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Found 11 papers, 7 papers with code
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations.
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.
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding.
Neural Vocoder is All You Need for Speech Super-resolution
In this paper, we propose a neural vocoder based speech super-resolution method (NVSR) that can handle a variety of input resolution and upsampling ratios.
Ranked #2 on
Audio Super-Resolution
on VCTK Multi-Speaker
Learning source-aware representations of music in a discrete latent space
To address this issue, we propose a novel method to learn source-awarelatent representations of music through Vector-Quantized Variational Auto-Encoder(VQ-VAE). We train our VQ-VAE to encode an input mixture into a tensor of integers in a discrete latentspace, and design them to have a decomposed structure which allows humans to manipulatethe latent vector in a source-aware manner.
LightSAFT: Lightweight Latent Source Aware Frequency Transform for Source Separation
Conditioned source separations have attracted significant attention because of their flexibility, applicability and extensionality.
KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing
This paper proposes a two-stream neural network for music demixing, called KUIELab-MDX-Net, which shows a good balance of performance and required resources.
Ranked #7 on
Music Source Separation
on MUSDB18
Music Demixing Challenge 2021
The main differences compared with the past challenges are 1) the competition is designed to more easily allow machine learning practitioners from other disciplines to participate, 2) evaluation is done on a hidden test set created by music professionals dedicated exclusively to the challenge to assure the transparency of the challenge, i. e., the test set is not accessible from anyone except the challenge organizers, and 3) the dataset provides a wider range of music genres and involved a greater number of mixing engineers.
AMSS-Net: Audio Manipulation on User-Specified Sources with Textual Queries
This paper proposes a neural network that performs audio transformations to user-specified sources (e. g., vocals) of a given audio track according to a given description while preserving other sources not mentioned in the description.
LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation
Recent deep-learning approaches have shown that Frequency Transformation (FT) blocks can significantly improve spectrogram-based single-source separation models by capturing frequency patterns.
Ranked #20 on
Music Source Separation
on MUSDB18
Investigating U-Nets with various Intermediate Blocks for Spectrogram-based Singing Voice Separation
Singing Voice Separation (SVS) tries to separate singing voice from a given mixed musical signal.
