• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.211-214
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• 2008

일부 음악 장르분류에 관한 기존 연구에서는 특징 추출을 위한 구간 선택 시 사람이 직접 음악의 주요 구간을 지정하는 방법을 사용하였다. 이러한 방법은 분류 성능이 좋은 반면 수작업으로 인한 부담으로 새롭게 등록되는 음악들에 대해 지속적으로 적용하기가 곤란하다. 이러한 이유로 최근 음악 장르 분류와 관련된 연구에서는 자동으로 추출구간을 선정하는 방법을 사용하고 있는데 이러한 연구의 대부분이 고정된 구간 (예, 30초 이후의 30초 구간)에서 특징을 추출하는 관계로 분류의 정확도가 떨어지는 문제점을 갖고 있다. 본 논문에서는 이러한 문제점을 해결하기 위해 음악 전체 구간에 대하여 반복구간을 파악하고, 그 중 음악을 대표할 수 있는 단일 대표구간을 선정한 후, 대표구간으로 부터 특징을 추출하여 장르 분류 시스템에 적용하는 방법을 제안하였다. 실험 결과, 기존 고정구간을 사용한 방법에 비해 괄목할 만한 성능 향상을 얻을 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.288-291
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• 2011

기존 음악 장르 분류의 경우 음악의 특징 추출 또는 기계학습을 중점적으로 연구되어왔다. 하지만 자동 분류에 필요한 장르 데이터는 음악을 제공하는 웹 사이트마다 다르고, 각 웹 사이트의 장르 분류는 해당 음악이 아닌 앨범의 장르를 표시한다. 보다 나은 자동 분류를 위해서는 일관된 장르 데이터의 제공이 필요한데, 본 논문에서는 이러한 연구의 일환으로 여러 웹사이트에서 수집한 장르 데이터에 따른 판별 성능을 분석하였다. 분석 결과 장르 분류 방법에 따라 신경망 학습 및 판별성능이 큰 차이가 발생하였다.

• The Journal of the Acoustical Society of Korea
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• v.29 no.4
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• pp.265-269
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• 2010

This paper proposes a novel spectral audio feature, spectral contrast MFCC (SCMFCC), and studies its performance on the musical genre classification. For a successful musical genre classifier, extracting features that allow direct access to the relevant genre-specific information is crucial. In this regard, the features based on the spectral contrast, which represents the relative distribution of the harmonic and non-harmonic components, have received increased attention. The proposed SCMFCC feature utilizes the spectral contrst on the mel-frequency cepstrum and thus conforms the conventional MFCC in a way more relevant for musical genre classification. By performing classification test on the widely used music DB, we compare the performance of the proposed feature with that of the previous ones.

• The Journal of the Acoustical Society of Korea
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• v.36 no.2
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• pp.144-150
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• 2017

Musical genre is indispensible in constructing music information retrieval system, such as music search and classification. In general, the spectral characteristics of a music signal are obtained based on a subband decomposition to represent the relative distribution of the harmonic and the non-harmonic components. In this paper, we investigate the subband decomposition parameters in extracting features, which improves musical genre classification accuracy. In addition, the linear projection methods are studied to reduce the resulting feature dimension. Experiments on the widely used music datasets confirmed that the subband decomposition finer than the widely-adopted octave scale is conducive in improving genre-classification accuracy and showed that the feature-dimension reduction is effective reducing a classifier's computational complexity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.114-117
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• 2003

본 논문에서는 사용자가 음악을 직접 선곡하지 않고 락, 트로트, 댄스, 힙합, 발라드 등 5가지의 장르 중 사용자가 선호하는 음악의 장르를 추천하는 시스템을 구현하였다. 실시간으로 연주되는 음악에서 Bass Drum 신호를 추출ㆍ분석하여, 기본적으로 한 마디에 소요되는 시간, 주법, 진폭 등 세가지 파라메터를 이용하여 5가지 장르로 분류하였다 선택 곡 수와 들은 시간으로 퍼지 추론을 통해 각 장르에 대한 사용자 만족도를 평가한다. 평가된 만족도에 의해 사용자가 선호하는 장르의 음악을 제공하는 시스템을 제안한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.4
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• pp.33-38
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• 2009

This paper proposes an automatic equalizer control method in automobile audio system. The proposed method discriminates the music segment from the consecutive real-time audio stream of the radio and the equalizer is controlled automatically according to the classified genre of the music segment. For enhancing the accuracy of the music genre classification in real-time, timbre feature and rhythm feature extracted from the consecutive audio stream is applied to GMM(Gaussian mixture model) classifier. The proposed method evaluates the performance of the music genre classification, which classified various audio segments segmented from the audio signal of the radio broadcast in automobile audio system into one of five music genres.

• The Journal of the Acoustical Society of Korea
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• v.24 no.1
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• pp.29-37
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• 2005

This paper proposes an automatic genre classification system for Korean traditional music. The Proposed system accepts and classifies queried input music as one of the six musical genres such as Royal Shrine Music, Classcal Chamber Music, Folk Song, Folk Music, Buddhist Music, Shamanist Music based on music contents. In general, content-based music genre classification consists of two stages - music feature vector extraction and Pattern classification. For feature extraction. the system extracts 58 dimensional feature vectors including spectral centroid, spectral rolloff and spectral flux based on STFT and also the coefficient domain features such as LPC, MFCC, and then these features are further optimized using SFS method. For Pattern or genre classification, k-NN, Gaussian, GMM and SVM algorithms are considered. In addition, the proposed system adopts MFC method to settle down the uncertainty problem of the system performance due to the different query Patterns (or portions). From the experimental results. we verify the successful genre classification performance over $97{\%}$ for both the k-NN and SVM classifier, however SVM classifier provides almost three times faster classification performance than the k-NN.

• Journal of KIISE:Software and Applications
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• v.35 no.11
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• pp.692-700
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• 2008

In some previous works on musical genre classification, human experts specify segments of a song for extracting musical features. Although this approach might contribute to performance enhancement, it requires manual intervention and thus can not be easily applied to new incoming songs. To extract musical features without the manual intervention, most of recent researches on music genre classification extract features from a pre-determined part of a song (for example, 30 seconds after initial 30 seconds), which may cause loss of accuracy. In this paper, in order to alleviate the accuracy problem, we propose a new method, which extracts features from representative segments (or main theme part) identified by structure analysis of music piece. The proposed method detects segments with repeated melody in a song and selects representative ones among them by considering their positions and energies. Experimental results show that the proposed method significantly improve the accuracy compared to the approach using a pre-determined part.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.817-826
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• 2007

The existing a music genre classification research used signal feature of the part which gets 20 seconds interval of the random or the $40%{\sim}45%$ after in the music. This paper propose it to increase the accuracy of existing research to classify music genre using climax part in the music. Generally the music is divided to three parts; introduction, progress and climax. And the climax is the part which the music emphasizes and expresses the feature of the music best. So, we can get efficient result if the climax is used, when the music classify. We can get the climax in the music finding the tempo and node which uses FFT and the maximum waveform from each node. In this paper, we did a genre classification experiment which uses existing research method and proposing method. The existing method expressed 47% accuracy. And proposing method expressed 56% accuracy which is improved than existing method.

• The Journal of the Acoustical Society of Korea
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• v.33 no.1
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• pp.81-86
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• 2014

This paper presents a study on the effectiveness of using the spectral and the temporal octave-band order statistics for musical genre classification. In order to represent the relative disposition of the harmonic and non-harmonic components, we utilize the octave-band order statistics of power spectral distribution. Experiments on the widely used two music datasets were performed; the results show that the octave-band order statistics improve genre classification accuracy by 2.61 % for one dataset and 8.9 % for another dataset compared with the mel-frequency cepstral coefficients and the octave-band spectral contrast. Experimental results show that the octave-band order statistics are promising for musical genre classification.