170 research outputs found
RoboJam: A Musical Mixture Density Network for Collaborative Touchscreen Interaction
RoboJam is a machine-learning system for generating music that assists users
of a touchscreen music app by performing responses to their short
improvisations. This system uses a recurrent artificial neural network to
generate sequences of touchscreen interactions and absolute timings, rather
than high-level musical notes. To accomplish this, RoboJam's network uses a
mixture density layer to predict appropriate touch interaction locations in
space and time. In this paper, we describe the design and implementation of
RoboJam's network and how it has been integrated into a touchscreen music app.
A preliminary evaluation analyses the system in terms of training, musical
generation and user interaction
Neural Translation of Musical Style
Music is an expressive form of communication often used to convey emotion in
scenarios where "words are not enough". Part of this information lies in the
musical composition where well-defined language exists. However, a significant
amount of information is added during a performance as the musician interprets
the composition. The performer injects expressiveness into the written score
through variations of different musical properties such as dynamics and tempo.
In this paper, we describe a model that can learn to perform sheet music. Our
research concludes that the generated performances are indistinguishable from a
human performance, thereby passing a test in the spirit of a "musical Turing
test"
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
Music composition based on Artificial Neural Networks
In the recent years, research on Artificial Intelligence has ushered in a new
phase of technology evolution. Autonomous systems such as voice assistants or
self-driving cars are a present reality as first commercial systems have been already
launched to the market.
New applications emerge each year as huge amounts of generated data and
computational capabilities make the development of accurate and expert systems
plausible. This evolution is optimizing processes of many core fields such as agriculture,
telecommunications or medicine.
A quite technological field such as music is also beginning to notice changes,
as recommendation engines, synthesizers and music generation are attractive fields
of research with some preliminary results.
With this project, we intend to contribute to ease the process of music
creation making it more accessible to people. The subject of this project is the
design, development and experimentation of an AI engine to generate music. A
simple, but pleasant to hear artificially generated melody could serve as a base for
people to compose more complex pieces of music.
At the same time, the project sheds some light to the nuts and bolts of
novel techniques for music composition, as the Long Short Term Memory network
selected.
The system processes MIDI files and extracts relevant information for training
the network. The extracted data has been selected by analyzing the main aspects
used in the field of Music Information Retrieval.
An online listening test taken by subjects of different musical backgrounds
is designed to measure the quality of the artificial composer. The final results prove
that pleasant to hear melodies have been composed.Ingeniería de Sistemas Audiovisuale
Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models
Algorithmic music composition is a way of composing musical pieces with
minimal to no human intervention. While recurrent neural networks are
traditionally applied to many sequence-to-sequence prediction tasks, including
successful implementations of music composition, their standard supervised
learning approach based on input-to-output mapping leads to a lack of note
variety. These models can therefore be seen as potentially unsuitable for tasks
such as music generation. Generative adversarial networks learn the generative
distribution of data and lead to varied samples. This work implements and
compares adversarial and non-adversarial training of recurrent neural network
music composers on MIDI data. The resulting music samples are evaluated by
human listeners, their preferences recorded. The evaluation indicates that
adversarial training produces more aesthetically pleasing music.Comment: Submitted to a 2023 conference, 20 pages, 13 figure
A Survey of AI Music Generation Tools and Models
In this work, we provide a comprehensive survey of AI music generation tools,
including both research projects and commercialized applications. To conduct
our analysis, we classified music generation approaches into three categories:
parameter-based, text-based, and visual-based classes. Our survey highlights
the diverse possibilities and functional features of these tools, which cater
to a wide range of users, from regular listeners to professional musicians. We
observed that each tool has its own set of advantages and limitations. As a
result, we have compiled a comprehensive list of these factors that should be
considered during the tool selection process. Moreover, our survey offers
critical insights into the underlying mechanisms and challenges of AI music
generation
- …