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In this paper, a joint synchronization and Doppler scaling factor estimation algorithm has been proposed for underwater acoustic communications. The training sequence, which consists of two Zadoff-Chu (ZC) sequences being conjugate with each other, is utilized to synchronize and estimate Doppler scaling factor, time delay and carrier frequency offsets (CFOs). ZC sequences are well designed to show...
Deep Denoising Autoencoder (DDAE) is an effective method for noise reduction and speech enhancement. However, a single DDAE with a fixed number of frames for neural network input cannot extract contextual information sufficiently. It has also less generalization in unknown SNRs (signal-to-noise-ratio) and the enhanced output has some residual noise. In this paper, we use a modular model in which three...
Binaural features of interaural level difference and interaural phase difference have proved to be very effective in training deep neural networks (DNNs), to generate time-frequency masks for target speech extraction in speech-speech mixtures. However, effectiveness of binaural features is reduced in more common speech-noise scenarios, since the noise may over-shadow the speech in adverse conditions...
Acoustic event detection (AED) is currently a very active research area with multiple applications in the development of smart acoustic spaces. In this context, the advances brought by Internet of Things (IoT) platforms where multiple distributed microphones are available have also contributed to this interest. In such scenarios, the use of data fusion techniques merging information from several sensors...
In this paper we consider centralized cooperative spectrum sensing (SS) techniques for cognitive radio networks using energy detector scheme. In light of the requirements imposed by centralized SS methods such as Maximum Ratio Combining (MRC), namely the estimation and transmission of the signal-to-noise ratio (SNR) on each secondary user, as well as the transmission of the exact energy level to the...
Recently, automatic modulation classification techniques using convolutional neural networks on raw IQ samples have been investigated and show promise when compared to more traditional likelihood-based or feature-based techniques. While likelihood-based and feature-based techniques are effective, making classification decisions directly on the raw IQ samples allows for reduced system complexity and...
This paper proposes a neural network (NN) approach for demodulating output signals of a nonlinear channel with memory. The feed-forward neural network is trained to learn the appropriate mapping between nonlinear input patterns and source bits. The simulation results provide some evidence that neural networks can learn the effect of nonlinear channels with memory and demodulate the output signal of...
Estimation of intracranial sources, using inverse solutions methods, has been proposed as a mean to improve performance in non-invasive brain-computer interfaces. These methods estimate the activity of a large number of neural sources from a smaller number of scalp electroencephalography (EEG) channels. This is a highly undetermined problem and regularisation constraints need to be applied. In this...
Sound event detection (SED) in environmental recordings is a key topic of research in machine listening, with applications in noise monitoring for smart cities, self-driving cars, surveillance, bioa-coustic monitoring, and indexing of large multimedia collections. Developing new solutions for SED often relies on the availability of strongly labeled audio recordings, where the annotation includes the...
In this paper, the problem of adaptive beamforming in the presence of direction-of-arrival (DOA) mismatch is investigated. To develop a robust beamformer against such an imperfection, a new approach is devised by formulating an output signal-to-interference-plus-noise ratio (SINR) maximization problem. In particular, the proposed robust beamforming approach consists of two steps. At first, the standard...
Multichannel receivers are usually employed in high-rate underwater acoustic communication to achieve spatial diversity. Passive time reversal combined with a single-channel adaptive decision feedback equalization (PTR-DFE) is a low-complexity solution to achieve both spatial and temporal focusing. Block-based PTR-DFE (BB-PTR-DFE) extends PTR-DFE to time-varying channels. Multichannel DFE (M-DFE)...
Recently, the minimum mean squared error (MMSE) has been a benchmark of optimization criterion for deep neural network (DNN) based speech enhancement. In this study, a probabilistic learning framework to estimate the DNN parameters for single-channel speech enhancement is proposed. First, the statistical analysis shows that the prediction error vector at the DNN output well follows a unimodal density...
Voice Activity Detection (VAD) plays an important role in current technological applications, such as wireless communications and speech recognition. In this paper, we address the VAD task through machine learning by using a discriminative restricted Boltzmann machine (DRBM). We extend the conventional DRBM to deal with continuous-valued data and employ feature vectors based either on mel-frequency...
The performance of automatic speech recognition systems under noisy environments still leaves room for improvement. Speech enhancement or feature enhancement techniques for increasing noise robustness of these systems usually add components to the recognition system that need careful optimization. In this work, we propose the use of a relatively simple curriculum training strategy called accordion...
Many applications in audio signal processing require a precise identification of time frames where a predefined target source is active. In previous work, Artificial Neural Networks (ANNs) with crosscorrelation features showed a considerable potential in this field. In this paper, the performance of ANN-based target activity detection is analyzed in more detail and compared with a well-performing...
The conventional algorithm of channel estimation based on IEEE802.11ac is Least Square (LS) algorithm, which uses the Very High Throughput-Long Training Field (VHTLTF) of frame header as training sequence. While the conventional algorithm does not take into account the effects of noise and the varying characteristics of channel in time domain. In order to describe the slow change of channel in time...
This paper considers the problem of knowledge-aided space-time adaptive processing (KA-STAP) combined with a parametric technique. Specifically, by modeling the disturbance as a multichannel autoregressive (AR) process, we introduce a stochastic signal model in which the spatial covariance matrix of the disturbance is assumed to be random, with some prior distribution. Incorporating the a priori knowledge...
We proposed and demonstrated an optimized in-band optical signal-to-noise ratio (OSNR) monitoring method for polarization division multiplexed coherent optical coherent optical orthogonal frequency division multiplexing (PDM-CO-OFDM) system with CAZAC equalization and Raman amplification, and it shows satisfactory accuracy in a wide range.
Event-Related Potentials (ERPs) is a regular electrophysiological response which is evoked by outer world events or stimuli from brain, and an important approach to explore the human cognitive function. A variety of methods have been proposed for an attempt to analyze it, with varying degrees of success. In this paper, we have proposed a novel method for learning the ERPs, which bases on the effective...
Since the low SNR environment, generally the modulation recognition rate of signal modulation type is not very high. In this paper, we studied an automatic recognition method of communication signal modulation type in the low SNR. According to analyze the signal entropy as the feature, three characteristics are selected, and the random forest is as the classifier, finally we get a high recognition-rate...
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