• Journal of the Korea Society for Simulation
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• 제26권4호
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• pp.23-34
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• 2017

During Khmer Rouge Regime, Cambodian pop music was completely forgotten since 90% of artists were killed. After recovering from war since 1979, the music started to grow again in 1990. However, Cambodian popular music dynamic and flows are observably directed by the multifaceted socioeconomic, political and creative forces. The major problems are the plagiarism and piracy which have been prevailing for years in the industry. Recently, the consciousness of the need to preserve Khmer original songs from both fans and artist have been increased and become a new trend for Cambodia young population. Still, the music quality is in the limit state. To increase the mind-set, the feedbacks and inspiration are needed. The study suggested a music ranking website using sentiment analysis which data were collected from Production Companies Facebook Pages' posts and comments. The study proposed an algorithm which translates from Khmer to English, doing sentiment analysis and generate the ranking. The result showed 80% accuracy of translation and sentiment analysis on the proposed system. The songs that rank high in the system are the songs which are original and fit the occasion in Cambodia. With the proposed ranking algorithm, it would help to increase the competitive advantage of the musical productions as well as to encourage the producers to compose the new songs which fit the particular activities and event.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• 제16권5호
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• pp.239-246
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• 2023

In this paper, we propose an open-loop algorithm to classify speech and music signals using the spectral flux parameters and Mel Frequency Cepstral Coefficients(MFCC) parameters for the audio coder. To increase responsiveness, the MFCC was used as a short-term feature parameter and spectral fluxes were used as a long-term feature parameters to improve accuracy. The overall voice/music signal classification decision is made by combining the short-term classification method and the long-term classification method. The Gaussian Mixed Model (GMM) was used for pattern recognition and the optimal GMM parameters were extracted using the Expectation Maximization (EM) algorithm. The proposed long-term and short-term combined speech/music signal classification method showed an average classification error rate of 1.5% on various audio sound sources, and improved the classification error rate by 0.9% compared to the short-term single classification method and 0.6% compared to the long-term single classification method. The proposed speech/music signal classification method was able to improve the classification error rate performance by 9.1% in percussion music signals with attacks and 5.8% in voice signals compared to the Unified Speech Audio Coding (USAC) audio classification method.

• The Journal of the Acoustical Society of Korea
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• 제25권2E호
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• pp.74-78
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• 2006

This paper proposes an effective method for classifying emotions of the music from its acoustical signals. Two feature sets, timbre and tempo, are directly extracted from the modified discrete cosine transform coefficients (MDCT), which are the output of partial MP3 (MPEG 1 Layer 3) decoder. Our tempo feature extraction method is based on the long-term modulation spectrum analysis. In order to effectively combine these two feature sets with different time resolution in an integrated system, a classifier with two layers based on AdaBoost algorithm is used. In the first layer the MDCT-driven timbre features are employed. By adding the MDCT-driven tempo feature in the second layer, the classification precision is improved dramatically.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• 제11권6호
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• pp.200-207
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• 2001

In this paper, a new method for Partial field decomposition is developed that is based on the beamforming algorithm for the application of acoustical holography to a composite sound field generated by multiple incoherent sound sources. In the proposed method, source Positions are first predicted by MUSIC(multiple signal classification) algorithm. The composite sound fields can then be decomposed into each partial field by the beamforming. Results of both numerical simulations and experiments show that the method can find each partial field very accurately and effectively, and that it also has Potential to be used for application to distributed sources.

• The Journal of the Korea Contents Association
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• 제20권4호
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• pp.1-13
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• 2020

In order to understand the recommendation algorithm applied to the online video platform, this study examines the relationship between the content characteristics of K-pop music videos and related videos recommended for playback on YouTube, and analyses which videos are recommended as related videos through network analysis. As a result, the more liked videos, the higher recommendation ranking and most of the videos belonging to the same channel or produced by the same agency were recommended as related videos. As a result of the network analysis of the related video, the network of K-pop music video is strongly formed, and the BTS music video is highly centralized in the network analysis of the related video. These results suggest that the network between K-pops is strong, so when you enter K-pop as a search query and watch videos, you can enjoy K-pop continuously. But when watching other genres of video, K-pop may not be recommended as a related video.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제40권6호
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• pp.1130-1141
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• 2015

Conventionally, a pulse radar is used for middle-sized or large-sized ships to detect other ships or obstacles located at a long distance. However, it is hardly equipped for most of the small-sized ships due to mounting and maintenance costs. Therefore, FMCW(frequency modulated continuous wave) radar is suggested as an alternative for the small-sized ships. Since it operates with low power and has good range resolution for relatively close objects, it is eligible for the small-sized ships. In previously proposed FMCW radar system, it only estimates distance and velocity of a target ship placed in the direction of main beam and is hard to detect several ships simultaneously. Thus, we suggest the method for detecting several ships at the same time by applying MUSIC(multiple signal classification) algorithm to FMCW radar signal received by a phased array antenna. In addition, by combining digital beam forming with the MUSIC algorithm, better angle resolution is achievable.

• Journal of the Institute of Electronics Engineers of Korea SP
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• 제48권5호
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• pp.102-108
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• 2011

Support vector machines are well known for their outstanding performance in pattern recognition fields. One example of their applications is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel scheme that improves the speech/music classification of support vector machines based on the second-order conditional maximum a priori. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. According to experimental results, the proposed algorithm shows its compatibility and potential for improving the performance of support vector machines.

• Journal of the Institute of Electronics Engineers of Korea SP
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• 제48권2호
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• pp.141-148
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• 2011

Support vector machines have been extensively studied and utilized in pattern recognition area for years. One of interesting applications of this technique is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel approach that improves the speech/music classification of support vector machines. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. We first analyze the impact of kernel width parameter on the classifications made by support vector machines. From this analysis, we observe that we can fine-tune outputs of support vector machines with the kernel width parameter. To make the most of this capability, we identify strong correlation among neighboring input frames, and use this correlation information as a guide to adjusting kernel width parameter. According to the experimental results, the proposed algorithm is found to have potential for improving the performance of support vector machines.

• Journal of the Institute of Electronics Engineers of Korea SP
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• 제45권6호
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• pp.142-147
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• 2008

In this paper, we propose a novel a roach to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the support vector machine (SVM). The SVM makes it possible to build on an optimal hyperplane that is separated without the error where the distance between the closest vectors and the hyperplane is maximal. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then feature vectors which are a lied to the SVM are selected from relevant parameters of the SMV for the efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.

• The Journal of the Acoustical Society of Korea
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• 제32권3호
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• pp.227-236
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• 2013

In this paper, we present a vocal suppression algorithm that can enhance the quality of music signal coded using Spatial Audio Object Coding (SAOC) in Karaoke mode. The residual vocal component in the coded music signal is estimated by using a cross prediction method in which the music signal coded in Karaoke mode is used as the primary input and the vocal signal coded in Solo mode is used as a reference. However, the signals are extracted from the same downmix signal and highly correlated, so that the music signal can be severely damaged by the cross prediction. To prevent this, a psycho-acoustic disturbance rule is proposed, in which the level of disturbance to the reference input of the cross prediction filter is adapted according to the auditory masking property. Objective and subjective test were performed and the results confirm that the proposed algorithm offers improved quality.