Search Results
All for One and One for All: Improving Music Separation by Bridging Networks
This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes.
Ranked #21 on
Music Source Separation
on MUSDB18
Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained Models
We present and release a new tool for music source separation with pre-trained models called Spleeter. Spleeter was designed with ease of use, separation performance and speed in mind.
Ranked #19 on
Music Source Separation
on MUSDB18
(using extra training data)
Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation
This approach has several limitations: 1) its incorrect phase reconstruction degrades the performance, 2) it limits the magnitude of masks between 0 and 1 while we observe that 22% of time-frequency bins have ideal ratio mask values of over~1 in a popular dataset, MUSDB18, 3) its potential on very deep architectures is under-explored.
Sound Audio and Speech Processing
The Whole Is Greater than the Sum of Its Parts: Improving DNN-based Music Source Separation
We modify the target network, i. e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information.
Densely connected multidilated convolutional networks for dense prediction tasks
In this paper, we claim the importance of a dense simultaneous modeling of multiresolution representation and propose a novel CNN architecture called densely connected multidilated DenseNet (D3Net).
Ranked #47 on
Semantic Segmentation
on Cityscapes test
Densely Connected Multi-Dilated Convolutional Networks for Dense Prediction Tasks
In this paper, we claim the importance of a dense simultaneous modeling of multiresolution representation and propose a novel CNN architecture called densely connected multidilated DenseNet (D3Net).
D3Net: Densely connected multidilated DenseNet for music source separation
In this paper, we claim the importance of a rapid growth of a receptive field and a simultaneous modeling of multi-resolution data in a single convolution layer, and propose a novel CNN architecture called densely connected dilated DenseNet (D3Net).
Ranked #12 on
Music Source Separation
on MUSDB18
(using extra training data)
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
Universal Source Separation with Weakly Labelled Data
To use large-scale weakly labeled/unlabeled audio data for audio source separation, we propose a universal audio source separation framework containing: 1) an audio tagging model trained on weakly labeled data as a query net; and 2) a conditional source separation model that takes query net outputs as conditions to separate arbitrary sound sources.
Sound Audio and Speech Processing
Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data
Our approach uses a single model for source separation of multiple sound types, and relies solely on weakly-labeled data for training.
Ranked #1 on
Audio Source Separation
on AudioSet
