Search Results for author:
Found 8 papers, 6 papers with code
Generalized Multi-Source Inference for Text Conditioned Music Diffusion Models
Multi-Source Diffusion Models (MSDM) allow for compositional musical generation tasks: generating a set of coherent sources, creating accompaniments, and performing source separation.
Zero-Shot Duet Singing Voices Separation with Diffusion Models
In this paper, we examine this problem in the context of duet singing voices separation, and propose a method to enforce the coherency of singer identity by splitting the mixture into overlapping segments and performing posterior sampling in an auto-regressive manner, conditioning on the previous segment.
SyncFusion: Multimodal Onset-synchronized Video-to-Audio Foley Synthesis
Sound design involves creatively selecting, recording, and editing sound effects for various media like cinema, video games, and virtual/augmented reality.
Accelerating Transformer Inference for Translation via Parallel Decoding
We propose to reframe the standard greedy autoregressive decoding of MT with a parallel formulation leveraging Jacobi and Gauss-Seidel fixed-point iteration methods for fast inference.
Multi-Source Diffusion Models for Simultaneous Music Generation and Separation
In this work, we define a diffusion-based generative model capable of both music synthesis and source separation by learning the score of the joint probability density of sources sharing a context.
Latent Autoregressive Source Separation
Autoregressive models have achieved impressive results over a wide range of domains in terms of generation quality and downstream task performance.
Adversarial Permutation Invariant Training for Universal Sound Separation
Universal sound separation consists of separating mixes with arbitrary sounds of different types, and permutation invariant training (PIT) is used to train source agnostic models that do so.
Unsupervised Source Separation via Bayesian Inference in the Latent Domain
State of the art audio source separation models rely on supervised data-driven approaches, which can be expensive in terms of labeling resources.
Ranked #1 on
Music Source Separation
on Slakh2100
