Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Physeter catodon localization by sparse coding   This paper presents a spermwhale' localization architecture using jointly a
bag-of-features (BoF) approach and machine learning framework. BoF methods are
known, especially in computer vision, to produce from a collection of local
features a global representation invariant to principal signal transformations.
Our idea is to regress supervisely from these local features two rough
estimates of the distance and azimuth thanks to some datasets where both
acoustic events and ground-truth position are now available. Furthermore, these
estimates can feed a particle filter system in order to obtain a precise
spermwhale' position even in mono-hydrophone configuration. Anti-collision
system and whale watching are considered applications of this work.

15
The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: eess
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph