Abstract book (pdf) - ICPR 2010

ness and quality of the tracking compared to the use of the RGB space. This is asserted by the experiments performed on
several sequences showing vehicles and pedestrians in various contexts.
13:30-16:30, Paper ThBCT9.44
Signal-To-Signal Ratio Independent Speaker Identifi Cation for Co-Channel Speech Signals
Saeidi, Rahim, Univ. of Eastern Finland
Mowlaee, Pejman, Aalborg Univ.
Kinnunen, Tomi, Univ. of Eastern Finland
Tan, Zheng-Hua, Aalborg Univ.
Christensen, Mads Græsbøll, Aalborg Univ.
Jensen, Søren Holdt, Aalborg Univ.
Fränti, Pasi, Univ. of Eastern Finland
In this paper, we consider speaker identification for the co-channel scenario in which speech mixture from speakers is
recorded by one microphone only. The goal is to identify both of the speakers from their mixed signal. High recognition
accuracies have already been reported when an accurately estimated signal-to-signal ratio (SSR) is available. In this paper,
we approach the problem without estimating SSR. We show that a simple method based on fusion of adapted Gaussian
mixture models and Kullback-Leibler divergence calculated between models, achieves an accuracy of 97% and 93% when
the two target speakers enlisted as three and two most probable speakers, respectively.
13:30-16:30, Paper ThBCT9.45
Selection of Training Instances for Music Genre Classification
Lopes, Miguel, INESC Porto
Gouyon, Fabien, INESC Porto
Koerich, Alessandro, PUCPR
Oliveira, Luiz, Federal Univ. of Parana
In this paper we present a method for the selection of training instances based on the classification accuracy of a SVM
classifier. The instances consist of feature vectors representing short-term, low-level characteristics of music audio signals.
The objective is to build, from only a portion of the training data, a music genre classifier with at least similar performance
as when the whole data is used. The particularity of our approach lies in a pre-classification of instances prior to the main
classifier training: i.e. we select from the training data those instances that show better discrimination with respect to class
memberships. On a very challenging dataset of 900 music pieces divided among 10 music genres, the instance selection
method slightly improves the music genre classification in 2.4 percentage points. On the other hand, the resulting classification
model is significantly reduced, permitting much faster classification over test data.
13:30-16:30, Paper ThBCT9.46
Semi-Blind Speech-Music Separation using Sparsity and Continuity Priors
Erdogan, Hakan, Sabanci Univ.
M. Grais, Emad, Sabanci Univ.
In this paper we propose an approach for the problem of single channel source separation of speech and music signals.
Our approach is based on representing each source’s power spectral density using dictionaries and nonlinearly projecting
the mixture signal spectrum onto the combined span of the dictionary entries. We encourage sparsity and continuity of the
dictionary coefficients using penalty terms (or log-priors) in an optimization framework. We propose to use a novel coordinate
descent technique for optimization, which nicely handles nonnegativity constraints and nonquadratic penalty terms.
We use an adaptive Wiener filter, and spectral subtraction to reconstruct both of the sources from the mixture data after
corresponding power spectral densities (PSDs) are estimated for each source. Using conventional metrics, we measure
the performance of the system on simulated mixtures of single person speech and piano music sources. The results indicate
that the proposed method is a promising technique for low speech-to-music ratio conditions and that sparsity and continuity
priors help improve the performance of the proposed system.
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