https://github.com/cran/dtw
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dtw.bib
@ARTICLE{Tormene2008,
author = {Paolo Tormene and Toni Giorgino and Silvana Quaglini and Mario Stefanelli},
title = {Matching Incomplete Time Series with Dynamic Time Warping: An Algorithm
and an Application to Post-stroke Rehabilitation.},
journal = {Artificial Intelligence in Medicine},
year = {2009},
volume = {45},
pages = {11--34},
number = {1},
month = {Jan},
abstract = {OBJECTIVE: The purpose of this study was to assess the performance
of a real-time ("open-end") version of the dynamic time warping (DTW)
algorithm for the recognition of motor exercises. Given a possibly
incomplete input stream of data and a reference time series, the
open-end DTW algorithm computes both the size of the prefix of reference
which is best matched by the input, and the dissimilarity between
the matched portions. The algorithm was used to provide real-time
feedback to neurological patients undergoing motor rehabilitation.
METHODS AND MATERIALS: We acquired a dataset of multivariate time
series from a sensorized long-sleeve shirt which contains 29 strain
sensors distributed on the upper limb. Seven typical rehabilitation
exercises were recorded in several variations, both correctly and
incorrectly executed, and at various speeds, totaling a data set
of 840 time series. Nearest-neighbour classifiers were built according
to the outputs of open-end DTW alignments and their global counterparts
on exercise pairs. The classifiers were also tested on well-known
public datasets from heterogeneous domains. RESULTS: Nonparametric
tests show that (1) on full time series the two algorithms achieve
the same classification accuracy (p-value =0.32); (2) on partial
time series, classifiers based on open-end DTW have a far higher
accuracy (kappa=0.898 versus kappa=0.447;p<10(-5)); and (3) the prediction
of the matched fraction follows closely the ground truth (root mean
square <10\%). The results hold for the motor rehabilitation and
the other datasets tested, as well. CONCLUSIONS: The open-end variant
of the DTW algorithm is suitable for the classification of truncated
quantitative time series, even in the presence of noise. Early recognition
and accurate class prediction can be achieved, provided that enough
variance is available over the time span of the reference. Therefore,
the proposed technique expands the use of DTW to a wider range of
applications, such as real-time biofeedback systems.},
doi = {10.1016/j.artmed.2008.11.007},
institution = {Laboratory for Biomedical Informatics, Computer Engineering and Systems
Science, Università di Pavia, Via Ferrata 1, Pavia, Italy.},
owner = {toni},
pii = {S0933-3657(08)00177-2},
pmid = {19111449},
timestamp = {2009.03.08},
url = {http://dx.doi.org/10.1016/j.artmed.2008.11.007}
}
@ARTICLE{Sakoe1978,
title = {Dynamic programming algorithm optimization for spoken word recognition},
author = {Sakoe, H. and Chiba, S.},
journal = {Acoustics, Speech, and Signal Processing [see also {IEEE} Transactions on Signal Processing], {IEEE} Transactions on},
year = {1978},
volume = {26},
number = {1},
pages = {43--49},
month = feb,
abstract = {This paper reports on an optimum dynamic progxamming (DP) based time-
normalization algorithm for spoken word recognition. First, a
general principle of time-normalization is given using time-
warping function. Then, two time-normalized distance
definitions, called symmetric and asymmetric forms, are derived
from the principle. These two forms are compared with each
other through theoretical discussions and experimental studies.
The symmetric form algorithm superiority is established. A new
technique, called slope constraint, is successfully introduced,
in which the warping function slope is restricted so as to
improve discrimination between words in different categories.
The effective slope constraint characteristic is qualitatively
analyzed, and the optimum slope constraint condition is
determined through experiments. The optimized algorithm is then
extensively subjected to experimental comparison with various
DP-algorithms, previously applied to spoken word recognition by
different research groups. The experiment shows that the
present algorithm gives no more than about two-thirds errors,
even compared to the best conventional algorithm.},
ISSN = {0096-3518},
}
@ARTICLE{Itakura1975,
title = {Minimum prediction residual principle applied to speech recognition},
author = {Itakura, F.},
journal = {Acoustics, Speech, and Signal Processing [see also {IEEE} Transactions on Signal Processing], {IEEE} Transactions on},
year = {1975},
volume = {23},
number = {1},
pages = {67--72},
month = feb,
abstract = {A computer system is described in which isolated words, spoken by a
designated talker, are recognized through calculation of a
minimum prediction residual. A reference pattern for each word
to be recognized is stored as a time pattern of linear
prediction coefficients (LPC). The total log prediction
residual of an input signal is minimized by optimally
registering the reference LPC onto the input autocorrelation
coefficients using the dynamic programming algorithm (DP). The
input signal is recognized as the reference word which produces
the minimum prediction residual. A sequential decision
procedure is used to reduce the amount of computation in DP. A
frequency normalization with respect to the long-time spectral
distribution is used to reduce effects of variations in the
frequency response of telephone connections. The system has
been implemented on a DDP-516 computer for the 200-word
recognition experiment. The recognition rate for a designated
male talker is 97.3 percent for telephone input, and the
recognition time is about 22 times real time.},
ISSN = {0096-3518},
}
@article{Velichko,
author = {V. M. Velichko and N. G. Zagoruyko},
title = {Automatic Recognition of 200 Words},
journal = {International Journal of Man-Machine Studies},
volume = {2},
issue = {3},
year = {1970},
pages = {223-234},
bibsource = {http://www.interaction-design.org/references/},
}
@ARTICLE{White1976,
title = {Speech recognition experiments with linear predication, bandpass filtering, and dynamic programming},
author = {White, G. and Neely, R.},
journal = {Acoustics, Speech, and Signal Processing [see also {IEEE} Transactions on Signal Processing], {IEEE} Transactions on},
year = {1976},
volume = {24},
number = {2},
pages = {183--188},
month = apr,
abstract = {Automatic speech recognition experiments are described in which
several popular preprocessing and classification strategies are
compared. Preprocessing is done either by linear predictive
analysis or by bandpass filtering. The two approaches are shown
to produce similar recognition scores. The classifier uses
either linear time stretching or dynamic programming to achieve
time alignment. It is shown that dynamic programming is of
major importance for recognition of polysyllabic words. The
speech is compressed into a quasi-phoneme character string or
preserved uncompressed. Best results are obtained with
uncompressed data, using nonlinear time registration for
multisyllabic words.},
ISSN = {0096-3518},
}
@ARTICLE{Myers1980,
title = {Performance tradeoffs in dynamic time warping algorithms for isolated word recognition},
author = {Myers, C. and Rabiner, L. and Rosenberg, A.},
journal = {Acoustics, Speech, and Signal Processing [see also {IEEE} Transactions on Signal Processing], {IEEE} Transactions on},
year = {1980},
volume = {28},
number = {6},
pages = {623--635},
month = dec,
abstract = {The technique of dynamic programming for the time registration of a
reference and a test pattern has found widespread use in the
area of isolated word recognition. Recently, a number of
variations on the basic time warping algorithm have been
proposed by Sakoe and Chiba, and Rabiner, Rosenberg, and
Levinson. These algorithms all assume that the test input is
the time pattern of a feature vector from an isolated word
whose endpoints are known (at least approximately). The major
differences in the methods are the global path constraints
(i.e., the region of possible warping paths), the local
continuity constraints on the path, and the distance weighting
and normalization used to give the overall minimum distance.
The purpose of this investigation is to study the effects of
such variations on the performance of different dynamic time
warping algorithms for a realistic speech database. The
performance measures that were used include: speed of
operation, memory requirements, and recognition accuracy. The
results show that both axis orientation and relative length of
the reference and the test patterns are important factors in
recognition accuracy. Our results suggest a new approach to
dynamic time warping for isolated words in which both the
reference and test patterns are linearly warped to a fixed
length, and then a simplified dynamic time warping algorithm is
used to handle the nonlinear component of the time alignment.
Results with this new algorithm show performance comparable to
or better than that of all other dynamic time warping
algorithms that were studied. },
ISSN = {0096-3518},
}
@MASTERSTHESIS{MyersMS,
author = {Myers, C. S.},
title = {A Comparative Study Of Several Dynamic Time Warping
Algorithms For Speech Recognition},
school = {MIT},
year = {1980},
month = {Jun 20},
owner = {toni},
timestamp = {2008.04.17},
url =
{http://dspace.mit.edu/bitstream/1721.1/27909/1/07888629.pdf}
}