A Musical Learning Algorithm

Cope, David

doi:10.1162/0148926041790685

Cited by 16 publications

(14 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Melody [Keller and Morrison 2007;Gillick et al 2010;Herremans and Sörensen 2012] Harmony [Hiller Jr and Isaacson 1957;Steedman 1984;Ebcioglu 1988;Cope 1996;Assayag et al 1999b;Cope 2004;Huang and Chew 2005;Anders 2007;Anders and Miranda 2009;Aguilera et al 2010;Sörensen 2012, 2013;Tanaka et al 2016] Narrative [Rutherford and Wiggins 2002] Difficulty [Lin and Liu 2006] Interaction [Lewis 2000;Chemillier 2001;Morales-Manzanares et al 2001;Marsden 2004] Narrative [Casella and Paiva 2001;Farbood et al 2007;Brown 2012;Nakamura et al 1994]…”

Section: Rule/constraint Satisfaction/grammar-basedmentioning

confidence: 99%

A Functional Taxonomy of Music Generation Systems

2017

View full text Add to dashboard Cite

Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys, and facilitates the identification of grand challenges so as to set the stage for new breakthroughs.

show abstract

Section: Rule/constraint Satisfaction/grammar-basedmentioning

confidence: 99%

A Functional Taxonomy of Music Generation Systems

2017

View full text Add to dashboard Cite

show abstract

“…Indeed, recent technological advances significantly enhanced the way automatic environments compose music [5], expressively perform it [12], accompany human musicians [23], and the way music is sold and bought through web stores [11].…”

Section: Roberto Espositomentioning

confidence: 99%

BREVE: An HMPerceptron-Based Chord Recognition System

Radicioni

Esposito

2010

Advances in Music Information Retrieval

View full text Add to dashboard Cite

Tonal harmony analysis is a sophisticated task. It combines general knowledge with contextual cues, and it is concerned with faceted and evolving objects such as musical language, execution style and taste. We present BREVE, a system for performing a particular kind of harmony analysis, chord recognition: music is encoded as a sequence of sounding events and the system should assing the appropriate chord label to each event. The solution proposed to the problem relies on a conditional model, where domain knowledge is encoded in the form of Boolean features. BREVE exploits the recently proposed algorithm CarpeDiem to obtain significant computational gains in solving the optimization problem underlying the classification process. The implemented system has been validated on a corpus of chorales from J.S. Bach: we report and discuss the learnt weights, point out the committed errors, and elaborate on the correlation between errors and growth in the classification times in places where the music is less clearly asserted.

show abstract

“…In (Cope 2004), David Cope discusses his recent music composition tool, entitled Gradus. It reads data in the form of short musical exercises and derives a small set of rules.…”

Section: Related Workmentioning

confidence: 99%

A Machine-Learning Approach to Autonomous Music Composition

Lichtenwalter¹,

Lichtenwalter²,

Chawla³

2010

Journal of Intelligent Systems

View full text Add to dashboard Cite

Several music composition systems exist that generate blues chord progressions, jazz improvisation, or classical pieces. Such systems often work by applying a set of rules explicitly provided to the system to determine what sequence of output values is appropriate. Others use pattern recognition and generation techniques such as Markov models. These systems often suffer from mediocre performance and limited generality. We propose a system that goes from raw musical data to feature vector representation to classification models. We employ sliding window sequential machine learning techniques to generate classifiers that correspond to a training set of musical data. Our approach has the advantage of greater generality than explicitly specified musical grammar rules and the potential to apply a wide variety of powerful existing non-sequential learning algorithms. We present the design and implementation of the composition system. We demonstrate the efficacy of the method, show and analyze successful samples of its output, and discuss ways in which it might be improved.

show abstract

A Musical Learning Algorithm

Cited by 16 publications

References 3 publications

A Functional Taxonomy of Music Generation Systems

A Functional Taxonomy of Music Generation Systems

BREVE: An HMPerceptron-Based Chord Recognition System

A Machine-Learning Approach to Autonomous Music Composition

Contact Info

Product

Resources

About