• Journal of IKEEE
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• v.24 no.1
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• pp.339-346
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• 2020

In this paper, we propose an angle estimator that is designed to be applied to Bluetooth low-power application technology based on multiple signal classification (MUSIC) algorithm, and present the result of implementation in FPGA. The MUSIC algorithm is designed for H/W high-speed design because it requires a lot of calculations due to high accuracy, and the snapshot variable is designed to cope with various resolution requirements of indoor systems. As a result of the implementation with Xilinx zynq-7000, it was confirmed that 9,081 LUTs were implemented, and it was designed to operate at =the operating frequency of 100MHz.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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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.

• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.977-985
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• 2016

In this paper, we propose a new method for on-line genre classification using spectrogram and deep neural network. For on-line processing, the proposed method inputs an audio signal for a time period of 1sec and classifies its genre among 3 genres of speech, music, and effect. In order to provide the generality of processing, it uses the spectrogram as a feature vector, instead of MFCC which has been widely used for audio analysis. We measure the performance of genre classification using real TV audio signals, and confirm that the proposed method has better performance than the conventional method for all genres. In particular, it decreases the rate of classification error between music and effect, which often occurs in the conventional method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.2
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• pp.95-106
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• 2007

This paper proposes an effective tempo feature extraction method for music information retrieval. The tempo information is modeled by the narrow-band temporal modulation components, which are decomposed into a modulation spectrum via joint frequency analysis. In implementation, the tempo feature is directly extracted from the modified discrete cosine transform coefficients, which is the output of partial MP3(MPEG 1 Layer 3) decoder. Then, different features are extracted from the amplitudes of modulation spectrum and applied to different music information retrieval tasks. The logarithmic scale modulation frequency coefficients are employed in automatic music emotion classification and music genre classification. The classification precision in both systems is improved significantly. The bit vectors derived from adaptive modulation spectrum is used in audio fingerprinting task That is proved to be able to achieve high robustness in this application. The experimental results in these tasks validate the effectiveness of the proposed tempo feature.

• The Journal of the Acoustical Society of Korea
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• v.26 no.8
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• pp.390-396
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• 2007

In this letter, we propose a novel approach to improve the performance of speech/music classification for the selectable mode vocoder(SMV) of 3GPP2 using the Gaussian mixture model(GMM) which is based on the expectation-maximization(EM) algorithm. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then feature vectors which are applied to the GMM 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 Korean Institute of Communications and Information Sciences
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• v.30 no.5C
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• pp.305-312
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• 2005

Three classification algorithms are tested using musical instruments. Several classification algorithms are introduced and among them, Bayes rule, NN and k-NN performances evaluated. ZCR, mean, variance and average peak level feature vectors are extracted from instruments sample file and used as data set to classification system. Used musical instruments are Violin, baroque violin and baroque cello. Results of experiment show that the performance of NN algorithm excels other algorithms in musical instruments classification.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.238-244
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• 2013

For musical genre classification, various types of feature vectors are utilized. Mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), and octave-based spectral contrast (OSC) are widely used as short-term features, and their long-term variations are also utilized. In this paper, OSC features are extracted not only in the single-octave band domain, but also in the multiple-octave band one to capture the correlation between octave bands. As a baseline system, we select the genre classification system that won the fourth place in the 2012 music information retrieval evaluation exchange (MIREX) contest. By applying the OSC features based on multiple-octave bands, we obtain the better classification accuracy by 0.40% and 3.15% for the GTZAN and Ballroom databases, respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.36-41
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• 2010

An advanced form of Multistage Feature-based Classification Model(AMFCM), called AMFCM, is proposed in this paper. AMFCM like MFCM does not use the concatenated form of available feature vectors extracted from original data to classify each data, but uses only groups related to each feature vector to classify separately. The prpposed AMFCM improves the contribution rate used in MFCM and proposes a confusion table for each local classifier using a specific feature vector group. The confusion table for each local classifier contains accuracy information of each local classifier on each class of data. The proposed AMFCM is applied to the problem of music genre classification on a set of music data. The results demonstrate that the proposed AMFCM outperforms MFCM by 8% - 15% on average in terms of classification accuracy depending on the grouping algorithms used for local classifiers and the number of clusters.

• International Journal of Costume and Fashion
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• v.6 no.2
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• pp.28-42
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• 2006

In present modern society, media contributes more to the constructing of personal identities than any other medium. Music video, a postmodernism branch among a variety of media, offers a complex experience of sounds combined with visual images. In particular. fashion in music video helps conveying contexts effectively and functions as a medium of immediate communication by visual effect. Considering the socio-cultural effects of music video. gender identity represented in fashion in it can be of great importance. Therefore, this study is geared to the reconsidering of gender identity represented through costumes in music video by analyzing fashions in it. Gender identity in socio-cultural category is classified as masculinity, femininity, and the third sex. By examining fashions based on the classification. this study will help to create new design concepts and to understand gender identity in fashion. The results of this study are as follows: First. masculinity in music video fashion was categorized into stereotyped masculinity, sexual masculinity. and metro sexual masculinity. Second, femininity in music video fashion was categorized into stereotyped femininity. sexual femininity, and contra sexual femininity. Third, the third sex in music video fashion was categorized into transvestism, masculinization of female, and feminization of male. This phenomenon is presented into music videos through females in male attire and males in female attire. Through this research, gender identity represented in fashion of music video was demonstrated, and the importance of the relationship between representation of identity through fashion and socio-cultural environment was reconfirmed.

• Journal of Web Engineering
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• v.20 no.8
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• pp.2335-2360
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• 2021

A folksonomy is a classification system in which volunteers collaboratively create and manage tags to annotate and categorize content. The folksonomy has several problems in retrieving music using tags, including problems related to synonyms, different tagging levels, and neologisms. To solve the problem posed by synonyms, we introduced a mood vector with 12 possible moods, each represented by a numeric value, as an internal tag. This allows moods in music pieces and mood tags to be represented internally by numeric values, which can be used to retrieve music pieces. To determine the mood vector of a music piece, 12 regressors predicting the possibility of each mood based on acoustic features were built using Support Vector Regression. To map a tag to its mood vector, the relationship between moods in a piece of music and mood tags was investigated based on tagging data retrieved from Last.fm, a website that allows users to search for and stream music. To evaluate retrieval performance, music pieces on Last.fm annotated with at least one mood tag were used as a test set. When calculating precision and recall, music pieces annotated with synonyms of a given query tag were treated as relevant. These experiments on a real-world data set illustrate the utility of the internal tagging of music. Our approach offers a practical solution to the problem caused by synonyms.