• 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 the Acoustical Society of Korea
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• v.27 no.6
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• pp.319-324
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• 2008

In this paper, we propose a novel approach to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the discriminative weight training which is based on the minimum classification error (MCE) algorithm. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then proposed the speech/music decision rule is expressed as the geometric mean of optimally weighted features which are selected from the SMV. 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 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 Korea Multimedia Society
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• v.16 no.11
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• pp.1325-1337
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• 2013

Music affects sensitivity and emotion of human, emotional power of the music has been applied to various fields. Especially, to visualize as well as listen to music is able to create various atmosphere. In this paper, we proposed sensitivity control system for interaction with people to merge music and lighting. Because existing FT(Fourier Transform) has not information about the time, to analyze information of changed signal according to the time is difficult. In order to solve such a problem, we use STFT(Short Time Fourier Transform) method to analyze music signal. and also, we classified music for three genre and compared the frequency characteristics according to genre, and control the color, brightness of LED light based on the frequency components within analysis range. Unlike existing LED lighting control study using music, we had color control of emotional lighting and brightness control using variation amount of music signal in this paper. Proposed lighting control system will be able to utilize various industry fields as well as emotional lighting.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2003.11a
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• pp.215-218
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• 2003

본 논문에서는 MPEG-7에 정의된 오디오 서술자를 이용한 오디오 특징을 기반으로 한 음악 검색 알고리즘을 제안한다. 특히 timbral 특징들은 음색 구분을 용이하게 할 수 있어 음악 검색뿐만 아니라 음악 장르 분류 또는 Query by humming에 이용 될 수 있다. 이러한 연구를 통하여 오디오 신호의 대표적인 특성을 표현 할 수 있는 특징벡터를 구성 할 수 있다면 추후에 멀티모달 시스템을 이용한 검색 알고리즘에도 오디오 특징으로 이용 될 수 있을 것이다 본 논문에서는 방송 시스템에 적용 할 수 있도록 검색 범위를 특정 컨텐츠의 O.S.T 앨범으로 제한하였다. 즉, 사용자가 임의로 선택한 부분적인 오디오 클립만을 이용하여 그 컨텐츠 전체의 O.S.T 앨범 내에서 음악을 검색할 수 있도록 하였다. 오디오 특징벡터를 구성하기 위한 MPEG-7 오디오 서술자의 조합 방법을 제안하고 distance 또는 ratio 계산 방식을 통해 성능 향상을 추구하였다. 또한 reference 음악의 템플릿 구성 방식의 변화를 통해 성능 향상을 추구하였다. Classifier로 k-NN 방식을 사용하여 성능 평가를 수행한 결과 timbral spectral feature들의 비율을 이용한 IFCR(Intra-Feature Component Ratio) 방식이 Euclidean distance 방식보다 우수한 성능을 보였다.

• 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 SC
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• v.48 no.5
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• pp.31-36
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• 2011

In this paper, Mel-Frequency Cepstral Coefficient (MFCC), Decorrelated Filter Bank (DFB), Octave-based Spectral Contrast (OSC), Zero-Crossing Rate (ZCR), and Spectral Contract/Roll-Off are combined as a set of multiple feature-vectors for the music genre classification system based on the Support Vector Machine (SVM) classifier. In the conventional system, feature vectors for the entire genre classes are normalized for the SVM model training and classification. However, in this paper, selected feature vectors that are compared based on the One-Against-One (OAO) SVM classifier are only used for normalization. Using OSC as a single feature-vector and the multiple feature-vectors, we obtain the genre classification rates of 60.8% and 77.4%, respectively, with the conventional normalization method. Using the proposed normalization method, we obtain the increased classification rates by 8.2% and 3.3% for OSC and the multiple feature-vectors, respectively.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.2-7
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• 2004

In this paper, we proposed the content-based music information retrieval technique using some MPEG-7 low-level descriptors. Especially, pitch information and timbral features can be applied in music genre classification, music retrieval, or QBH(Query By Humming) because these can be modeling the stochasticpattern or timbral information of music signal. In this work, we restricted the music domain as O.S.T of movie or soap opera to apply broadcasting system. That is, the user can retrievalthe information of the unknown music using only an audio clip with a few seconds extracted from video content when background music sound greeted user's ear. We proposed the audio feature set organized by MPEG-7 descriptors and distance function by vector distance or ratio computation. Thus, we observed that the feature set organized by pitch information is superior to timbral spectral feature set and IFCR(Intra-Feature Component Ratio) is better than ED(Euclidean Distance) as a vector distance function. To evaluate music recognition, k-NN is used as a classifier

• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.83-91
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• 2016

Music-similarity computation is crucial in developing music information retrieval systems for browsing and classification. This paper overviews the recently-proposed centroid-model based music retrieval method and applies the distributional similarity measures to the model for retrieval-performance evaluation. Probabilistic distance measures (also called divergence) compute the distance between two probability distributions in a certain sense. In this paper, we consider the alpha divergence in computing distance between two centroid models for music retrieval. The alpha divergence includes the widely-used Kullback-Leibler divergence and Bhattacharyya distance depending on the values of alpha. Experiments were conducted on both genre and singer datasets. We compare the music-retrieval performance of the distributional similarity with that of the vector distances. The experimental results show that the alpha divergence improves the performance of the centroid-model based music retrieval.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.133-140
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• 2012

In this paper, we propose a music recommendation method considering users' tags by collaborative tagging in a social music site. Since collaborative tagging allows a user to add keywords chosen by himself to web resources, it provides users' preference about the web resources concretely. In particular, emotional tags which represent human's emotion contain users' musical preference more directly than factual tags which represent facts such as musical genre and artists. Therefore, to classify the tags into the emotional tags and the factual tags and to assign weighted values to the emotional tags, a tag ontology called UniTag is developed. After preprocessing the tags, the weighted tags are used to create user profiles, and the music recommendation algorithm is executed based on the profiles. To evaluate the proposed method, a conventional playcount-based recommendation, an unweighted tag-based recommendation, and an weighted tag-based recommendation are executed. Our experimental results show that the weighted tag-based recommendation outperforms other two approaches in terms of precision.