Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining Practical Machine Learning Tools and Techniques

The measurement of meaning

How to ... be a reflective practitioner.

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Flow The Psychology Of Optimal Experience

The Internet of Musical Things (IoMusT) is an emerging research field positioned at the intersection of Internet of Things, new interfaces for musical expression, ubiquitous music, human–computer interaction, artificial intelligence, and participatory art. From a computer science perspective, IoMusT refers to the networks of computing devices embedded in physical objects (musical things) dedicated to the production and/or reception of musical content. Musical things, such as smart musical instruments or wearables, are connected by an infrastructure that enables multidirectional communication, both locally and remotely. We present a vision in which the IoMusT enables the connection of digital and physical domains by means of appropriate information and communication technologies, fostering novel musical applications and services. The ecosystems associated with the IoMusT include interoperable devices and services that connect musicians and audiences to support musician–musician, audience–musicians, and audience–audience interactions. In this paper, we first propose a vision for the IoMusT and its motivations. We then discuss five scenarios illustrating how the IoMusT could support: 1) augmented and immersive concert experiences; 2) audience participation; 3) remote rehearsals; 4) music e-learning; and 5) smart studio production. We identify key capabilities missing from today’s systems and discuss the research needed to develop these capabilities across a set of interdisciplinary challenges. These encompass network communication (e.g., ultra-low latency and security), music information research (e.g., artificial intelligence for real-time audio content description and multimodal sensing), music interaction (e.g., distributed performance and music e-learning), as well as legal and responsible innovation aspects to ensure that future IoMusT services are socially desirable and undertaken in the public interest.

Internet of Musical Things: Vision and Challenges

The prominent status of music in human culture and every day life is due in large part to its striking ability to elicit emotions, which may manifest from slight variation in mood to changes in our physical condition and actions. In this paper, we first review state of the art stud- ies on music and emotions from dierent disciplines including psychology, musicology and music information retrieval. Based on these studies, we then propose new insights to enhance automated music emotion recog- nition models.

/pdf/multidisciplinary-perspectives-on-music-emotion-recognition-5gi7av28nk.pdf

Multidisciplinary Perspectives on Music Emotion Recognition: Implications for Content and Context-Based Models

The striking ability of music to elicit emotions assures its prominent status in human culture and every day life. Music is often enjoyed and sought for its ability to induce or convey emotions, which may manifest in anything from a slight variation in mood, to changes in our physical condition and actions. Consequently, research on how we might associate musical pieces with emotions and, more generally, how music brings about an emotional response is attracting ever increasing attention. First, this paper provides a thorough review of studies on the relation of music and emotions from different disciplines. We then propose new insights to enhance automated music emotion recognition models using recent results from psychology, musicology, affective computing, semantic technologies and music information retrieval.

/pdf/music-emotion-recognition-from-content-to-context-based-3fg3vtgd6x.pdf

Music Emotion Recognition: From Content- to Context-Based Models

Most contemporary Western performing arts practices restrict creative interactions from audiences. Open Symphony is designed to explore audience-performer interaction in live music performances, assisted by digital technology. Audiences can conduct improvising performers by voting for various musical "modes." Technological components include a web-based mobile application, a visual client displaying generated symbolic scores, and a server service for the exchange of creative data. The interaction model, app, and visualization were designed through an iterative participatory design process. The system was experienced by about 120 audience and performer participants (35 completed surveys) in controlled (lab) and real-world settings. Feedback on usability and user experience was overall positive, and live interactions demonstrate significant levels of audience creative engagement. The authors identified further design challenges around audience sense of control, learnability, and compositional structure. This article is part of a special issue on multimedia for enriched music.

/pdf/open-symphony-creative-participation-for-audiences-of-live-1p6j4qrye3.pdf

Open Symphony: Creative Participation for Audiences of Live Music Performances

This study deals with the acoustical factors liable to account for expressiveness in clarinet performances. Mechanical and expressive performances of excerpts from Bach9s Suite No. II and Mozart9s Quintet for Clarinet and Strings were recorded. Timbre, timing, dynamics, and pitch descriptors were extracted from the recorded performances. The data were processed using a two-way analysis of variance, where the musician9s expressive intentions and the note factors were defined as the independent variables. In both musical excerpts, a strong effect of the expressive intention was observed on the timbre (attack time, spectral centroid, odd/even ratio), timing (intertone onset intervals) and dynamics (root mean square envelope) descriptors. The changes in the timbre descriptors were found to depend on the position of the notes in the musical phrases. These results suggest that timbre, as well as timing and dynamics variations, may mediate expressiveness in the musical messages transmitted from performers to listeners.

https://www.jstor.org/stable/10.1525/mp.2010.28.2.135

Mathieu Barthet

Papers

Internet of Musical Things: Vision and Challenges

Multidisciplinary Perspectives on Music Emotion Recognition: Implications for Content and Context-Based Models

Music Emotion Recognition: From Content- to Context-Based Models

Open Symphony: Creative Participation for Audiences of Live Music Performances

Acoustical Correlates of Timbre and Expressiveness in Clarinet Performance