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Found 6 papers, 5 papers with code
COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations
We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a contrastive learning method for musical audio representations that captures the harmonic and rhythmic coherence between samples.
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.
Fish sounds: towards the evaluation of marine acoustic biodiversity through data-driven audio source separation
Moreover, one of the causes of biodiversity loss is sound pollution; in data obtained from regions with loud anthropic noise, it is hard to separate the artificial from the fish sound manually.
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.
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Music Source Separation
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