12,811 research outputs found
Studies in Signal Processing Techniques for Speech Enhancement: A comparative study
Speech enhancement is very essential to suppress the background noise and to increase speech intelligibility and reduce fatigue in hearing. There exist many simple speech enhancement algorithms like spectral subtraction to complex algorithms like Bayesian Magnitude estimators based on Minimum Mean Square Error (MMSE) and its variants. A continuous research is going and new algorithms are emerging to enhance speech signal recorded in the background of environment such as industries, vehicles and aircraft cockpit. In aviation industries speech enhancement plays a vital role to bring crucial information from pilot’s conversation in case of an incident or accident by suppressing engine and other cockpit instrument noises. In this work proposed is a new approach to speech enhancement making use harmonic wavelet transform and Bayesian estimators. The performance indicators, SNR and listening confirms to the fact that newly modified algorithms using harmonic wavelet transform indeed show better results than currently existing methods. Further, the Harmonic Wavelet Transform is computationally efficient and simple to implement due to its inbuilt decimation-interpolation operations compared to those of filter-bank approach to realize sub-bands
Foundational principles for large scale inference: Illustrations through correlation mining
When can reliable inference be drawn in the "Big Data" context? This paper
presents a framework for answering this fundamental question in the context of
correlation mining, with implications for general large scale inference. In
large scale data applications like genomics, connectomics, and eco-informatics
the dataset is often variable-rich but sample-starved: a regime where the
number of acquired samples (statistical replicates) is far fewer than the
number of observed variables (genes, neurons, voxels, or chemical
constituents). Much of recent work has focused on understanding the
computational complexity of proposed methods for "Big Data." Sample complexity
however has received relatively less attention, especially in the setting when
the sample size is fixed, and the dimension grows without bound. To
address this gap, we develop a unified statistical framework that explicitly
quantifies the sample complexity of various inferential tasks. Sampling regimes
can be divided into several categories: 1) the classical asymptotic regime
where the variable dimension is fixed and the sample size goes to infinity; 2)
the mixed asymptotic regime where both variable dimension and sample size go to
infinity at comparable rates; 3) the purely high dimensional asymptotic regime
where the variable dimension goes to infinity and the sample size is fixed.
Each regime has its niche but only the latter regime applies to exa-scale data
dimension. We illustrate this high dimensional framework for the problem of
correlation mining, where it is the matrix of pairwise and partial correlations
among the variables that are of interest. We demonstrate various regimes of
correlation mining based on the unifying perspective of high dimensional
learning rates and sample complexity for different structured covariance models
and different inference tasks
Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression
This paper addresses the problem of localizing audio sources using binaural
measurements. We propose a supervised formulation that simultaneously localizes
multiple sources at different locations. The approach is intrinsically
efficient because, contrary to prior work, it relies neither on source
separation, nor on monaural segregation. The method starts with a training
stage that establishes a locally-linear Gaussian regression model between the
directional coordinates of all the sources and the auditory features extracted
from binaural measurements. While fixed-length wide-spectrum sounds (white
noise) are used for training to reliably estimate the model parameters, we show
that the testing (localization) can be extended to variable-length
sparse-spectrum sounds (such as speech), thus enabling a wide range of
realistic applications. Indeed, we demonstrate that the method can be used for
audio-visual fusion, namely to map speech signals onto images and hence to
spatially align the audio and visual modalities, thus enabling to discriminate
between speaking and non-speaking faces. We release a novel corpus of real-room
recordings that allow quantitative evaluation of the co-localization method in
the presence of one or two sound sources. Experiments demonstrate increased
accuracy and speed relative to several state-of-the-art methods.Comment: 15 pages, 8 figure
On signal-noise decomposition of timeseries using the continuous wavelet transform: Application to sunspot index
We show that the continuous wavelet transform can provide a unique
decomposition of a timeseries in to 'signal-like' and 'noise-like' components:
From the overall wavelet spectrum two mutually independent skeleton spectra
can be extracted, allowing the separate detection and monitoring in even
non-stationary timeseries of the evolution of (a) both stable but also
transient, evolving periodicities, such as the output of low dimensional
dynamical systems and (b) scale-invariant structures, such as discontinuities,
self-similar structures or noise. An indicative application to the
monthly-averaged sunspot index reveals, apart from the well-known 11-year
periodicity, 3 of its harmonics, the 2-year periodicity (quasi-biennial
oscillation, QBO) and several more (some of which detected previously in
various solar, earth-solar connection and climate indices), here proposed being
just harmonics of the QBO, in all supporting the double-cycle solar magnetic
dynamo model (Benevolenskaya, 1998, 2000). The scale maximal spectrum reveals
the presence of 1/f fluctuations with timescales up to 1 year in the sunspot
number, indicating that the solar magnetic configurations involved in the
transient solar activity phenomena with those characteristic timescales are in
a self-organized-critical state (SOC), as previously proposed for the solar
flare occurence (Lu and Hamilton, 1991).Comment: 22 pages, 2 figure
CMB-S4 Science Book, First Edition
This book lays out the scientific goals to be addressed by the
next-generation ground-based cosmic microwave background experiment, CMB-S4,
envisioned to consist of dedicated telescopes at the South Pole, the high
Chilean Atacama plateau and possibly a northern hemisphere site, all equipped
with new superconducting cameras. CMB-S4 will dramatically advance cosmological
studies by crossing critical thresholds in the search for the B-mode
polarization signature of primordial gravitational waves, in the determination
of the number and masses of the neutrinos, in the search for evidence of new
light relics, in constraining the nature of dark energy, and in testing general
relativity on large scales
Application and validation of spatial mixture modelling for the joint detection-estimation of brain activity in fMRI.
International audienceWithin-subject analysis in event-related functional Magnetic Resonance Imaging (fMRI) first relies on (i) a detection step to localize which parts of the brain are activated by a given stimulus type, and second on (ii) an estimation step to recover the temporal dynamics of the brain response. Recently, a Bayesian detection-estimation approach that jointly addresses (i)-(ii) has been proposed in [1]. This work is based on an independent mixture model (IMM) and provides both a spatial activity map and an estimate of brain dynamics. In [2], we accounted for spatial correlation using a spatial mixture model (SMM) based on a binary Markov random field. Here, we assess the SMM robustness and flexibility on simulations which diverge from the priors and the generative BOLD model and further extend comparison between SMM and IMM on real fMRI data, focusing on a region of interest in the auditory cortex
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