461 research outputs found
GASP : Geometric Association with Surface Patches
A fundamental challenge to sensory processing tasks in perception and
robotics is the problem of obtaining data associations across views. We present
a robust solution for ascertaining potentially dense surface patch (superpixel)
associations, requiring just range information. Our approach involves
decomposition of a view into regularized surface patches. We represent them as
sequences expressing geometry invariantly over their superpixel neighborhoods,
as uniquely consistent partial orderings. We match these representations
through an optimal sequence comparison metric based on the Damerau-Levenshtein
distance - enabling robust association with quadratic complexity (in contrast
to hitherto employed joint matching formulations which are NP-complete). The
approach is able to perform under wide baselines, heavy rotations, partial
overlaps, significant occlusions and sensor noise.
The technique does not require any priors -- motion or otherwise, and does
not make restrictive assumptions on scene structure and sensor movement. It
does not require appearance -- is hence more widely applicable than appearance
reliant methods, and invulnerable to related ambiguities such as textureless or
aliased content. We present promising qualitative and quantitative results
under diverse settings, along with comparatives with popular approaches based
on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201
Geometric algorithms for transposition invariant content-based music retrieval
We are grateful to Mika Turkia for the implementations.Peer reviewe
Pattern vectors from algebraic graph theory
Graphstructures have proven computationally cumbersome for pattern analysis. The reason for this is that, before graphs can be converted to pattern vectors, correspondences must be established between the nodes of structures which are potentially of different size. To overcome this problem, in this paper, we turn to the spectral decomposition of the Laplacian matrix. We show how the elements of the spectral matrix for the Laplacian can be used to construct symmetric polynomials that are permutation invariants. The coefficients of these polynomials can be used as graph features which can be encoded in a vectorial manner. We extend this representation to graphs in which there are unary attributes on the nodes and binary attributes on the edges by using the spectral decomposition of a Hermitian property matrix that can be viewed as a complex analogue of the Laplacian. To embed the graphs in a pattern space, we explore whether the vectors of invariants can be embedded in a low- dimensional space using a number of alternative strategies, including principal components analysis ( PCA), multidimensional scaling ( MDS), and locality preserving projection ( LPP). Experimentally, we demonstrate that the embeddings result in well- defined graph clusters. Our experiments with the spectral representation involve both synthetic and real- world data. The experiments with synthetic data demonstrate that the distances between spectral feature vectors can be used to discriminate between graphs on the basis of their structure. The real- world experiments show that the method can be used to locate clusters of graphs
Machine Annotation of Traditional Irish Dance Music
The work presented in this thesis is validated in experiments using 130 realworld field recordings of traditional music from sessions, classes, concerts and commercial recordings. Test audio includes solo and ensemble playing on a variety of instruments recorded in real-world settings such as noisy public sessions. Results are reported using standard measures from the field of information retrieval (IR) including accuracy, error, precision and recall and the system is compared to alternative approaches for CBMIR common in the literature
A Comprehensive Trainable Error Model for Sung Music Queries
We propose a model for errors in sung queries, a variant of the hidden Markov
model (HMM). This is a solution to the problem of identifying the degree of
similarity between a (typically error-laden) sung query and a potential target
in a database of musical works, an important problem in the field of music
information retrieval. Similarity metrics are a critical component of
query-by-humming (QBH) applications which search audio and multimedia databases
for strong matches to oral queries. Our model comprehensively expresses the
types of error or variation between target and query: cumulative and
non-cumulative local errors, transposition, tempo and tempo changes,
insertions, deletions and modulation. The model is not only expressive, but
automatically trainable, or able to learn and generalize from query examples.
We present results of simulations, designed to assess the discriminatory
potential of the model, and tests with real sung queries, to demonstrate
relevance to real-world applications
The distribution of cycles in breakpoint graphs of signed permutations
Breakpoint graphs are ubiquitous structures in the field of genome
rearrangements. Their cycle decomposition has proved useful in computing and
bounding many measures of (dis)similarity between genomes, and studying the
distribution of those cycles is therefore critical to gaining insight on the
distributions of the genomic distances that rely on it. We extend here the work
initiated by Doignon and Labarre, who enumerated unsigned permutations whose
breakpoint graph contains cycles, to signed permutations, and prove
explicit formulas for computing the expected value and the variance of the
corresponding distributions, both in the unsigned case and in the signed case.
We also compare these distributions to those of several well-studied distances,
emphasising the cases where approximations obtained in this way stand out.
Finally, we show how our results can be used to derive simpler proofs of other
previously known results
A Geometric Approach to Pattern Matching in Polyphonic Music
The music pattern matching problem involves finding matches of a small fragment of music called the "pattern" into a larger body of music called the "score". We represent music as a series of horizontal line segments in the plane, and reformulate the problem as finding the best translation of a small set of horizontal line segments into a larger set of horizontal line segments. We present an efficient algorithm that can handle general weight models that measure the musical quality of a match of the pattern into the score, allowing for approximate pattern matching.
We give an algorithm with running time O(nm(d + log m)), where n is the size of the score, m is the size of the pattern, and d is the size of the discrete set of musical pitches used. Our algorithm compares favourably to previous approaches to the music pattern matching problem. We also demonstrate that this geometric formulation of the music pattern matching problem is unlikely to have a significantly faster algorithm since it is at least as hard as 3SUM, a basic problem that is conjectured to have no subquadratic algorithm. Lastly, we present experiments to show how our algorithm can find musically sensible variations of a theme, as well as polyphonic musical patterns in a polyphonic score
Discovering distorted repeating patterns in polyphonic music through longest increasing subsequences
We study the problem of identifying repetitions under transposition and time-warp invariances in polyphonic symbolic music. Using a novel onset-time-pair representation, we reduce the repeating pattern discovery problem to instances of the classical problem of finding the longest increasing subsequences. The resulting algorithm works in O(n(2) log n) time where n is the number of notes in a musical work. We also study windowed variants of the problem where onset-time differences between notes are restricted, and show that they can also be solved in O(n(2) log n) time using the algorithm.Peer reviewe
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