🏛️國立陽明交通大學機構典藏

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HIERARCHICAL THEME AND TOPIC MODEL FOR SUMMARIZATION
(2013-01-01)
Chien, Jen-Tzung
;
Chang, Ying-Lan
;
電機資訊學士班
;
Undergraduate Honors Program of Electrical Engineering and Computer Science
This paper presents a hierarchical summarization model to extract representative sentences from a set of documents. In this study, we select the thematic sentences and identify the topical words based on a hierarchical theme and topic model (H2TM). The latent themes and topics are inferred from document collection. A tree stick-breaking process is proposed to draw the theme proportions for representation of sentences. The structural learning is performed without fixing the number of themes and topics. This H2TM is delicate and flexible to represent words and sentences from heterogeneous documents. Thematic sentences are effectively extracted for document summarization. In the experiments, the proposed H2TM outperforms the other methods in terms of precision, recall and F-measure.
Bayesian Sparse Topic Model
(2014-03-01)
Chien, Jen-Tzung
;
Chang, Ying-Lan
;
電機資訊學士班
;
Undergraduate Honors Program of Electrical Engineering and Computer Science
This paper presents a new Bayesian sparse learning approach to select salient lexical features for sparse topic modeling. The Bayesian learning based on latent Dirichlet allocation (LDA) is performed by incorporating the spike-and-slab priors. According to this sparse LDA (sLDA), the spike distribution is used to select salient words while the slab distribution is applied to establish the latent topic model based on those selected relevant words. The variational inference procedure is developed to estimate prior parameters for sLDA. In the experiments on document modeling using LDA and sLDA, we find that the proposed sLDA does not only reduce the model perplexity but also reduce the memory and computation costs. Bayesian feature selection method does effectively identify relevant topic words for building sparse topic model.
Bayesian group sparse learning for music source separation
(2013)
Chien, Jen-Tzung
;
Hsieh, Hsin-Lung
;
電機資訊學士班
;
Undergraduate Honors Program of Electrical Engineering and Computer Science
Nonnegative matrix factorization (NMF) is developed for parts-based representation of nonnegative signals with the sparseness constraint. The signals are adequately represented by a set of basis vectors and the corresponding weight parameters. NMF has been successfully applied for blind source separation and many other signal processing systems. Typically, controlling the degree of sparseness and characterizing the uncertainty of model parameters are two critical issues for model regularization using NMF. This paper presents the Bayesian group sparse learning for NMF and applies it for single-channel music source separation. This method reconstructs the rhythmic or repetitive signal from a common subspace spanned by the shared bases for the whole signal and simultaneously decodes the harmonic or residual signal from an individual subspace consisting of separate bases for different signal segments. A Laplacian scale mixture distribution is introduced for sparse coding given a sparseness control parameter. The relevance of basis vectors for reconstructing two groups of music signals is automatically determined. A Markov chain Monte Carlo procedure is presented to infer two sets of model parameters and hyperparameters through a sampling procedure based on the conditional posterior distributions. Experiments on separating single-channel audio signals into rhythmic and harmonic source signals show that the proposed method outperforms baseline NMF, Bayesian NMF, and other group-based NMF in terms of signal-to-interference ratio.
Nonstationary Source Separation Using Sequential and Variational Bayesian Learning
(2013-05-01)
Chien, Jen-Tzung
;
Hsieh, Hsin-Lung
;
電機資訊學士班
;
Undergraduate Honors Program of Electrical Engineering and Computer Science
Independent component analysis (ICA) is a popular approach for blind source separation where the mixing process is assumed to be unchanged with a fixed set of stationary source signals. However, the mixing system and source signals are non-stationary in real-world applications, e. g., the source signals may abruptly appear or disappear, the sources may be replaced by new ones or even moving by time. This paper presents an online learning algorithm for the Gaussian process (GP) and establishes a separation procedure in the presence of nonstationary and temporally correlated mixing coefficients and source signals. In this procedure, we capture the evolved statistics from sequential signals according to online Bayesian learning. The activity of nonstationary sources is reflected by an automatic relevance determination, which is incrementally estimated at each frame and continuously propagated to the next frame. We employ the GP to characterize the temporal structures of time-varying mixing coefficients and source signals. A variational Bayesian inference is developed to approximate the true posterior for estimating the nonstationary ICA parameters and for characterizing the activity of latent sources. The differences between this ICA method and the sequential Monte Carlo ICA are illustrated. In the experiments, the proposed algorithm outperforms the other ICA methods for the separation of audio signals in the presence of different nonstationary scenarios.
Large-Vocabulary Continuous Speech Recognition Systems
(2012-11-01)
Saon, George
;
Chien, Jen-Tzung
;
電機資訊學士班
;
Undergraduate Honors Program of Electrical Engineering and Computer Science

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🏛️國立陽明交通大學機構典藏

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