%% Papers Published
@article{fds330205,
Author = {Tralie, CJ and Smith, A and Borggren, N and Hineman, J and Bendich, P and Zulch, P and Harer, J},
Title = {Geometric cross-modal comparison of heterogeneous sensor
data},
Journal = {2018 Ieee Aerospace Conference},
Publisher = {IEEE},
Year = {2018},
Month = {March},
url = {http://dx.doi.org/10.1109/aero.2018.8396789},
Abstract = {In this work, we address the problem of cross-modal
comparison of aerial data streams. A variety of simulated
automobile trajectories are sensed using two different
modalities: full-motion video, and radio-frequency (RF)
signals received by detectors at various locations. The
information represented by the two modalities is compared
using self-similarity matrices (SSMs) corresponding to
time-ordered point clouds in feature spaces of each of these
data sources; we note that these feature spaces can be of
entirely different scale and dimensionality. Several metrics
for comparing SSMs are explored, including a cutting-edge
time-warping technique that can simultaneously handle local
time warping and partial matches, while also controlling for
the change in geometry between feature spaces of the two
modalities. We note that this technique is quite general,
and does not depend on the choice of modalities. In this
particular setting, we demonstrate that the cross-modal
distance between SSMs corresponding to the same trajectory
type is smaller than the cross-modal distance between SSMs
corresponding to distinct trajectory types, and we formalize
this observation via precision-recall metrics in
experiments. Finally, we comment on promising implications
of these ideas for future integration into
multiple-hypothesis tracking systems.},
Doi = {10.1109/aero.2018.8396789},
Key = {fds330205}
}
@article{fds330397,
Author = {Tralie, CJ and Perea, JA},
Title = {(Quasi)Periodicity Quantification in Video Data, Using
Topology},
Journal = {Siam Journal on Imaging Sciences},
Volume = {11},
Number = {2},
Pages = {1049-1077},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {2018},
Month = {January},
url = {http://dx.doi.org/10.1137/17m1150736},
Abstract = {This work introduces a novel framework for quantifying the
presence and strength of recurrent dynamics in video data.
Specifically, we provide continuous measures of periodicity
(perfect repetition) and quasiperiodicity (superposition of
periodic modes with non-commensurate periods), in a way
which does not require segmentation, training, object
tracking or 1-dimensional surrogate signals. Our methodology
operates directly on video data. The approach combines ideas
from nonlinear time series analysis (delay embeddings) and
computational topology (persistent homology), by translating
the problem of finding recurrent dynamics in video data,
into the problem of determining the circularity or
toroidality of an associated geometric space. Through
extensive testing, we show the robustness of our scores with
respect to several noise models/levels; we show that our
periodicity score is superior to other methods when compared
to human-generated periodicity rankings; and furthermore, we
show that our quasiperiodicity score clearly indicates the
presence of biphonation in videos of vibrating vocal
folds.},
Doi = {10.1137/17m1150736},
Key = {fds330397}
}
@article{fds330395,
Author = {Tralie, CJ},
Title = {Self-Similarity Based Time Warping},
Year = {2017},
Month = {November},
Abstract = {In this work, we explore the problem of aligning two
time-ordered point clouds which are spatially transformed
and re-parameterized versions of each other. This has a
diverse array of applications such as cross modal time
series synchronization (e.g. MOCAP to video) and alignment
of discretized curves in images. Most other works that
address this problem attempt to jointly uncover a spatial
alignment and correspondences between the two point clouds,
or to derive local invariants to spatial transformations
such as curvature before computing correspondences. By
contrast, we sidestep spatial alignment completely by using
self-similarity matrices (SSMs) as a proxy to the
time-ordered point clouds, since self-similarity matrices
are blind to isometries and respect global geometry. Our
algorithm, dubbed "Isometry Blind Dynamic Time Warping"
(IBDTW), is simple and general, and we show that its
associated dissimilarity measure lower bounds the L1
Gromov-Hausdorff distance between the two point sets when
restricted to warping paths. We also present a local,
partial alignment extension of IBDTW based on the Smith
Waterman algorithm. This eliminates the need for tedious
manual cropping of time series, which is ordinarily
necessary for global alignment algorithms to function
properly.},
Key = {fds330395}
}
@article{fds330396,
Author = {Tralie, C},
Title = {Moebius Beats: The Twisted Spaces of Sliding Window Audio
Novelty Functions with Rhythmic Subdivisions},
Year = {2017},
Month = {November},
Abstract = {In this work, we show that the sliding window embeddings of
certain audio novelty functions (ANFs) representing songs
with rhythmic subdivisions concentrate on the boundary of
non-orientable surfaces such as the Moebius strip. This
insight provides a radically different topological approach
to classifying types of rhythm hierarchies. In particular,
we use tools from topological data analysis (TDA) to detect
subdivisions, and we use thresholds derived from TDA to
build graphs at different scales. The Laplacian eigenvectors
of these graphs contain information which can be used to
estimate tempos of the subdivisions. We show a proof of
concept example on an audio snippet from the MIREX tempo
training dataset, and we hope in future work to find a place
for this in other MIR pipelines.},
Key = {fds330396}
}
@article{fds330206,
Author = {Tralie, CJ},
Title = {Early MFCC And HPCP Fusion for Robust Cover Song
Identification},
Journal = {18th International Society for Music Information Retrieval
(ISMIR)},
Year = {2017},
Month = {October},
Abstract = {While most schemes for automatic cover song identification
have focused on note-based features such as HPCP and chord
profiles, a few recent papers surprisingly showed that local
self-similarities of MFCC-based features also have
classification power for this task. Since MFCC and HPCP
capture complementary information, we design an unsupervised
algorithm that combines normalized, beat-synchronous blocks
of these features using cross-similarity fusion before
attempting to locally align a pair of songs. As an added
bonus, our scheme naturally incorporates structural
information in each song to fill in alignment gaps where
both feature sets fail. We show a striking jump in
performance over MFCC and HPCP alone, achieving a state of
the art mean reciprocal rank of 0.87 on the Covers80
dataset. We also introduce a new medium-sized hand designed
benchmark dataset called "Covers 1000," which consists of
395 cliques of cover songs for a total of 1000 songs, and we
show that our algorithm achieves an MRR of 0.9 on this
dataset for the first correctly identified song in a clique.
We provide the precomputed HPCP and MFCC features, as well
as beat intervals, for all songs in the Covers 1000 dataset
for use in further research.},
Key = {fds330206}
}
@article{fds330207,
Author = {Tralie, C},
Title = {High Dimensional Geometry of Sliding Window Embeddings of
Periodic Videos},
Journal = {Proceedings of the 32st International Symposium on
Computational Geometry (SOCG)},
Year = {2016},
Month = {June},
Abstract = {We explore the high dimensional geometry of sliding windows
of periodic videos. Under a reas- onable model for periodic
videos, we show that the sliding window is necessary to
disambiguate all states within a period, and we show that a
video embedding with a sliding window of an appropriate
dimension lies on a topological loop along a hypertorus.
This hypertorus has an in- dependent ellipse for each
harmonic of the motion. Natural motions with sharp
transitions from foreground to background have many
harmonics and are hence in higher dimensions, so linear
subspace projections such as PCA do not accurately summarize
the geometry of these videos. Noting this, we invoke tools
from topological data analysis and cohomology to
parameterize mo- tions in high dimensions with circular
coordinates after the embeddings. We show applications to
videos in which there is obvious periodic motion and to
videos in which the motion is hidden.},
Key = {fds330207}
}
@article{fds330208,
Author = {Bendich, P and Gasparovic, E and Harer, J and Tralie,
C},
Title = {Geometric Models for Musical Audio Data},
Journal = {Proceedings of the 32st International Symposium on
Computational Geometry (SOCG)},
Year = {2016},
Month = {June},
Key = {fds330208}
}
@article{fds330398,
Author = {Tralie, CJ and Bendich, P},
Title = {Cover Song Identification with Timbral Shape
Sequences},
Journal = {16th International Society for Music Information Retrieval
(ISMIR)},
Pages = {38-44},
Year = {2015},
Month = {October},
Abstract = {We introduce a novel low level feature for identifying cover
songs which quantifies the relative changes in the smoothed
frequency spectrum of a song. Our key insight is that a
sliding window representation of a chunk of audio can be
viewed as a time-ordered point cloud in high dimensions. For
corresponding chunks of audio between different versions of
the same song, these point clouds are approximately rotated,
translated, and scaled copies of each other. If we treat
MFCC embeddings as point clouds and cast the problem as a
relative shape sequence, we are able to correctly identify
42/80 cover songs in the "Covers 80" dataset. By contrast,
all other work to date on cover songs exclusively relies on
matching note sequences from Chroma derived
features.},
Key = {fds330398}
}
@article{fds330209,
Author = {Deyle, T and Tralie, CJ and Reynolds, MS and Kemp,
CC},
Title = {In-hand radio frequency identification (RFID) for robotic
manipulation},
Journal = {2013 Ieee International Conference on Robotics and
Automation},
Pages = {1234-1241},
Publisher = {IEEE},
Year = {2013},
Month = {May},
url = {http://dx.doi.org/10.1109/icra.2013.6630729},
Abstract = {We present a unique multi-antenna RFID reader (a sensor)
embedded in a robot's manipulator that is designed to
operate with ordinary UHF RFID tags in a short-range,
near-field electromagnetic regime. Using specially designed
near-field antennas enables our sensor to obtain spatial
information from tags at ranges of less than 1 meter. In
this work, we characterize the near-field sensor's ability
to detect tagged objects in the robots manipulator, present
robot behaviors to determine the identity of a grasped
object, and investigate how additional RF signal properties
can be used for 'pre-touch' capabilities such as servoing to
grasp an object. The future combination of long-range
(far-field) and short-range (near-field) UHF RFID sensing
has the potential to enable roboticists to jump-start
applications by obviating or supplementing
false-positive-prone visual object recognition. These
techniques may be especially useful in the healthcare and
service sectors, where mis-identification of an object (for
example, a medication bottle) could have catastrophic
consequences. © 2013 IEEE.},
Doi = {10.1109/icra.2013.6630729},
Key = {fds330209}
}
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Mathematics Department