7 research outputs found
Single-channel blind source separation of underwater acoustic signals using improved NMF and FastICA
When automatic monitoring buoys receive mixed acoustic signals from multiple underwater acoustic targets, the statistical blind source separation (BSS) task is used to separate the signals and identify vessel features, which is overly complex and needs improvement, especially noting that noise cancellation and stealth technologies are advancing rapidly. To fill this gap in capability, an improved non-negative matrix factorization (NMF) based BSS algorithm is built on a FastICA machine learning backbone. With this tool, the spatial and spectral correlation of underwater acoustic signals is introduced into the NMF algorithm improved by to resolve the non-convex and feature correlation problems commonly encountered by contemporary NMF algorithms. Moreover, the improved modulation feature adaptability of the NMF increases the local expressivity and independence of the decomposed base matrix, which is proven to meet the requirements of FastICA and used to improve the BSS effect of the FastICA. Simulated and empirical results show that compared with state-of-the-art FastICA and NMF based BSS algorithms, our novel approach obtains better signal-to-noise reduction and separation accuracy while maintaining superior target signal recognition features
Sparse Approximation and Dictionary Learning with Applications to Audio Signals
PhDOver-complete transforms have recently become the focus of a wide wealth of research in
signal processing, machine learning, statistics and related fields. Their great modelling
flexibility allows to find sparse representations and approximations of data that in turn
prove to be very efficient in a wide range of applications. Sparse models express signals as
linear combinations of a few basis functions called atoms taken from a so-called dictionary.
Finding the optimal dictionary from a set of training signals of a given class is the objective
of dictionary learning and the main focus of this thesis. The experimental evidence
presented here focuses on the processing of audio signals, and the role of sparse algorithms
in audio applications is accordingly highlighted.
The first main contribution of this thesis is the development of a pitch-synchronous
transform where the frame-by-frame analysis of audio data is adapted so that each frame
analysing periodic signals contains an integer number of periods. This algorithm presents
a technique for adapting transform parameters to the audio signal to be analysed, it
is shown to improve the sparsity of the representation if compared to a non pitchsynchronous
approach and further evaluated in the context of source separation by binary
masking.
A second main contribution is the development of a novel model and relative algorithm
for dictionary learning of convolved signals, where the observed variables are sparsely approximated
by the atoms contained in a convolved dictionary. An algorithm is devised to
learn the impulse response applied to the dictionary and experimental results on synthetic
data show the superior approximation performance of the proposed method compared to
a state-of-the-art dictionary learning algorithm.
Finally, a third main contribution is the development of methods for learning dictionaries
that are both well adapted to a training set of data and mutually incoherent. Two
novel algorithms namely the incoherent k-svd and the iterative projections and rotations
(ipr) algorithm are introduced and compared to different techniques published in the
literature in a sparse approximation context. The ipr algorithm in particular is shown
to outperform the benchmark techniques in learning very incoherent dictionaries while
maintaining a good signal-to-noise ratio of the representation
Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics
This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p
PERICLES Deliverable 4.3:Content Semantics and Use Context Analysis Techniques
The current deliverable summarises the work conducted within task T4.3 of WP4, focusing on the extraction and the subsequent analysis of semantic information from digital content, which is imperative for its preservability. More specifically, the deliverable defines content semantic information from a visual and textual perspective, explains how this information can be exploited in long-term digital preservation and proposes novel approaches for extracting this information in a scalable manner. Additionally, the deliverable discusses novel techniques for retrieving and analysing the context of use of digital objects. Although this topic has not been extensively studied by existing literature, we believe use context is vital in augmenting the semantic information and maintaining the usability and preservability of the digital objects, as well as their ability to be accurately interpreted as initially intended.PERICLE
Abstracts on Radio Direction Finding (1899 - 1995)
The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography).
Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM.
The contents of these files are:
1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format];
2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format];
3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion