8 research outputs found
A Recurrent Encoder-Decoder Approach with Skip-filtering Connections for Monaural Singing Voice Separation
The objective of deep learning methods based on encoder-decoder architectures
for music source separation is to approximate either ideal time-frequency masks
or spectral representations of the target music source(s). The spectral
representations are then used to derive time-frequency masks. In this work we
introduce a method to directly learn time-frequency masks from an observed
mixture magnitude spectrum. We employ recurrent neural networks and train them
using prior knowledge only for the magnitude spectrum of the target source. To
assess the performance of the proposed method, we focus on the task of singing
voice separation. The results from an objective evaluation show that our
proposed method provides comparable results to deep learning based methods
which operate over complicated signal representations. Compared to previous
methods that approximate time-frequency masks, our method has increased
performance of signal to distortion ratio by an average of 3.8 dB
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation
In deep neural networks with convolutional layers, each layer typically has
fixed-size/single-resolution receptive field (RF). Convolutional layers with a
large RF capture global information from the input features, while layers with
small RF size capture local details with high resolution from the input
features. In this work, we introduce novel deep multi-resolution fully
convolutional neural networks (MR-FCNN), where each layer has different RF
sizes to extract multi-resolution features that capture the global and local
details information from its input features. The proposed MR-FCNN is applied to
separate a target audio source from a mixture of many audio sources.
Experimental results show that using MR-FCNN improves the performance compared
to feedforward deep neural networks (DNNs) and single resolution deep fully
convolutional neural networks (FCNNs) on the audio source separation problem.Comment: arXiv admin note: text overlap with arXiv:1703.0801
Singing voice separation: a study on training data
In the recent years, singing voice separation systems showed increased
performance due to the use of supervised training. The design of training
datasets is known as a crucial factor in the performance of such systems. We
investigate on how the characteristics of the training dataset impacts the
separation performances of state-of-the-art singing voice separation
algorithms. We show that the separation quality and diversity are two important
and complementary assets of a good training dataset. We also provide insights
on possible transforms to perform data augmentation for this task
Trained Models for "A Recurrent Encoder-Decoder Approach With Skip-Filtering Connections For Monaural Singing Voice Separation"
<p>Support material (binary files) for the following work: S.I. Mimilakis, K. Drossos, T. Virtanen, G. Schuller, "A Recurrent Encoder-Decoder Approach With Skip-Filtering Connections For Monaural Singing Voice Separation", accepted for presentation at the 2017 IEEE International Workshop on Machine Learning for Signal Processing, September 25–28, 2017, Tokyo, Japan.</p>
<p>To be used here:Â https://github.com/Js-Mim/mlsp2017_svsep_skipfilt/</p
Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation
Monaural Singing Voice Separation (MSVS) is a challenging task and has been
studied for decades. Deep neural networks (DNNs) are the current
state-of-the-art methods for MSVS. However, the existing DNNs are often
designed manually, which is time-consuming and error-prone. In addition, the
network architectures are usually pre-defined, and not adapted to the training
data. To address these issues, we introduce a Neural Architecture Search (NAS)
method to the structure design of DNNs for MSVS. Specifically, we propose a new
multi-resolution Convolutional Neural Network (CNN) framework for MSVS namely
Multi-Resolution Pooling CNN (MRP-CNN), which uses various-size pooling
operators to extract multi-resolution features. Based on the NAS, we then
develop an evolving framework namely Evolving MRP-CNN (E-MRP-CNN), by
automatically searching the effective MRP-CNN structures using genetic
algorithms, optimized in terms of a single-objective considering only
separation performance, or multi-objective considering both the separation
performance and the model complexity. The multi-objective E-MRP-CNN gives a set
of Pareto-optimal solutions, each providing a trade-off between separation
performance and model complexity. Quantitative and qualitative evaluations on
the MIR-1K and DSD100 datasets are used to demonstrate the advantages of the
proposed framework over several recent baselines