Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders

Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals. The success of many existing systems is therefore largely dependent on the choice of features used for training. In this work, we introduce a novel multi-channel, multi-resolution convolutional auto-encoder neural network that works on raw time-domain signals to determine appropriate multi-resolution features for separating the singing-voice from stereo music. Our experimental results show that the proposed method can achieve multi-channel audio source separation without the need for hand-crafted features or any pre- or post-processing

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

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The 4-Tions: Your Guide to Developing Successful Job Search Strategies

The 4-Tions is designed to provide you with fundamental job search strategies and practices to help you develop habits that will stay with you your entire professional life. The 4-Tions represents what the authors consider to be the 4 most important aspects of the job search. These 4 areas are: ❋ Documenta tion : Resume, Application Information, References ❋ Prepara tion : Company Background, History, Questions ❋ Presenta tion : Appearance, Clothing, Hygiene, Grooming ❋ Communica tion : Articulation, Eye Contact, Active Listeninghttps://digitalcommons.olivet.edu/arch_alumbk/1000/thumbnail.jp

Atypical plug formation in internal elastoviscoplastic fluid flows over a non-smooth topology

An experimental and computational investigation of the internal flow of elastoviscoplastic fluids over non-smooth topologies is presented in two complimentary studies. In the first study, we visualize the creeping flow of a Carbopol gel over a cavity embedded in a thin slot using Optical Coherence Tomography (OCT) and confocal microscopy. We measure the size and shape of the plug as a function of Bingham and Weissenberg numbers. An asymmetry in the plug shape is observed which is also evident in our second study -- numerical simulations using adaptive finite element method based upon an augmented Lagrangian scheme. We quantify the asymmetry and present the results as a function of the product of the Weissenberg and Bingham numbers which collapse onto a single curve for each of these geometries. These findings underscore the theoretical underpinnings of the synergy between elasticity and plasticity of these complex fluids

Single-carrier frequency-domain equalization with hybrid decision feedback equalizer for Hammerstein channels containing nonlinear transmit amplifier

We propose a nonlinear hybrid decision feedback equalizer (NHDFE) for single-carrier (SC) block transmission systems with nonlinear transmit high power amplifier (HPA), which significantly outperforms our previous nonlinear SC frequency-domain equalization (NFDE) design. To obtain the coefficients of the channel impulse response (CIR) as well as to estimate the nonlinear mapping and the inverse nonlinear mapping of the HPA, we adopt a complex-valued (CV) B-spline neural network approach. Specifically, we use a CV B-spline neural network to model the nonlinear HPA, and we develop an efficient alternating least squares scheme for estimating the parameters of the Hammerstein channel, including both the CIR coefficients and the parameters of the CV B-spline model. We also adopt another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can be estimated using the least squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. The effectiveness of our NHDFE design is demonstrated in a simulation study, which shows that the NHDFE achieves a signal-to-noise ratio gain of 4dB over the NFDE at the bit error rate level of 10−4