171 research outputs found
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
Generating music has a few notable differences from generating images and
videos. First, music is an art of time, necessitating a temporal model. Second,
music is usually composed of multiple instruments/tracks with their own
temporal dynamics, but collectively they unfold over time interdependently.
Lastly, musical notes are often grouped into chords, arpeggios or melodies in
polyphonic music, and thereby introducing a chronological ordering of notes is
not naturally suitable. In this paper, we propose three models for symbolic
multi-track music generation under the framework of generative adversarial
networks (GANs). The three models, which differ in the underlying assumptions
and accordingly the network architectures, are referred to as the jamming
model, the composer model and the hybrid model. We trained the proposed models
on a dataset of over one hundred thousand bars of rock music and applied them
to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings.
A few intra-track and inter-track objective metrics are also proposed to
evaluate the generative results, in addition to a subjective user study. We
show that our models can generate coherent music of four bars right from
scratch (i.e. without human inputs). We also extend our models to human-AI
cooperative music generation: given a specific track composed by human, we can
generate four additional tracks to accompany it. All code, the dataset and the
rendered audio samples are available at https://salu133445.github.io/musegan/ .Comment: to appear at AAAI 201
Algorithmic Music Composition and Accompaniment Using Neural Networks
The goal of this project was to use neural networks as a tool for live music performance. Specifically, the intention was to adapt a preexisting neural network code library to work in Max, a visual programming language commonly used to create instruments and effects for electronic music and audio processing. This was done using ConvNetJS, a JavaScript library created by Andrej Karpathy.
Several neural network models were trained using a range of different training data, including music from various genres. The resulting neural network-based instruments were used to play brief pieces of music, which they used as input to create unique musical output.
Max, while useful for live performance and audio processing, proved to be somewhat impractical for this project. Implementing too complex of a network caused performance issues and even crashing. Because of this, smaller networks, which are less robust in their prediction abilities had to be used, producing very simplistic musical patterns
Musical Deep Learning: Stylistic Melodic Generation with Complexity Based Similarity
The wide-ranging impact of deep learning models implies significant application in music analysis, retrieval, and generation. Initial findings from musical application of a conditional restricted Boltzmann machine (CRBM) show promise towards informing creative computation. Taking advantage of the CRBM’s ability to model temporal dependencies full reconstructions of pieces are achievable given a few starting seed notes. The generation of new material using figuration from the training corpus requires restrictions on the size
and memory space of the CRBM, forcing associative rather than perfect recall. Musical analysis and information complexity measures show the musical encoding to be the primary determinant of the nature of the generated results
RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning
This paper presents a deep reinforcement learning algorithm for online
accompaniment generation, with potential for real-time interactive
human-machine duet improvisation. Different from offline music generation and
harmonization, online music accompaniment requires the algorithm to respond to
human input and generate the machine counterpart in a sequential order. We cast
this as a reinforcement learning problem, where the generation agent learns a
policy to generate a musical note (action) based on previously generated
context (state). The key of this algorithm is the well-functioning reward
model. Instead of defining it using music composition rules, we learn this
model from monophonic and polyphonic training data. This model considers the
compatibility of the machine-generated note with both the machine-generated
context and the human-generated context. Experiments show that this algorithm
is able to respond to the human part and generate a melodic, harmonic and
diverse machine part. Subjective evaluations on preferences show that the
proposed algorithm generates music pieces of higher quality than the baseline
method
A Review of Intelligent Music Generation Systems
With the introduction of ChatGPT, the public's perception of AI-generated
content (AIGC) has begun to reshape. Artificial intelligence has significantly
reduced the barrier to entry for non-professionals in creative endeavors,
enhancing the efficiency of content creation. Recent advancements have seen
significant improvements in the quality of symbolic music generation, which is
enabled by the use of modern generative algorithms to extract patterns implicit
in a piece of music based on rule constraints or a musical corpus.
Nevertheless, existing literature reviews tend to present a conventional and
conservative perspective on future development trajectories, with a notable
absence of thorough benchmarking of generative models. This paper provides a
survey and analysis of recent intelligent music generation techniques,
outlining their respective characteristics and discussing existing methods for
evaluation. Additionally, the paper compares the different characteristics of
music generation techniques in the East and West as well as analysing the
field's development prospects
AI Methods in Algorithmic Composition: A Comprehensive Survey
Algorithmic composition is the partial or total automation of the process of music composition
by using computers. Since the 1950s, different computational techniques related to
Artificial Intelligence have been used for algorithmic composition, including grammatical
representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint
programming and evolutionary algorithms. This survey aims to be a comprehensive
account of research on algorithmic composition, presenting a thorough view of the field for
researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project
(IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for
the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo
y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC-
5123) from the ConsejerÃa de Innovación y Ciencia de AndalucÃa
Generation of Two-Voice Imitative Counterpoint from Statistical Models
Generating new music based on rules of counterpoint has been deeply studied in music informatics. In this article, we try to go further, exploring a method for generating new music based on the style of Palestrina, based on combining statistical generation and pattern discovery. A template piece is used for pattern discovery, and the patterns are selected and organized according to a probabilistic distribution, using horizontal viewpoints to describe melodic properties of events. Once the template is covered with patterns, two-voice counterpoint in a florid style is generated into those patterns using a first-order Markov model. The template method solves the problem of coherence and imitation never addressed before in previous research in counterpoint music generation. For constructing the Markov model, vertical slices of pitch and rhythm are compiled over a large corpus of dyads from Palestrina masses. The template enforces different restrictions that filter the possible paths through the generation process. A double backtracking algorithm is implemented to handle cases where no solutions are found at some point within a generation path. Results are evaluated by both information content and listener evaluation, and the paper concludes with a proposed relationship between musical quality and information content. Part of this research has been presented at SMC 2016 in Hamburg, Germany
Evaluating Musical Foreshadowing of Videogame Narrative Experiences
We experiment with mood-expressing, procedurally gener-ated music for narrative foreshadowing in videogames, in-vestigating the relationship between music and the player’s experience of narrative events in a game. We designed and conducted a user study in which the game’s music expresses true foreshadowing in some trials (e.g. foreboding music before a negative event) and false foreshadowing in others (e.g. happy music that does not lead to a positive event). We observed players playing the game, recorded analytics data, and had them complete a survey upon completion of the gameplay. Thirty undergraduate and graduate students participated in the study. Statistical analyses suggest that the use of musical cues for narrative foreshadowing induces a better perceived consistency between music and game narra-tive. Surprisingly, false foreshadowing was found to enhance the player’s enjoyment
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