Apollo: An Interactive Environment for Generating Symbolic Musical Phrases using Corpus-based Style Imitation
- URL: http://arxiv.org/abs/2504.14055v1
- Date: Fri, 18 Apr 2025 19:53:51 GMT
- Title: Apollo: An Interactive Environment for Generating Symbolic Musical Phrases using Corpus-based Style Imitation
- Authors: Renaud Bougueng Tchemeube, Jeff Ens, Philippe Pasquier,
- Abstract summary: We introduce Apollo, an interactive music application for generating symbolic phrases of conventional western music.<n>The system makes it possible for music artists and researchers to generate new musical phrases in the style of the proposed corpus.<n>The generated symbolic music materials, encoded in the MIDI format, can be exported or streamed for various purposes.
- Score: 5.649205001069577
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the recent developments in machine intelligence and web technologies, new generative music systems are being explored for assisted composition using machine learning techniques on the web. Such systems are built for various tasks such as melodic, harmonic or rhythm generation, music interpolation, continuation and style imitation. In this paper, we introduce Apollo, an interactive music application for generating symbolic phrases of conventional western music using corpus-based style imitation techniques. In addition to enabling the construction and management of symbolic musical corpora, the system makes it possible for music artists and researchers to generate new musical phrases in the style of the proposed corpus. The system is available as a desktop application. The generated symbolic music materials, encoded in the MIDI format, can be exported or streamed for various purposes including using them as seed material for musical projects. We present the system design, implementation details, discuss and conclude with future work for the system.
Related papers
- MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization [52.498942604622165]
This paper presents MuVi, a framework to generate music that aligns with video content.
MuVi analyzes video content through a specially designed visual adaptor to extract contextually and temporally relevant features.
We show that MuVi demonstrates superior performance in both audio quality and temporal synchronization.
arXiv Detail & Related papers (2024-10-16T18:44:56Z) - MMT-BERT: Chord-aware Symbolic Music Generation Based on Multitrack Music Transformer and MusicBERT [44.204383306879095]
We propose a novel symbolic music representation and Generative Adversarial Network (GAN) framework specially designed for symbolic multitrack music generation.
To build a robust multitrack music generator, we fine-tune a pre-trained MusicBERT model to serve as the discriminator, and incorporate relativistic standard loss.
arXiv Detail & Related papers (2024-09-02T03:18:56Z) - 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) - MuPT: A Generative Symbolic Music Pretrained Transformer [56.09299510129221]
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) - 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) - A Dataset for Greek Traditional and Folk Music: Lyra [69.07390994897443]
This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data.
The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre.
arXiv Detail & Related papers (2022-11-21T14:15:43Z) - Setting the rhythm scene: deep learning-based drum loop generation from
arbitrary language cues [0.0]
We present a novel method that generates 2 compasses of a 4-piece drum pattern that embodies the "mood" of a language cue.
We envision this tool as composition aid for electronic music and audiovisual soundtrack production, or an improvisation tool for live performance.
In order to produce the training samples for this model, besides manual annotation of the "scene" or "mood" terms, we have designed a novel method to extract the consensus drum track of any song.
arXiv Detail & Related papers (2022-09-20T21:53:35Z) - Symphony Generation with Permutation Invariant Language Model [57.75739773758614]
We present a symbolic symphony music generation solution, SymphonyNet, based on a permutation invariant language model.
A novel transformer decoder architecture is introduced as backbone for modeling extra-long sequences of symphony tokens.
Our empirical results show that our proposed approach can generate coherent, novel, complex and harmonious symphony compared to human composition.
arXiv Detail & Related papers (2022-05-10T13:08:49Z) - Quantized GAN for Complex Music Generation from Dance Videos [48.196705493763986]
We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates musical samples conditioned on dance videos.
Our proposed framework takes dance video frames and human body motion as input, and learns to generate music samples that plausibly accompany the corresponding input.
arXiv Detail & Related papers (2022-04-01T17:53:39Z) - LyricJam: A system for generating lyrics for live instrumental music [11.521519161773288]
We describe a real-time system that receives a live audio stream from a jam session and generates lyric lines that are congruent with the live music being played.
Two novel approaches are proposed to align the learned latent spaces of audio and text representations.
arXiv Detail & Related papers (2021-06-03T16:06:46Z) - Personalized Popular Music Generation Using Imitation and Structure [1.971709238332434]
We propose a statistical machine learning model that is able to capture and imitate the structure, melody, chord, and bass style from a given example seed song.
An evaluation using 10 pop songs shows that our new representations and methods are able to create high-quality stylistic music.
arXiv Detail & Related papers (2021-05-10T23:43:00Z)
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.