• Proceedings of the IEEK Conference
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• summer
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• pp.229-232
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• 2004

In this paper, we propose a robust music retrieval system based on the content analysis of music. New feature extraction method called Multi-Feature Clustering (MFC) is proposed for the robust and optimum performance of the music retrieval system. It is demonstrated that the use of MFC significantly improves the system stability of music retrieval with better classification accuracy.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1581-1592
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• 2009

Recently, we can hear various kinds of music in everywhere and anytime. If a user wants to find the music which was heard before in a street or cafe, but he does not know the title of the music, it is difficult to find it. That is the limitation of previous retrieval system of music data. To overcome these problems, we research a method of content-based retrieval of music data based on the recorded humming, the part of recorded music and the played musical instrument. In this paper, we investigated previous content-based retrieval methods of papers, systems and patents. Based on that, we research a method of content-based retrieval of music data. That is, in case of using the recorded humming and music for query, we extract the frequency information from the recorded humming/music and the stored music data by using FFT. We use a MIDI file in case of query by the played musical instrument. And by using dynamic programming matching, the error caused by the disparity of length between the input source with the stored music data could be reduced.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.85-90
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• 2006

In this paper, we propose the content-based music information retrieval system with improved retrieval speed and stable performance while maintaining resonable retrieval accuracy In order to solve the in-stable system problem multi-feature clustering (MFC) is used to setup robust music DB. In addition, the music retrieval speed was improved by using the Superclass concept. Effectiveness of the system with SuperClass and without SuperClass is compared in terms of retrieval speed, accuracy and retrieval precision. It is demonstrated that the use of WC and Superclass substantially improves music retrieval speed up to $20\%\~40\%$ while maintaining almost equal retrieval accuracy.

• International Journal of Contents
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• v.8 no.2
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• pp.1-6
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• 2012

Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. However, music retrieval systems are greatly affected by background noise when music is recorded in noisy environments. Therefore, we evaluated various pre-processing methods using the Philips method to determine the one that performs most robust music retrieval in such environments. We found that dynamic noise reduction (DNR) is the best pre-processing method for a music retrieval system in noisy environments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.148-155
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• 2008

In this paper, we constructed the noise robust content-based music retrieval system in mobile environment. The performance of the proposed system was verified with ZCPA feature which is blown to have noise robust characteristic in speech recognition application. In addition, new indexing and fast retrieval method are proposed to improve retrieval speed about 99% compare to exhaustive retrieval for large music DB. From the computer simulation results in noise environment of 15dB - 0dB SNR, we confirm the superior performance of the proposed system about 5% - 30% compared to MFCC and FBE(filter bank energy) feature.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.5
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• pp.18-27
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• 2006

Rapid increase of the amount of music data demands for a new method that allows efficient similarity retrieval of music genre using audio features in music databases. To build this similarity retrieval, an indexing techniques that support audio features as a time-series pattern and data mining technologies are needed. In this paper, we address the development of a system that retrieves similar genre music based on the indexing techniques. We first propose the structure of content-based music genre retrieval system based on the time-series pattern index file and data mining technologies. In addition, we implement the time-series pattern index file using audio features and present performance analysis of the time-series pattern index file for similar genre retrieval. The experiments are performed on real data to verify the performance of the proposed method.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.32-55
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• 2018

The digitization of music has seen a considerable increase in audience size from a few localized listeners to a wider range of global listeners. At the same time, the digitization brings the challenge of smoothly retrieving music from large databases. To deal with this challenge, many systems which support the smooth retrieval of musical data have been developed. At the computational level, a query music piece is compared with the rest of the music pieces in the database. These systems, music information retrieval (MIR systems), work for various applications such as general music retrieval, plagiarism detection, music recommendation, and musicology. This paper mainly addresses two parts of the MIR research area. First, it presents a general overview of MIR, which will examine the history of MIR, the functionality of MIR, application areas of MIR, and the components of MIR. Second, we will investigate music similarity measurement methods, where we provide a comparative analysis of state of the art methods. The scope of this paper focuses on comparative analysis of the accuracy and efficiency of a few key MIR systems. These analyses help in understanding the current and future challenges associated with the field of MIR systems and music similarity measures.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.507-520
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• 2003

In this paper, an automatic construction method of theme melody index for large music database and an associative content-based music retrieval mechanism in which the constructed theme melody index is mainly used to improve the users' response time are proposed. First, the system automatically extracted the theme melody from a music file by the graphical clustering algorithm based on the similarities between motifs of the music. To place an extracted theme melody into the metric space of M-tree, we chose the average length variation and the average pitch variation of the theme melody as the major features. Moreover, we added the pitch signature and length signature which summarize the pitch variation pattern and the length variation pattern of a theme melody, respectively, to increase the precision of retrieval results. We also proposed the associative content-based music retrieval mechanism in which the k-nearest neighborhood searching and the range searching algorithms of M-tree are used to select the similar melodies to user's query melody from the theme melody index. To improve the users' satisfaction, the proposed retrieval mechanism includes ranking and user's relevance feedback functions. Also, we implemented the proposed mechanisms as the essential components of content-based music retrieval systems to verify the usefulness.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.95-104
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• 2009

This paper proposes the noise robust algorithm to detect the starting point of music. Detection of starting point of music is necessary to solve computational-waste problem and retrieval-comparison problem with inconsistent input data in music content based retrieval system. In particular, such detection is even more necessary in time sequential retrieval method that compares data in the sequential order of time in contents based music retrieval system. Whereas it has the long point that the retrieval is fast since it executes simple comparison in the order of time, time sequential retrieval method has the short point that data starting time to be compared should be the same. However, digitalized music cannot guarantee the equity of starting time by bit rate conversion. Therefore, this paper ensured that recognition rate shall not decrease even while executing high speed retrieval by applying time sequential retrieval method through detection of music starting point in the pre-processing stage of retrieval. Starting point detection used minimum wave model that can detect effective sound, and for strength against noise, the noises existing in mute sound were swapped. The proposed algorithm was confirmed to produce about 38% more excellent performance than the results to which starting point detection was not applied, and was verified for the strength against noise.

• 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