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Found 11 papers, 11 papers with code
Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls
We apply this method to fine-tune MusicGen, a leading autoregressive music generation model.
Content-based Controls For Music Large Language Modeling
We aim to further equip the models with direct and content-based controls on innate music languages such as pitch, chords and drum track.
Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls
We propose Polyffusion, a diffusion model that generates polyphonic music scores by regarding music as image-like piano roll representations.
Self-Supervised Hierarchical Metrical Structure Modeling
We propose a novel method to model hierarchical metrical structures for both symbolic music and audio signals in a self-supervised manner with minimal domain knowledge.
Learning Hierarchical Metrical Structure Beyond Measures
In this paper, we explore a data-driven approach to automatically extract hierarchical metrical structures from scores.
Interpreting Song Lyrics with an Audio-Informed Pre-trained Language Model
Lyric interpretations can help people understand songs and their lyrics quickly, and can also make it easier to manage, retrieve and discover songs efficiently from the growing mass of music archives.
A Unified Model for Zero-shot Music Source Separation, Transcription and Synthesis
We propose a unified model for three inter-related tasks: 1) to \textit{separate} individual sound sources from a mixed music audio, 2) to \textit{transcribe} each sound source to MIDI notes, and 3) to\textit{ synthesize} new pieces based on the timbre of separated sources.
POP909: A Pop-song Dataset for Music Arrangement Generation
The main body of the dataset contains the vocal melody, the lead instrument melody, and the piano accompaniment for each song in MIDI format, which are aligned to the original audio files.
PIANOTREE VAE: Structured Representation Learning for Polyphonic Music
The dominant approach for music representation learning involves the deep unsupervised model family variational autoencoder (VAE).
Deep Music Analogy Via Latent Representation Disentanglement
Analogy-making is a key method for computer algorithms to generate both natural and creative music pieces.
