scholarly journals Anticipation-RNN: enforcing unary constraints in sequence generation, with application to interactive music generation

2018 ◽  
Vol 32 (4) ◽  
pp. 995-1005 ◽  
Author(s):  
Gaëtan Hadjeres ◽  
Frank Nielsen
Keyword(s):  
Interactive Music ◽  
Sequence Generation ◽  
Music Generation

The Serial Collaborator: A Meta-Pianist for Real-Time Tonal and Non-Tonal Music Generation

Leonardo ◽  
2014 ◽  
Vol 47 (3) ◽  
pp. 260-261
Author(s):  
Roger T. Dean
Keyword(s):  
Real Time ◽  
Music Performance ◽  
Tonal Music ◽  
Interactive Music ◽  
Sequence Generation ◽  
Serial Music ◽  
Keyboard Music ◽  
Music Generation ◽  
Sequence Transformations

Serial music, which is mainly non-tonal, superimposes compositional freedom onto an unusually rigorous process of pitch-sequence transformations based on ‘tone rows’: a row is usually a sequence of notes using each of the 12 chromatic pitches once. Compositional freedom comprises forming chords from the sequences, and in multi-strand music, also in simultaneously presenting different segments of pitch-sequences. The present project coded a real-time serial music composer for automatic or interactive music performance. This Serial Keyboardist Collaborator can perform keyboard music which is impossible for a human to realize. Surprisingly, it was also useful in making more tonal music based on the same rigorous pitch-sequence generation.

SongBot: An Interactive Music Generation Robotic System for Non-musicians Learning from A Song

2021 ◽  
Author(s):  
Kaiwen Xue ◽  
Zhixuan Liu ◽  
Jiaying Li ◽  
Xiaoqiang Ji ◽  
Huihuan Qian
Keyword(s):  
Robotic System ◽  
Interactive Music ◽  
Music Generation

scholarly journals Chaotic Music Generation System Using Music Conductor Gesture

2013 ◽  
Vol 17 (2) ◽  
pp. 194-200 ◽  
Author(s):  
Shuai Chen ◽  
◽  
Yoichiro Maeda ◽  
Yasutake Takahashi
Keyword(s):  
Real Time ◽  
Microsoft Kinect ◽  
Human Beings ◽  
Generation System ◽  
Interactive Music ◽  
Music Generation

In research on interactive music generation, we propose a music generation method in which the computer generates music under the recognition of a humanmusic conductor’s gestures. In this research, generated music is tuned by parameters of a network of chaotic elements which are determined by the recognized gesture in real time. The music conductor’s hand motions are detected by Microsoft Kinect in this system. Music theories are embedded in the algorithm and, as a result, generated music is richer. Furthermore, we constructed the music generation system and performed experiments for generating music composed by human beings.

scholarly journals Algorithmic interactive music generation in videogames

2020 ◽  
Vol 9 (1) ◽  
pp. 38-59 ◽  
Author(s):  
Alvaro E. Lopez Duarte
Keyword(s):  
Markov Models ◽  
Composition Studies ◽  
Algorithmic Composition ◽  
Interactive Music ◽  
Interactive Control ◽  
Combinatorial Properties ◽  
Time Interaction ◽  
Algorithmic Music ◽  
Music Generation ◽  
Evolutionary Music

In this article, I review the concept of algorithmic generative and interactive music and discuss the advantages and challenges of its implementation in videogames. Excessive repetition caused by low interactivity in music sequences through gameplay has been tackled primarily by using random or sequential containers, coupled with overlapping rules and adaptive mix parameters, as demonstrated in the Dynamic Music Units in Audiokinetic’s Wwise middleware. This approach provides a higher variety through re-combinatorial properties of music tracks and also a responsive and interactive music stream. However, it mainly uses prerecorded music sequences that reappear and are easy to recognize throughout gameplay. Generative principles such as single-seed design have been occasionally applied in game music scoring to generate material. Some of them are complemented with rules and are assigned to sections with low emotional requirements, but support for real-time interaction in gameplay situations, although desirable, is rarely found.While algorithmic note-by-note generation can offer interactive flexibility and infinite diversity, it poses significant challenges such as achieving human-like performativity and producing a distinctive narrative style through measurable parameters or program arguments. Starting with music generation, I examine conceptual implementations and technical challenges of algorithmic composition studies that use Markov models, a-life/evolutionary music, generative grammars, agents, and artificial neural networks/deep learning. For each model, I evaluate rule-based strategies for interactive music transformation using parameters provided by contextual gameplay situations. Finally, I propose a compositional tool design based in modular instances of algorithmic music generation, featuring stylistic interactive control in connection with an audio engine rendering system.

Indexing Musical Sequences in Large Datasets Using Relational Databases

2015 ◽  
Vol 6 (2) ◽  
pp. 1-18
Author(s):  
Aleksey Charapko ◽  
Ching-Hua Chuan
Keyword(s):  
Relational Databases ◽  
Large Datasets ◽  
Interactive Music ◽  
Indexing Methods ◽  
Satisfactory Performance ◽  
Music Generation ◽  
Performance Results

This paper proposes and tests models that provide quick searching and retrieval of continuations and the longest repeated suffix for data sequences, particularly musical data, using relational databases. The authors extend existing interactive music-generation systems by focusing on large input sequences. Algorithms for indexing prefix trees and factor oracles in relational databases are also proposed. Experiments using textual and musical data provide satisfactory performance results for the models using the two indexing methods.

Automatic Music Generation

2019 ◽  
Vol 7 (3) ◽  
pp. 80-82
Author(s):  
Lawakesh Patel ◽  
Nidhi Singh ◽  
Rizwan Khan
Keyword(s):  
Music Generation ◽  
Automatic Music Generation
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