729 research outputs found
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
The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation
With recent breakthroughs in artificial neural networks, deep generative
models have become one of the leading techniques for computational creativity.
Despite very promising progress on image and short sequence generation,
symbolic music generation remains a challenging problem since the structure of
compositions are usually complicated. In this study, we attempt to solve the
melody generation problem constrained by the given chord progression. This
music meta-creation problem can also be incorporated into a plan recognition
system with user inputs and predictive structural outputs. In particular, we
explore the effect of explicit architectural encoding of musical structure via
comparing two sequential generative models: LSTM (a type of RNN) and WaveNet
(dilated temporal-CNN). As far as we know, this is the first study of applying
WaveNet to symbolic music generation, as well as the first systematic
comparison between temporal-CNN and RNN for music generation. We conduct a
survey for evaluation in our generations and implemented Variable Markov Oracle
in music pattern discovery. Experimental results show that to encode structure
more explicitly using a stack of dilated convolution layers improved the
performance significantly, and a global encoding of underlying chord
progression into the generation procedure gains even more.Comment: 8 pages, 13 figure
NBP 2.0: Updated Next Bar Predictor, an Improved Algorithmic Music Generator
Deep neural network advancements have enabled machines to produce melodies emulating human-composed music. However, the implementation of such machines is costly in terms of resources. In this paper, we present NBP 2.0, a refinement of the previous model next bar predictor (NBP) with two notable improvements: first, transforming each training instance to anchor all the notes to its musical scale, and second, changing the model architecture itself. NBP 2.0 maintained its straightforward and lightweight implementation, which is an advantage over the baseline models. Improvements were assessed using quantitative and qualitative metrics and, based on the results, the improvements from these changes made are notable
LooPy: A Research-Friendly Mix Framework for Music Information Retrieval on Electronic Dance Music
Music information retrieval (MIR) has gone through an explosive development
with the advancement of deep learning in recent years. However, music genres
like electronic dance music (EDM) has always been relatively less investigated
compared to others. Considering its wide range of applications, we present a
Python package for automated EDM audio generation as an infrastructure for MIR
for EDM songs, to mitigate the difficulty of acquiring labelled data. It is a
convenient tool that could be easily concatenated to the end of many symbolic
music generation pipelines. Inside this package, we provide a framework to
build professional-level templates that could render a well-produced track from
specified melody and chords, or produce massive tracks given only a specific
key by our probabilistic symbolic melody generator. Experiments show that our
mixes could achieve the same quality of the original reference songs produced
by world-famous artists, with respect to both subjective and objective
criteria. Our code is accessible in this repository:
https://github.com/Gariscat/loopy and the official site of the project is also
online https://loopy4edm.com .Comment: Submitted to ACM MM 202
Toward Interactive Music Generation: A Position Paper
Music generation using deep learning has received considerable attention in recent years. Researchers have developed various generative models capable of imitating musical conventions, comprehending the musical corpora, and generating new samples based on the learning outcome. Although the samples generated by these models are persuasive, they often lack musical structure and creativity. For instance, a vanilla end-to-end approach, which deals with all levels of music representation at once, does not offer human-level control and interaction during the learning process, leading to constrained results. Indeed, music creation is a recurrent process that follows some principles by a musician, where various musical features are reused or adapted. On the other hand, a musical piece adheres to a musical style, breaking down into precise concepts of timbre style, performance style, composition style, and the coherency between these aspects. Here, we study and analyze the current advances in music generation using deep learning models through different criteria. We discuss the shortcomings and limitations of these models regarding interactivity and adaptability. Finally, we draw the potential future research direction addressing multi-agent systems and reinforcement learning algorithms to alleviate these shortcomings and limitations
VGM-RNN: Recurrent Neural Networks for Video Game Music Generation
The recent explosion of interest in deep neural networks has affected and in some cases reinvigorated work in fields as diverse as natural language processing, image recognition, speech recognition and many more. For sequence learning tasks, recurrent neural networks and in particular LSTM-based networks have shown promising results. Recently there has been interest – for example in the research by Google’s Magenta team – in applying so-called “language modeling” recurrent neural networks to musical tasks, including for the automatic generation of original music. In this work we demonstrate our own LSTM-based music language modeling recurrent network. We show that it is able to learn musical features from a MIDI dataset and generate output that is musically interesting while demonstrating features of melody, harmony and rhythm. We source our dataset from VGMusic.com, a collection of user-submitted MIDI transcriptions of video game songs, and attempt to generate output which emulates this kind of music
- …