We demonstrate this technique by implementing Euclid's greatest common divisor algorithm and using it to control high-level music elements in an implementation of Terry Riley's In C, and to control audio synthesis parameters in a frequency-modulator synthesizer.
{"title":"Finite State Machines with Data Paths in Visual Languages for Music","authors":"Tiago Fernandes Tavares, José Eduardo Fornari","doi":"10.1162/comj_a_00688","DOIUrl":"https://doi.org/10.1162/comj_a_00688","url":null,"abstract":"Some music-domain visual programming languages (VPLs) have been shown to be Turing complete. The common lack of built-in flow control structures can obstruct using VPLs to implement general-purpose algorithms, however, which harms the direct use of algorithms and algorithm theory in art-creation processes using VPLs. In this article, we show how to systematically implement general-purpose algorithms in music-domain visual languages by using the computation model known as a finite state machine with data path. The results expose a finite state machine and a set of internal state variables that traverse paths whose speed can be controlled using metronome ticks and whose path depends on the initial conditions of the algorithm. These elements can be further mapped to music elements according to the musician's intentions. We demonstrate this technique by implementing Euclid's greatest common divisor algorithm and using it to control high-level music elements in an implementation of Terry Riley's In C, and to control audio synthesis parameters in a frequency-modulator synthesizer.","PeriodicalId":50639,"journal":{"name":"Computer Music Journal","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Esteban Gutiérrez, Christopher Haworth, Rodrigo F. Cádiz
Quadratic difference tones belong to a family of perceptual phenomena that arise from the neuromechanics of the auditory system in response to particular physical properties of sound. Long deployed as “ghost” or “phantom” tones by sound artists, improvisers, and computer musicians, in this article we address an entirely new topic: How to create a quadratic difference tone spectrum (QDTS) in which a target fundamental and harmonic overtone series are specified and in which the complex tone necessary to evoke it is synthesized. We propose a numerical algorithm that solves the problem of how to synthesize a QDTS for a target distribution of amplitudes. The algorithm aims to find a solution that matches the desired spectrum as closely as possible for an arbitrary number of target harmonics. Results from experiments using different parameter settings and target distributions show that the algorithm is effective in the majority of cases, with at least 99% of the cases being solvable in real time. An external object for the visual programming language Max is described. We discuss musical and perceptual considerations for using the external, and we describe a range of audio examples that demonstrate the synthesis of QDTSs across different cases. As we show, the method makes possible the matching of QDTSs to particular instrumental timbres with surprising efficiency. Also included is a discussion of a musical work by composer Marcin Pietruszewski that makes use of QDTS synthesis.
四次方差音属于感知现象的一种，它是听觉系统的神经力学对声音的特定物理特性做出反应时产生的。长期以来，声音艺术家、即兴演奏家和计算机音乐家一直将其作为 "幽灵 "或 "幻影 "音调使用，而在本文中，我们将讨论一个全新的话题：如何创建二次差分音谱（QDTS），在其中指定目标基音和谐波泛音系列，并合成唤起它所需的复合音调。我们提出了一种数值算法，用于解决如何为目标振幅分布合成 QDTS 的问题。该算法旨在为任意数量的目标谐波找到与所需频谱尽可能匹配的解决方案。使用不同参数设置和目标分布进行的实验结果表明，该算法在大多数情况下都很有效，至少 99% 的情况可以实时求解。我们还介绍了视觉编程语言 Max 的外部对象。我们讨论了使用该外部对象在音乐和感知方面的考虑因素，并描述了一系列音频示例，展示了不同情况下 QDTS 的合成。正如我们所展示的，该方法能以惊人的效率将 QDTS 与特定的乐器音色相匹配。此外，我们还讨论了作曲家 Marcin Pietruszewski 利用 QDTS 合成技术创作的音乐作品。
{"title":"Generating Sonic Phantoms with Quadratic Difference Tone Spectrum Synthesis","authors":"Esteban Gutiérrez, Christopher Haworth, Rodrigo F. Cádiz","doi":"10.1162/comj_a_00687","DOIUrl":"https://doi.org/10.1162/comj_a_00687","url":null,"abstract":"Quadratic difference tones belong to a family of perceptual phenomena that arise from the neuromechanics of the auditory system in response to particular physical properties of sound. Long deployed as “ghost” or “phantom” tones by sound artists, improvisers, and computer musicians, in this article we address an entirely new topic: How to create a quadratic difference tone spectrum (QDTS) in which a target fundamental and harmonic overtone series are specified and in which the complex tone necessary to evoke it is synthesized. We propose a numerical algorithm that solves the problem of how to synthesize a QDTS for a target distribution of amplitudes. The algorithm aims to find a solution that matches the desired spectrum as closely as possible for an arbitrary number of target harmonics. Results from experiments using different parameter settings and target distributions show that the algorithm is effective in the majority of cases, with at least 99% of the cases being solvable in real time. An external object for the visual programming language Max is described. We discuss musical and perceptual considerations for using the external, and we describe a range of audio examples that demonstrate the synthesis of QDTSs across different cases. As we show, the method makes possible the matching of QDTSs to particular instrumental timbres with surprising efficiency. Also included is a discussion of a musical work by composer Marcin Pietruszewski that makes use of QDTS synthesis.","PeriodicalId":50639,"journal":{"name":"Computer Music Journal","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aaron Einbond, Thibaut Carpentier, Diemo Schwarz, Jean Bresson
The situated spatial presence of musical instruments has been well studied in the fields of acoustics and music perception research, but so far it has not been the focus of human-AI interaction. We respond critically to this trend by seeking to reembody interactive electronics using data derived from natural acoustic phenomena. Two musical works, composed for human soloist and computer-generated live electronics, are intended to situate the listener in an immersive sonic environment in which real and virtual sources blend seamlessly. To do so, we experimented with two contrasting reproduction setups: a surrounding Ambisonic loudspeaker dome and a compact spherical loudspeaker array for radiation synthesis. A large database of measured radiation patterns of orchestral instruments served as a training set for machine learning models to control spatially rich 3-D patterns for electronic sounds. These are exploited during performance in response to live sounds captured with a spherical microphone array and used to train computer models of improvisation and to trigger corpus-based spatial synthesis. We show how AI techniques are useful to utilize complex, multidimensional, spatial data in the context of computer-assisted composition and human-computer interactive improvisation.
{"title":"Embodying Spatial Sound Synthesis with AI in Two Compositions for Instruments and 3-D Electronics","authors":"Aaron Einbond, Thibaut Carpentier, Diemo Schwarz, Jean Bresson","doi":"10.1162/comj_a_00664","DOIUrl":"https://doi.org/10.1162/comj_a_00664","url":null,"abstract":"The situated spatial presence of musical instruments has been well studied in the fields of acoustics and music perception research, but so far it has not been the focus of human-AI interaction. We respond critically to this trend by seeking to reembody interactive electronics using data derived from natural acoustic phenomena. Two musical works, composed for human soloist and computer-generated live electronics, are intended to situate the listener in an immersive sonic environment in which real and virtual sources blend seamlessly. To do so, we experimented with two contrasting reproduction setups: a surrounding Ambisonic loudspeaker dome and a compact spherical loudspeaker array for radiation synthesis. A large database of measured radiation patterns of orchestral instruments served as a training set for machine learning models to control spatially rich 3-D patterns for electronic sounds. These are exploited during performance in response to live sounds captured with a spherical microphone array and used to train computer models of improvisation and to trigger corpus-based spatial synthesis. We show how AI techniques are useful to utilize complex, multidimensional, spatial data in the context of computer-assisted composition and human-computer interactive improvisation.","PeriodicalId":50639,"journal":{"name":"Computer Music Journal","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139496833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gérard Assayag, Laurent Bonnasse-Gahot, Joakim Borg
Somax2 is an AI-based multiagent system for human-machine coimprovisation that generates stylistically coherent streams while continuously listening and adapting to musicians or other agents. The model on which it is based can be used with little configuration to interact with humans in full autonomy, but it also allows fine real-time control of its generative processes and interaction strategies, closer in this case to a “smart” digital instrument. An offspring of the Omax system, conceived at the Institut de Recherche et Coordination Acoustique/Musique, the Somax2 environment is part of the European Research Council Raising Cocreativity in Cyber-Human Musicianship (REACH) project, which studies distributed creativity as a general template for symbiotic interaction between humans and digital systems. It fosters mixed musical reality involving cocreative AI agents. The REACH project puts forward the idea that cocreativity in cyber-human systems results from the emergence of complex joint behavior, produced by interaction and featuring cross-learning mechanisms. Somax2 is a first step toward this ideal, and already shows life-size achievements. This article describes Somax2 extensively, from its theoretical model to its system architecture, through its listening and learning strategies, representation spaces, and interaction policies.
{"title":"Cocreative Interaction: Somax2 and the REACH Project","authors":"Gérard Assayag, Laurent Bonnasse-Gahot, Joakim Borg","doi":"10.1162/comj_a_00662","DOIUrl":"https://doi.org/10.1162/comj_a_00662","url":null,"abstract":"Somax2 is an AI-based multiagent system for human-machine coimprovisation that generates stylistically coherent streams while continuously listening and adapting to musicians or other agents. The model on which it is based can be used with little configuration to interact with humans in full autonomy, but it also allows fine real-time control of its generative processes and interaction strategies, closer in this case to a “smart” digital instrument. An offspring of the Omax system, conceived at the Institut de Recherche et Coordination Acoustique/Musique, the Somax2 environment is part of the European Research Council Raising Cocreativity in Cyber-Human Musicianship (REACH) project, which studies distributed creativity as a general template for symbiotic interaction between humans and digital systems. It fosters mixed musical reality involving cocreative AI agents. The REACH project puts forward the idea that cocreativity in cyber-human systems results from the emergence of complex joint behavior, produced by interaction and featuring cross-learning mechanisms. Somax2 is a first step toward this ideal, and already shows life-size achievements. This article describes Somax2 extensively, from its theoretical model to its system architecture, through its listening and learning strategies, representation spaces, and interaction policies.","PeriodicalId":50639,"journal":{"name":"Computer Music Journal","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Machine learning (ML) deals with algorithms able to learn from data, with the primary aim of finding optimum solutions to perform tasks autonomously. In recent years there has been development in integrating ML algorithms with live coding practices, raising questions about what to optimize or automate, the agency of the algorithms, and in which parts of the ML processes one might intervene midperformance. Live coding performance practices typically involve conversational interaction with algorithmic processes in real time. In analyzing systems integrating live coding and ML, we consider the musical and performative implications of the “moment of intervention” in the ML model and workflow, and the channels for real-time intervention. We propose a framework for analysis, through which we reflect on the domain-specific algorithms and practices being developed that combine these two practices.
机器学习（ML）涉及能够从数据中学习的算法，其主要目的是找到最佳解决方案来自主执行任务。近年来，将 ML 算法与实时编码实践结合在一起的做法得到了发展，但也提出了一些问题，如哪些内容需要优化或自动化、算法的作用以及在执行过程中可以干预 ML 流程的哪些部分。实时编码性能实践通常涉及与算法过程的实时对话交互。在分析集成了现场编码和 ML 的系统时，我们会考虑 ML 模型和工作流程中 "干预时刻 "的音乐和表演含义，以及实时干预的渠道。我们提出了一个分析框架，通过该框架，我们思考了结合这两种实践而开发的特定领域算法和实践。
{"title":"Live Coding Machine Learning: Finding the Moments of Intervention in Autonomous Processes","authors":"Iván Paz, Shelly Knottsy","doi":"10.1162/comj_a_00663","DOIUrl":"https://doi.org/10.1162/comj_a_00663","url":null,"abstract":"Machine learning (ML) deals with algorithms able to learn from data, with the primary aim of finding optimum solutions to perform tasks autonomously. In recent years there has been development in integrating ML algorithms with live coding practices, raising questions about what to optimize or automate, the agency of the algorithms, and in which parts of the ML processes one might intervene midperformance. Live coding performance practices typically involve conversational interaction with algorithmic processes in real time. In analyzing systems integrating live coding and ML, we consider the musical and performative implications of the “moment of intervention” in the ML model and workflow, and the channels for real-time intervention. We propose a framework for analysis, through which we reflect on the domain-specific algorithms and practices being developed that combine these two practices.","PeriodicalId":50639,"journal":{"name":"Computer Music Journal","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Çağrı Erdem, Benedikte Wallace, Kyrre Glette, Alexander Refsum Jensenius
Abstract In this article we introduce the coadaptive audiovisual instrument CAVI. This instrument uses deep learning to generate control signals based on muscle and motion data of a performer's actions. The generated signals control time-based live sound-processing modules. How does a performer perceive such an instrument? Does it feel like a machine learning-based musical tool? Or is it an actor with the potential to become a musical partner? We report on an evaluation of CAVI after it had been used in two public performances. The evaluation is based on interviews with the performers, audience questionnaires, and the creator's self-analysis. Our findings suggest that the perception of CAVI as a tool or actor correlates with the performer's sense of agency. The perceived agency changes throughout a performance based on several factors, including perceived musical coordination, the balance between surprise and familiarity, a “common sense,” and the physical characteristics of the performance setting.
{"title":"Tool or Actor? Expert Improvisers' Evaluation of a Musical AI “Toddler”","authors":"Çağrı Erdem, Benedikte Wallace, Kyrre Glette, Alexander Refsum Jensenius","doi":"10.1162/comj_a_00657","DOIUrl":"https://doi.org/10.1162/comj_a_00657","url":null,"abstract":"Abstract In this article we introduce the coadaptive audiovisual instrument CAVI. This instrument uses deep learning to generate control signals based on muscle and motion data of a performer's actions. The generated signals control time-based live sound-processing modules. How does a performer perceive such an instrument? Does it feel like a machine learning-based musical tool? Or is it an actor with the potential to become a musical partner? We report on an evaluation of CAVI after it had been used in two public performances. The evaluation is based on interviews with the performers, audience questionnaires, and the creator's self-analysis. Our findings suggest that the perception of CAVI as a tool or actor correlates with the performer's sense of agency. The perceived agency changes throughout a performance based on several factors, including perceived musical coordination, the balance between surprise and familiarity, a “common sense,” and the physical characteristics of the performance setting.","PeriodicalId":50639,"journal":{"name":"Computer Music Journal","volume":"57 25","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135091944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In this article we address the role of machine learning (ML) in the composition of two new musical works for acoustic instruments and electronics through autoethnographic reflection on the experience. Our study poses the key question of how ML shapes, and is in turn shaped by, the aesthetic commitments characterizing distinctive compositional practices. Further, we ask how artistic research in these practices can be informed by critical themes from humanities scholarship on material engagement and critical data studies. Through these frameworks, we consider in what ways the interaction with ML algorithms as part of the compositional process differs from that with other music technology tools. Rather than focus on narrowly conceived ML algorithms, we take into account the heterogeneous assemblage brought into play: from composers, performers, and listeners to loudspeakers, microphones, and audio descriptors. Our analysis focuses on a deconstructive critique of data as being contingent on the decisions and material conditions involved in the data creation process. It also explores how interaction among the human and nonhuman collaborators in the ML assemblage has significant similarities to—as well as differences from—existing models of material engagement. Tracking the creative process of composing these works, we uncover the aesthetic implications of the many nonlinear collaborative decisions involved in composing the assemblage.
{"title":"Composing the Assemblage: Probing Aesthetic and Technical Dimensions of Artistic Creation with Machine Learning","authors":"Artemi-Maria Gioti, Aaron Einbond, Georgina Born","doi":"10.1162/comj_a_00658","DOIUrl":"https://doi.org/10.1162/comj_a_00658","url":null,"abstract":"Abstract In this article we address the role of machine learning (ML) in the composition of two new musical works for acoustic instruments and electronics through autoethnographic reflection on the experience. Our study poses the key question of how ML shapes, and is in turn shaped by, the aesthetic commitments characterizing distinctive compositional practices. Further, we ask how artistic research in these practices can be informed by critical themes from humanities scholarship on material engagement and critical data studies. Through these frameworks, we consider in what ways the interaction with ML algorithms as part of the compositional process differs from that with other music technology tools. Rather than focus on narrowly conceived ML algorithms, we take into account the heterogeneous assemblage brought into play: from composers, performers, and listeners to loudspeakers, microphones, and audio descriptors. Our analysis focuses on a deconstructive critique of data as being contingent on the decisions and material conditions involved in the data creation process. It also explores how interaction among the human and nonhuman collaborators in the ML assemblage has significant similarities to—as well as differences from—existing models of material engagement. Tracking the creative process of composing these works, we uncover the aesthetic implications of the many nonlinear collaborative decisions involved in composing the assemblage.","PeriodicalId":50639,"journal":{"name":"Computer Music Journal","volume":"57 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135091945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. McCall, Jason Freeman, Tom McKlin, Taneisha Lee, Michael Horn, Brian Magerko
Many introductory computer science educational platforms foster student interest and facilitate student learning through the authentic incorporation of music. Although many such platforms have demonstrated promising outcomes in student engagement across diverse student populations and learning contexts, little is known about the specific ways in which music and computer science learning are uniquely combined to support student knowledge in both domains. This study looks at two different learning platforms for computer science and music (CS-plus-music), TunePad and EarSketch, which were used by middle school students during a week-long virtual summer camp. Using both platforms, students created computational music projects, which we analyzed for characteristics of music and code complexity across multiple dimensions. Students also completed surveys before and after the workshop about their perceptions of the platforms and their own backgrounds, and we interviewed some students. The results suggest that different connections between music and computing concepts emerge, as well as different progressions through the concepts themselves, depending in part on the design affordances of the application programming interface for computer music in each platform. Coupled with prior findings about the different roles each platform may play in developing situational interest for students, these findings suggest that different CS-plus-musiclearning platforms can provide complementary roles that benefit and support learning and development of student interest.
{"title":"Complementary Roles of Note-Oriented and Mixing-Oriented Software in Student Learning of Computer Science plus Music","authors":"L. McCall, Jason Freeman, Tom McKlin, Taneisha Lee, Michael Horn, Brian Magerko","doi":"10.1162/comj_a_00651","DOIUrl":"https://doi.org/10.1162/comj_a_00651","url":null,"abstract":"\u0000 Many introductory computer science educational platforms foster student interest and facilitate student learning through the authentic incorporation of music. Although many such platforms have demonstrated promising outcomes in student engagement across diverse student populations and learning contexts, little is known about the specific ways in which music and computer science learning are uniquely combined to support student knowledge in both domains. This study looks at two different learning platforms for computer science and music (CS-plus-music), TunePad and EarSketch, which were used by middle school students during a week-long virtual summer camp. Using both platforms, students created computational music projects, which we analyzed for characteristics of music and code complexity across multiple dimensions. Students also completed surveys before and after the workshop about their perceptions of the platforms and their own backgrounds, and we interviewed some students. The results suggest that different connections between music and computing concepts emerge, as well as different progressions through the concepts themselves, depending in part on the design affordances of the application programming interface for computer music in each platform. Coupled with prior findings about the different roles each platform may play in developing situational interest for students, these findings suggest that different CS-plus-musiclearning platforms can provide complementary roles that benefit and support learning and development of student interest.","PeriodicalId":50639,"journal":{"name":"Computer Music Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44663145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The automatic identification of cue points is a central task in applications as diverse as music thumbnailing, generation of mash ups, and DJ mixing. Our focus lies in electronic dance music and in a specific kind of cue point, the “switch point,” that makes it possible to automatically construct transitions between tracks, mimicking what professional DJs do. We present two approaches for the detection of switch points. One embodies a few general rules we established from interviews with professional DJs, the other models a manually annotated dataset that we curated. Both approaches are based on feature extraction and novelty analysis. From an evaluation conducted on previously unknown tracks, we found that about 90% of the points generated can be reliably used in the context of a DJ mix.
{"title":"Automatic Detection of Cue Points for the Emulation of DJ Mixing","authors":"Mickaël Zehren, Marco Alunno, P. Bientinesi","doi":"10.1162/comj_a_00652","DOIUrl":"https://doi.org/10.1162/comj_a_00652","url":null,"abstract":"\u0000 The automatic identification of cue points is a central task in applications as diverse as music thumbnailing, generation of mash ups, and DJ mixing. Our focus lies in electronic dance music and in a specific kind of cue point, the “switch point,” that makes it possible to automatically construct transitions between tracks, mimicking what professional DJs do. We present two approaches for the detection of switch points. One embodies a few general rules we established from interviews with professional DJs, the other models a manually annotated dataset that we curated. Both approaches are based on feature extraction and novelty analysis. From an evaluation conducted on previously unknown tracks, we found that about 90% of the points generated can be reliably used in the context of a DJ mix.","PeriodicalId":50639,"journal":{"name":"Computer Music Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45202210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.4135/9781412972024.n2140
R. Feller
James Dashow’s second volume of Soundings in Pure Duration features works for electronic sounds, several which are composed for instrumental or vocal soloists. The composer is well known in the electronic and computer music worlds and has produced a large amount of work over many decades. This release contains the last four works in the Soundings series, composed between 2014 and 2020, as well as the rerelease of “. . . At Other Times, the Distances,” an older, quadraphonic composition. This DVD contains stereo mix downs and full 5.0-surround mixes for each of the five compositions. The stereo versions were all spatially enhanced to suggest a wider-than-normal audio field. Dashow is perhaps best known for his work with spatialization. According to the liner notes,
詹姆斯-达肖（James Dashow）的第二卷《纯时长的声音》（Soundings in Pure Duration）收录了电子声音作品，其中有几首是为器乐或声乐独奏而作。这位作曲家在电子和计算机音乐界享有盛誉，几十年来创作了大量作品。此次发行的作品包括 "Soundings "系列中创作于 2014 年至 2020 年的最后四部作品，以及重新发行的"......"。在其他时间，距离"，这是一首较早的四声部作品。这张 DVD 包含五首作品的立体声混音和完整的 5.0 环绕声混音。所有立体声版本都经过空间增强处理，以显示比正常音场更宽广的音场。Dashow 最著名的作品可能是空间化。根据内页说明
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