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Found 14 papers, 4 papers with code
MR-MT3: Memory Retaining Multi-Track Music Transcription to Mitigate Instrument Leakage
This paper presents enhancements to the MT3 model, a state-of-the-art (SOTA) token-based multi-instrument automatic music transcription (AMT) model.
MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models
This paper introduces a novel approach to the editing of music generated by such models, enabling the modification of specific attributes, such as genre, mood and instrument, while maintaining other aspects unchanged.
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations.
Manifold Preserving Guided Diffusion
Despite the recent advancements, conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training.
On the Language Encoder of Contrastive Cross-modal Models
Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks.
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion
Consistency Models (CM) (Song et al., 2023) accelerate score-based diffusion model sampling at the cost of sample quality but lack a natural way to trade-off quality for speed.
Ranked #2 on
Image Generation
on CIFAR-10
Timbre-Trap: A Low-Resource Framework for Instrument-Agnostic Music Transcription
Several works have explored multi-instrument transcription as a means to bolster the performance of models on low-resource tasks, but these methods face the same data availability issues.
VRDMG: Vocal Restoration via Diffusion Posterior Sampling with Multiple Guidance
Restoring degraded music signals is essential to enhance audio quality for downstream music manipulation.
Automatic Piano Transcription with Hierarchical Frequency-Time Transformer
This is especially helpful when determining the precise onset and offset for each note in the polyphonic piano content.
Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects
We propose an end-to-end music mixing style transfer system that converts the mixing style of an input multitrack to that of a reference song.
Automatic music mixing with deep learning and out-of-domain data
Music mixing traditionally involves recording instruments in the form of clean, individual tracks and blending them into a final mixture using audio effects and expert knowledge (e. g., a mixing engineer).
Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks
A central task of a Disc Jockey (DJ) is to create a mixset of mu-sic with seamless transitions between adjacent tracks.
Preventing Oversmoothing in VAE via Generalized Variance Parameterization
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative.
AR-ELBO: Preventing Posterior Collapse Induced by Oversmoothing in Gaussian VAE
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon that the learned latent space becomes uninformative.
