• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.141-150
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• 2009

This paper proposes a system that can automatically write singing voices to music notes. First, the system uses Stabilized Diffusion Equation(SIDE) to divide the song to a series of syllabic parts based on pitch detection. By the song segmentation, our method can recognize the sound length of each fragment through clustering based on genetic algorithm. Moreover, this study introduces a concept called 'Relative Interval' so as to recognize interval based on pitch of singer. And it also adopted measure extraction algorithm using pause data to implement the higher precision of song transcription. By the experiments using 16 nursery songs, it is shown that the measure recognition rate is 91.5% and DMOS score reaches 3.82. These findings demonstrate effectiveness of system performance.

• The Journal of the Acoustical Society of Korea
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• v.32 no.3
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• pp.247-251
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• 2013

In this paper, near-field source localization algorithm is presented using Matrix Pencil in Uniform Linear Array(ULA). Based on the centrosymmetry of the ULA, the proposed algorithm decouples the steering vectors which allow for the bearing estimation using Matrix pencil. With estimated bearing, the range estimation of each source is consequently obtained by defining 1D MUSIC spectrum. Simulation results are presented to validate the performance of the proposed algorithm.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1237-1253
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• 2018

Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and can solve different optimization problems. In order to further improve the performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four complex function optimization and pressure vessel optimization problems were simulated using the optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to find global search and evolutionary speed. Optimization effect simulation results are satisfactory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1657-1670
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• 2013

The increase in the number of music files in smart phone and MP3 player makes it difficult to find the music files which people want. So, Query-by-Singing/Humming (QbSH) systems have been developed to retrieve music from a user's humming or singing without having to know detailed information about the title or singer of song. Most previous researches on QbSH have been conducted using musical instrument digital interface (MIDI) files as reference songs. However, the production of MIDI files is a time-consuming process. In addition, more and more music files are newly published with the development of music market. Consequently, the method of using the more common MPEG-1 audio layer 3 (MP3) files for reference songs is considered as an alternative. However, there is little previous research on QbSH with MP3 files because an MP3 file has a different waveform due to background music and multiple (polyphonic) melodies compared to the humming/singing query. To overcome these problems, we propose a new QbSH method using MP3 files on mobile device. This research is novel in four ways. First, this is the first research on QbSH using MP3 files as reference songs. Second, the start and end positions on the MP3 file to be matched are estimated by using multi-layered perceptron (MLP) prior to performing the matching with humming/singing query file. Third, for more accurate results, four MLPs are used, which produce the start and end positions for dynamic time warping (DTW) matching algorithm, and those for chroma-based DTW algorithm, respectively. Fourth, two matching scores by the DTW and chroma-based DTW algorithms are combined by using PRODUCT rule, through which a higher matching accuracy is obtained. Experimental results with AFA MP3 database show that the accuracy (Top 1 accuracy of 98%, with an MRR of 0.989) of the proposed method is much higher than that of other methods. We also showed the effectiveness of the proposed system on consumer mobile device.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1413-1419
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• 2015

Monopulse RADAR is the radar which detects the range of the target using a single transmitted signal. In this paper, using 9.41GHz X-band radar, the research for the phase comparison monopulse algorithm used in the marine environment is conducted. In addition, by applying the phase comparison monopulse algorithm, we calculate the RMSE for the various antenna spacings and the positions of the target. Based on that result, we compare the performance of the phase comparison monopulse algorithm in the uniform linear array with that in the non-uniform linear array. Finally, the differences in performance among the MUSIC algorithm, Bartlett method and the proposed phase comparison monopulse algorithm are analyzed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.1-7
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• 2009

An approach for the classification of music genres using a Fuzzy c-Means(FcM) with divergence-based kernel is proposed and presented in this paper. The proposed model utilizes the mean and covariance information of feature vectors extracted from music data and modelled by Gaussian Probability Density Function (GPDF). Furthermore, since the classifier utilizes a kernel method that can convert a complicated nonlinear classification boundary to a simpler linear one, he classifier can improve its classification accuracy over conventional algorithms. Experiments and results on collected music data sets demonstrate hat the proposed classification scheme outperforms conventional algorithms including FcM and SOM 17.73%-21.84% on average in terms of classification accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.12
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• pp.1013-1020
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• 2013

In this paper the super-resolution time delay estimation algorithms based on eigen-analysis are developed for spread spectrum signals along with their comparative performance analysis. First, we shall develop super-resolution time delay estimation algorithms using the representative eigen-analysis based AOA (angle-of-arrival) estimation algorithms such as MUSIC, Minimum-Norm, and ESPRIT, and apply them to the ISO/IEC 24730-2.1 real-time locating system (RTLS) employing a direct sequence spread spectrum (DS-SS) technique to compare their performances in RTLS environments. Simulation results illustrate that all the three algorithms can resolve multipath signals whose delay differences are even smaller than the Rayleigh resolution limit. Simulation results also show that MUSIC and Minimum-Norm provide a similar performance while ESPRIT is inferior to both algorithms in RTLS environments.

• The Journal of the Korea Contents Association
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• v.8 no.6
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• pp.16-25
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• 2008

Today, mobile phone is a necessity of modern life. For that reason, we suggest a particular system of a mobile phone which take a picture of music score image and automatically play it without any technical knowledges about the music score information. This experiment makes midi, acknowleging separate symbols via preprocessing to music score image taken. This paper utilizes 11 sorts of the score image taken by a mobile phone camera for this experiment. Through this method we suggest, as much as 98% on average takes place, which is very high recognizing ratio. Also, as we introduce this system in a mobile phone by porting, it takes 8.63 seconds on average to create midi following input of images.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.461-467
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• 2011

Accurate classification of input signals is the key prerequisite for variable bit-rate coding, which has been introduced in order to effectively utilize limited communication bandwidth. Especially, recent surge of multimedia services elevate the importance of speech/music classification. Among many speech/music classifier, the ones based on support vector machine (SVM) have a strong selling point, high classification accuracy, but their computational complexity and memory requirement hinder their way into actual implementations. Therefore, techniques that reduce the computational complexity and the memory requirement is inevitable, particularly for embedded systems. We first analyze implementation of an SVM-based classifier on embedded systems in terms of execution time and energy consumption, and then propose two techniques that alleviate the implementation requirements: One is a technique that removes support vectors that have insignificant contribution to the final classification, and the other is to skip processing some of input signals by virtue of strong correlations in speech/music frames. These are post-processing techniques that can work with any other optimization techniques applied during the training phase of SVM. With experiments, we validate the proposed algorithms from the perspectives of classification accuracy, execution time, and energy consumption.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1398-1406
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• 2007

Providing natural and efficient access to the fast growing multimedia information is a critical aspect for content-based information system. Query by humming system allows the user to find a song by humming part of the tune form music database. Conventional music information retrieval systems use a high precision pitch extraction method. However, it is very difficult to extract true pitch perfectly. So, In this paper, we propose to use YIN parameter with applying the reliability to reduce the pitch extraction errors. And we describes developed music information retrieval method based on a query by humming system which uses reliable feature extraction. Developed system is based on a continuous dynamic programming algorithm with features including pitch, duration and energy along with their confidence measures. The experiment showed that the proposed method could reduce the errors of the top-10 7.2% and the top-1 9.1% compared with the cepsturm based multiple pitch candidate. The overall retrieval system achieved 92.8% correct retrieval in the top-10 rank list on a database of 155 songs.