Audio source separation aims to extract individual sources from mixtures of multiple sound sources. Many techniques have been developed such as independent component analysis, computational auditory scene analysis, and non-negative matrix factorisation. A method based on Deep Neural Networks (DNNs) and time-frequency (T-F) masking has been recently developed for binaural audio source separation...

Abstract: The roles played by learning and memorization represent an important topic in deep learning research. Recent work on this subject has shown that the optimization behavior of DNNs trained on shuffled labels is qualitatively different from DNNs trained with real labels. Here, we propose a novel permutation approach that can differentiate memorization from learning in deep neural network...

this article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. using 37 years iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. this study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series...

Although complex-valued neural networks (CVNNs) – networks which can operate with complex arithmetic – have been around for a while, they have not been given reconsideration since the breakthrough of deep network architectures. This paper presents a critical assessment whether the novel tool set of deep neural networks (DNNs) should be extended to complex-valued arithmetic. Indeed, with DNNs ma...

this paper presents the application of radial basis neural networks to the development of a novel method for the condition monitoring and fault diagnosis of synchronous generators. in the proposed scheme, flux linkage analysis is used to reach a decision. probabilistic neural network (pnn) and discrete wavelet transform (dwt) are used in design of fault diagnosis system. pnn as main part of thi...

Previous work in speech-driven head motion synthesis is centred around Hidden Markov Model (HMM) based methods and data that does not show a large variability of expressiveness in both speech and motion. When using expressive data, these systems often fail to produce satisfactory results. Recent studies have shown that using deep neural networks (DNNs) results in a better synthesis of head moti...

In the past few years, deep neural networks (DNNs) have achieved great successes in speech recognition. The deep network model can be viewed as a series of feature transforms followed by a log-linear classifier. For input of speeches from different bandwidths, although the hidden layer transform and log-linear classification can be shared, the input layer transforms should be specially designed...

Recent works investigated the generalization properties in deep neural networks (DNNs) by studying the Information Bottleneck in DNNs. However, the measurement of the mutual information (MI) is often inaccurate due to the density estimation. To address this issue, we propose to measure the dependency instead of MI between layers in DNNs. Specifically, we propose to use Hilbert-Schmidt Independe...

Recently, there have been significant advances in deep neural networks (DNNs) and they have shown superior performance in audio and image processing. In this paper, we explore DNNs to push the limit for branch prediction. We treat branch prediction as a classification problem and explore both deep convolutional neural networks (CNNs) and deep belief networks (DBNs) for branch prediction. We ana...

the probable lack of some arcs and nodes in the stochastic networks is considered in this paper, and its effect is shown as the arrival probability from a given source node to a given sink node. a discrete time markov chain with an absorbing state is established in a directed acyclic network. then, the probability of transition from the initial state to the absorbing state is computed. it is as...