• Journal of Information Processing Systems
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• v.14 no.1
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• pp.218-227
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• 2018

Vocal effort detection is important for both robust speech recognition and speaker recognition. In this paper, the spectral information entropy feature which contains more salient information regarding the vocal effort level is firstly proposed. Then, the model fusion method based on complementary model is presented to recognize vocal effort level. Experiments are conducted on isolated words test set, and the results show the spectral information entropy has the best performance among the three kinds of features. Meanwhile, the recognition accuracy of all vocal effort levels reaches 81.6%. Thus, potential of the proposed method is demonstrated.

• Korean Journal of Remote Sensing
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• v.22 no.4
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• pp.295-304
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• 2006

Image fusion is defined as making new image by merging two or more images using special algorithms. In case of remote sensing, it means fusing multispectral low-resolution remotely sensed image with panchromatic high-resolution image. Generally, hyperspectral image fusion is accomplished by utilizing fusion technique of multispectral imagery or spectral unmixing model. But, the former may distort spectral information and the latter needs endmember data or additional data, and has a problem with not preserving spatial information well. This study proposes a new algorithm based on two stage spectral unmixing model for preserving hyperspectral image's spectral information. The proposed fusion technique is implemented and tested using Hyperion and ALI images. it is shown to work well on maintaining more spatial/spectral information than the PCA/GS fusion algorithms.

• Current Optics and Photonics
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• v.5 no.4
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• pp.431-443
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• 2021

Sparse unmixing has been proven to be an effective method for hyperspectral unmixing. Hyperspectral images contain rich spectral and spatial information. The means to make full use of spectral information, spatial information, and enhanced sparsity constraints are the main research directions to improve the accuracy of sparse unmixing. However, many algorithms only focus on one or two of these factors, because it is difficult to construct an unmixing model that considers all three factors. To address this issue, a novel algorithm called multiview-based spectral weighted and low-rank row-sparsity unmixing is proposed. A multiview data set is generated through spectral partitioning, and then spectral weighting is imposed on it to exploit the abundant spectral information. The row-sparsity approach, which controls the sparsity by the l_2,0 norm, outperforms the single-sparsity approach in many scenarios. Many algorithms use convex relaxation methods to solve the l_2,0 norm to avoid the NP-hard problem, but this will reduce sparsity and unmixing accuracy. In this paper, a row-hard-threshold function is introduced to solve the l_2,0 norm directly, which guarantees the sparsity of the results. The high spatial correlation of hyperspectral images is associated with low column rank; therefore, the low-rank constraint is adopted to utilize spatial information. Experiments with simulated and real data prove that the proposed algorithm can obtain better unmixing results.

• Current Optics and Photonics
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• v.7 no.5
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• pp.518-528
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• 2023

As an effective means of remotely detecting the spectral information of the object, the spectral calibration for the Savart polarization interference imaging spectrometer (SPIIS) is a basis and prerequisite of information quantification, and its experimental calibration scheme is firstly proposed in this paper. In order to evaluate the accuracy of the spectral information acquisition, the linear interpolation, cubic spline interpolation, and piecewise cubic interpolation algorithms are adopted, and the precision of the quadratic polynomial fitting is the highest, whose fitting error is better than 5.8642 nm in the wavelength range of [500 nm, 820 nm]. Besides, the inversed value of the spectral resolution for the monochromatic light is greater than the theoretical value, and the deviation between them becomes larger with the wavelength increasing, which is mainly caused by the structural design of the SPIIS, together with the rationality of the spectral restoration algorithm and the selection of the maximum optical path difference (OPD). This work demonstrates that the SPIIS has achieved high performance assuring the feasibility of its practical use in various fields.

• Proceedings of the KSPS conference
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• 2005.04a
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• pp.53-56
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• 2005

In this paper, we propose a transformation based robust adaptation technique that uses the maximum mutual information(MMI) estimation for the objective function and the linear spectral transformation(LST) for adaptation. LST is an adaptation method that deals with environmental noises in the linear spectral domain, so that a small number of parameters can be used for fast adaptation. The proposed technique is called MMI-LST, and evaluated on TIMIT and FFMTIMIT corpora to show that it is advantageous when only a small amount of adaptation speech is used.

• Journal of Multimedia Information System
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• v.5 no.1
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• pp.63-66
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• 2018

In this work, the smoothing and the interpolation basis splines are analyzed. As well as the possibility of using the spectral properties of the basis splines for digital signal processing are shown. This takes into account the fact that basic splines represent finite, piecewise polynomial functions defined on compact media.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2415-2418
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• 2003

In this paper, we present a new filter to adjust formant information. Spectral envelope in speech analysis shows information about characteristics of speech and formant information determines speech timbre. So, if formant position is adjusted, we can verify adjusted speech timbre. A presented filter is to adjust this formant. This filter is composed of triangular filters. Using this filter we could locate the formant frequency at target position.

• Journal of Communications and Networks
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• v.11 no.1
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• pp.1-10
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• 2009

In this paper, we propose a chip-level detection and a spectral-slice scheme for the tunable-transmitter/fixed-receiver (TTFR)-based asynchronous spectral phase-encoded optical codedivision multiple-access (CDMA) system combined with timeencoding. The chip-level detection can enhance the tolerance of multiple access interference (MAI) because the channel collision does not occur as long as there is at least one weighted position without MAI. Moreover, the spectral-slice scheme can reduce the interference probability because the MAI with the different frequency has no adverse effects on the channel collision rate. As a result, these techniques mitigate channel collisions. We analyze the channel collision rate theoretically, and show that the proposed system can achieve a lower channel collision rate in comparison to both conventional systems with and without the time-encoding method.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.251-256
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• 2004

The image classification is one of the most important studies in the remote sensing. In general, the MLC(Maximum Likelihood Classification) classification that in consideration of distribution of training information is the most effective way but it produces a bad result when we apply it to actual hyperspectral image with the same classification technique. The purpose of this research is to reveal that which one is the most effective and suitable way of the classification algorithms iii the hyperspectral image classification. To confirm this matter, we apply the MLC classification algorithm which has distribution information and SAM(Spectral Angle Mapper), SFF(Spectral Feature Fitting) algorithm which use average information of the training class to both multispectral image and hyperspectral image. I conclude this result through quantitative and visual analysis using confusion matrix could confirm that SAM and SFF algorithm using of spectral pattern in vector domain is more effective way in the hyperspectral image classification than MLC which considered distribution.

• Journal of KIISE
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• v.43 no.6
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• pp.686-691
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• 2016

In this paper, we introduced the adaptive spectral co-clustering, a spectral clustering for multiview data, especially data with more than three views. In the adaptive spectral co-clustering, the performance is improved by sharing information from diverse views. For the efficiency in information sharing, a co-training approach is adopted. In the co-training step, a set of parameters are estimated to make all views in data maximally independent, and then, information is shared with respect to estimated parameters. This co-training step increases the efficiency of information sharing comparing with ordinary feature concatenation and co-training methods that assume the independence among views. The adaptive spectral co-clustering was evaluated with synthetic dataset and multi lingual document dataset. The experimental results indicated the efficiency of the adaptive spectral co-clustering with the performances in every iterations and similarity matrix generated with information sharing.