• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.119-130
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• 2013

This paper researches the algorithm, whose materiality and expressiveness can be obtained through live coding. Live coding is an improvised genre of music that generates sounds while writing code in real time and projecting it onto a screen. Previous studies of live coding have focused on the development environment to support live coding performance effectively. However, this study examines the aesthetic attitude immanent in the realization of the algorithm through analyzing mostly used languages such as ChucK, Impromtu, and the visualization of live code and cases of "aa-cell" and "slub" performance. The aesthetic attitudes of live coding performance can be divided into algebraic and geometric attitudes. Algebraic attitudes underline the temporal development of concepts; geometric attitudes highlight the materialization of the spatial structure of concepts through image schemas. Such a difference echoes the tension between conception and materiality, which appears in both conceptual and concrete poetry. The linguistic question of whether conception or materiality is more greatly emphasized defines the expressiveness of the algorithm.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.19-26
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• 2012

The rapid increase of information imposes new demands of content management. The purpose of automatic audio segmentation and classification is to meet the rising need for efficient content management. With this reason, this paper proposes a high-accuracy algorithm that segments audio signals and classifies them into different classes such as speech, music, silence, and environment sounds. The proposed algorithm utilizes support vector machine (SVM) to detect audio-cuts, which are boundaries between different kinds of sounds using the parameter sequence. We then extract feature vectors that are composed of statistical data and they are used as an input of fuzzy c-means (FCM) classifier to partition audio-segments into different classes. To evaluate segmentation and classification performance of the proposed SVM-FCM based algorithm, we consider precision and recall rates for segmentation and classification accuracy for classification. Furthermore, we compare the proposed algorithm with other methods including binary and FCM classifiers in terms of segmentation performance. Experimental results show that the proposed algorithm outperforms other methods in both precision and recall rates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5860-5876
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• 2018

A single snapshot data can only provide limited amount of information so that the rank of covariance matrix is not full, which is not adopted to complete the parameter estimation directly using the traditional super-resolution method. Aiming at solving the problem, a joint time delay and angle estimation using matrix pencil method based on information reconstruction vector for orthogonal frequency division multiplexing (OFDM) signal is proposed. Firstly, according to the channel frequency response vector of each array element, the algorithm reconstructs the vector data with delay and angle parameter information from both frequency and space dimensions. Then the enhanced data matrix for the extended array element is constructed, and the parameter vector of time delay and angle is estimated by the two-dimensional matrix pencil (2D MP) algorithm. Finally, the joint estimation of two-dimensional parameters is accomplished by the parameter pairing. The algorithm does not need a pseudo-spectral peak search, and the location of the target can be determined only by a single receiver, which can reduce the overhead of the positioning system. The theoretical analysis and simulation results show that the estimation accuracy of the proposed method in a single snapshot and low signal-to-noise ratio environment is much higher than that of Root Multiple Signal Classification algorithm (Root-MUSIC), and this method also achieves the higher estimation performance and efficiency with lower complexity cost compared to the one-dimensional matrix pencil algorithm.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.05a
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• pp.280-283
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• 2004

Today, the guide line of the SFRC mix design and the construction was not embodied, and the convenience of the practical application on the spot is not good. In this research, hence, the program which is optimized to result the mix proportion by the flexural strength and toughness, was developed to apply with ease SFRC on the practical spot. This program would minimize the number of trial mixes and achieve an economical and appropriate mixture. In addition, the theoretical background on which the program is based, will be the basis of the embodied method to mixing SFRC. New algorithm, in this research, was used to develop the mix proportioning program of SFRC. The new algorithm is the Harmony Search which is the heuristic method mimicking the improvisation of music players. And, beside to single fiber reinforced concrete, it was developed the program about the hybrid fiber reinforced concrete that two kinds of steel fibers, which have the different geometry, was reinforced. This will be able to keep the world trend to study, hence, offers the basis of the next generation research.

• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.279-286
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• 2006

Classification method used in this paper is applied for the first time to Korean traditional music. Among the frequency distribution vectors, average peak value is suggested and proved effective comparing to previous classification success rate. Mean, variance, spectral centroid, average peak value and ZCR are used to classify Korean traditional musical instruments. To achieve Korean traditional instruments automatic classification, Spectral analysis is used. For the spectral domain, Various functions are introduced to extract features from the data files. k-NN classification algorithm is applied to experiments. Taegum, gayagum and violin are classified in accuracy of 94.44% which is higher than previous success rate 87%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10B
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• pp.1252-1257
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• 2011

In this paper, efficient functions for audio spectrum mapping with visible spectrum are proposed. Through mapping overall hearing frequency band with visible frequency band, emotional lighting might be possible. We propose a basic linear mapping function and non-linear mapping functions emphasizing specific audio frequency bands. For the algorithm implementation, spectrum analysis method and filter method are introduced. Especially, in this paper, a prototype LED lighting equipment using the digital filter method is implemented. The proposed lighting method can be applied to many LED lighting area using music.

• The Journal of the Acoustical Society of Korea
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• v.31 no.5
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• pp.332-339
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• 2012

Beamspace transform algorithm transforms spatial-domain data - such as x, y, z dimension - into incidence-angle-domain data, which is called beamspace-domain data. The beamspace transform method is generally used in source localization and tracking, and adaptive beamforming problem. When the beamspace transform method is used in multichannel audio source separation, the inverse beamspace transform is also important because the source image have to be reconstructed. This paper studies the beamspace transform and inverse transform algorithms for multichannel audio source separation system, especially for the beamspace-domain multichannel NMF algorithm.

• Journal of Advanced Navigation Technology
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• v.16 no.4
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• pp.649-656
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• 2012

The Global Positioning System (GPS) is widely utilized for commercial and military applications to estimate the location of the user or object. The GPS suffers from various intentional or unintentional interferers and it requires estimating the accurate angle-of-arrival (AOA) of the GPS signal to suppress interference signals and to efficiently detect GPS data. Since the power of GPS signal is very low comparing with the noise and interference signals, it is extremely difficult to estimate GPS AOA before despreading. Although AOA of GPS signal is usually estimated after despreading, it requires choosing the GPS AOA among results of AOA estimation because they include AOAs of interference and GPS signals when existing high-power interferers. In this paper, we propose the efficient choosing algorithm of the GPS signal among the estimated AOAs. The proposed algorithm compares the estimated results before despreading and after despreading for choosing AOA of GPS signal. Computer simulation examples are presented to illustrate the performance of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.439-451
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• 2021

We propose a deep learning-based beamformer combined with spectral subtraction for continuous speech recognition operating in noisy environments. Conventional beamforming systems were mostly evaluated by using pre-segmented audio signals which were typically generated by mixing speech and noise continuously on a computer. However, since speech utterances are sparsely uttered along the time axis in real environments, conventional beamforming systems degrade in case when noise-only signals without speech are input. To alleviate this drawback, we combine online beamforming algorithm and spectral subtraction. We construct a Continuous Speech Enhancement (CSE) evaluation set to evaluate the online beamforming algorithm in noisy environments. The evaluation set is built by mixing sparsely-occurring speech utterances of the CHiME3 evaluation set and continuously-played CHiME3 background noise and background music of MUSDB. Using a Kaldi-based toolkit and Google web speech recognizer as a speech recognition back-end, we confirm that the proposed online beamforming algorithm with spectral subtraction shows better performance than the baseline online algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.155-163
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• 2012

The geometrical and electrical errors of array sensors can severely degrade the performance of array sensor system. Various calibration techniques are developed to alleviate this problem. In this paper, two different calibration methods with respect to location, gain and phase of array sensors are presented. One method applies the first-order Taylor series expansion to approximate the true steering vector from the nominal values of array sensors. Then a set of equations is formed by using the null characteristics of the MUSIC spectrum to estimate errors of location, gain and phase of array sensors. Another method estimates these errors based on the data covariance matrix of pilot sources. From the simulations, it is demonstrated that two calibration algorithms calibrated an array system successfully. In addition to that, Fistas and Manikas's algorithm is more robust against noise than Ng and Lie's one when SNR is from 10dB to 50dB.