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 Programme
Overview
Detailed
Programme
| Signal
Processing and Pattern Recognition (SPPR) |
CHAIR: JORGE HENRIQUES
Time: Wednesday,
March 23rd, 16h00-17h45
| SPPR-1 |
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| Title: |
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Associative memories and diagnostic
classification of EMG signals |
| Author(s): |
C. Shirota,
M. Y. Barretto,
C. Itiki |
| Abstract: |
In this work, associative memories are used
for diagnostic classification of needle EMG signals. Vectors
containing 44 autoregressive coefficients represent each signal
and are presented as stimuli to associative memories. As the
number of training stimuli increases, the method recursively
updates associative memories. The obtained classification
results are equivalent to the ones provided by the traditional
Fisher's discriminant, indicating the feasibility of the proposed
method. |
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| SPPR-2 |
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| Title: |
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Discretization of Series of Communication
Signals in Noisy Environment by Reinforcement Learning |
| Author(s): |
Katsunari Shibata |
| Abstract: |
Thinking about the “Symbol Grounding Problem”
and the brain structure of the living things, the authors
believe that it is the best solution for generating communication
in robot-like systems to use a neural network that is trained
based on reinforcement learning. As the first step of the
research of symbol emergence using neural network, it was
examined that parallel analog communication signals are binarized
in some degree by noise addition in reinforcement learning-based
communication acquisition. In this paper, it was examined
that a series of analog communication signals are binarized
by noise addition using recurrent neural networks. Furthermore,
when the noise ratio became larger, the degree of the binarization
became larger. |
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| SPPR-3 |
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| Title: |
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The Research of Speaker-Independent
Continuous Mandarin Chinese Digits Speech-Recognition Based
on the Dynamic Search Method of High-Dimension Space Vertex
Cover |
| Author(s): |
Wenming Cao,
Xiaoxia Pan,
Shoujue Wang |
| Abstract: |
In this paper, we present a novel algorithm
of speaker-independent continuous Mandarin Chinese digits
speech-recognition, which is based on the dynamic searching
method of high-dimension space vertex cover. It doesn’t
need endpoint detecting and segmenting. We construct a cover
set for every class of digits firstly, and then we put every
numeric string into these cover sets, and the numeric string
is recognized directly by the dynamic search method. Finally,
there are 32 people in experiment, 16 female and 16 male,
and 256 digits all together. All these digits are not learned.
The correct recognition result is 218, and error recognition
result is 26. Correct recognition rate is 85.19%. |
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| SPPR-4 |
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| Title: |
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A Connectionist Model of Finding
Partial Groups in Music Recordings with Application to Music
Transcription |
| Author(s): |
Matija Marolt |
| Abstract: |
In this paper, we present a technique for tracking
groups of partials in musical signals, based on networks of
adaptive oscillators. We show how synchronization of adaptive
oscillators can be utilized to detect periodic patterns in
outputs of a human auditory model and thus track stable frequency
components (partials) in musical signals. We present the integration
of the partial tracking model into a connectionist system
for transcription of polyphonic piano music. We provide a
short overview of our transcription system and present its
performance on transcriptions of several real piano recordings. |
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| SPPR-5 |
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| Title: |
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Adaptive ICA Algorithm Based on
Asymmetric Generalized Gaussian Density Model |
| Author(s): |
Fasong Wang,
Hongwei Li |
| Abstract: |
A novel Independent Component Analysis(ICA)
algorithm is achieved, which enable to separate mixtures of
symmetric and asymmetric sources with self adaptive nonlinear
score functions. It is derived by using the parameterized
asymmetric generalized Gaussian density(AGGD) model. Compared
with conventional ICA algorithm, the proposed AGGD-ICA method
can separate a wide range of source signals including skewed
source signals. Simulations confirm the effectiveness and
performance of the approach. |
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