Using a Bi-directional LSTM Model with Attention Mechanism trained on
MIDI Data for Generating Unique Music
- URL: http://arxiv.org/abs/2011.00773v1
- Date: Mon, 2 Nov 2020 06:43:28 GMT
- Title: Using a Bi-directional LSTM Model with Attention Mechanism trained on
MIDI Data for Generating Unique Music
- Authors: Ashish Ranjan, Varun Nagesh Jolly Behera, Motahar Reza
- Abstract summary: This paper proposes a bi-directional LSTM model with attention mechanism capable of generating similar type of music based on MIDI data.
The music generated by the model follows the theme/style of the music the model is trained on.
- Score: 0.25559196081940677
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generating music is an interesting and challenging problem in the field of
machine learning. Mimicking human creativity has been popular in recent years,
especially in the field of computer vision and image processing. With the
advent of GANs, it is possible to generate new similar images, based on trained
data. But this cannot be done for music similarly, as music has an extra
temporal dimension. So it is necessary to understand how music is represented
in digital form. When building models that perform this generative task, the
learning and generation part is done in some high-level representation such as
MIDI (Musical Instrument Digital Interface) or scores. This paper proposes a
bi-directional LSTM (Long short-term memory) model with attention mechanism
capable of generating similar type of music based on MIDI data. The music
generated by the model follows the theme/style of the music the model is
trained on. Also, due to the nature of MIDI, the tempo, instrument, and other
parameters can be defined, and changed, post generation.
Related papers
- MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models [57.47799823804519]
We are inspired by how musicians compose music not just from a movie script, but also through visualizations.
We propose MeLFusion, a model that can effectively use cues from a textual description and the corresponding image to synthesize music.
Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music.
arXiv Detail & Related papers (2024-06-07T06:38:59Z) - MidiCaps: A large-scale MIDI dataset with text captions [6.806050368211496]
This work aims to enable research that combines LLMs with symbolic music by presenting, the first openly available large-scale MIDI dataset with text captions.
Inspired by recent advancements in captioning techniques, we present a curated dataset of over 168k MIDI files with textual descriptions.
arXiv Detail & Related papers (2024-06-04T12:21:55Z) - MuPT: A Generative Symbolic Music Pretrained Transformer [73.47607237309258]
We explore the application of Large Language Models (LLMs) to the pre-training of music.
To address the challenges associated with misaligned measures from different tracks during generation, we propose a Synchronized Multi-Track ABC Notation (SMT-ABC Notation)
Our contributions include a series of models capable of handling up to 8192 tokens, covering 90% of the symbolic music data in our training set.
arXiv Detail & Related papers (2024-04-09T15:35:52Z) - Video2Music: Suitable Music Generation from Videos using an Affective
Multimodal Transformer model [32.801213106782335]
We develop a generative music AI framework, Video2Music, that can match a provided video.
In a thorough experiment, we show that our proposed framework can generate music that matches the video content in terms of emotion.
arXiv Detail & Related papers (2023-11-02T03:33:00Z) - Graph-based Polyphonic Multitrack Music Generation [9.701208207491879]
This paper introduces a novel graph representation for music and a deep Variational Autoencoder that generates the structure and the content of musical graphs separately.
By separating the structure and content of musical graphs, it is possible to condition generation by specifying which instruments are played at certain times.
arXiv Detail & Related papers (2023-07-27T15:18:50Z) - Simple and Controllable Music Generation [94.61958781346176]
MusicGen is a single Language Model (LM) that operates over several streams of compressed discrete music representation, i.e., tokens.
Unlike prior work, MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns.
arXiv Detail & Related papers (2023-06-08T15:31:05Z) - Learning to Generate Music With Sentiment [1.8275108630751844]
This paper presents a generative Deep Learning model that can be directed to compose music with a given sentiment.
Besides music generation, the same model can be used for sentiment analysis of symbolic music.
arXiv Detail & Related papers (2021-03-09T03:16:52Z) - PopMAG: Pop Music Accompaniment Generation [190.09996798215738]
We propose a novel MUlti-track MIDI representation (MuMIDI) which enables simultaneous multi-track generation in a single sequence.
MuMIDI enlarges the sequence length and brings the new challenge of long-term music modeling.
We call our system for pop music accompaniment generation as PopMAG.
arXiv Detail & Related papers (2020-08-18T02:28:36Z) - Foley Music: Learning to Generate Music from Videos [115.41099127291216]
Foley Music is a system that can synthesize plausible music for a silent video clip about people playing musical instruments.
We first identify two key intermediate representations for a successful video to music generator: body keypoints from videos and MIDI events from audio recordings.
We present a Graph$-$Transformer framework that can accurately predict MIDI event sequences in accordance with the body movements.
arXiv Detail & Related papers (2020-07-21T17:59:06Z) - RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement
Learning [69.20460466735852]
This paper presents a deep reinforcement learning algorithm for online accompaniment generation.
The proposed algorithm is able to respond to the human part and generate a melodic, harmonic and diverse machine part.
arXiv Detail & Related papers (2020-02-08T03:53:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.