• International Journal of Contents
• /
• v.8 no.2
• /
• pp.1-6
• /
• 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 Korea Society of Computer and Information
• /
• v.12 no.6
• /
• pp.97-104
• /
• 2007

This paper presents the problem of the feature extraction techniques that has been used a content-based analysis, classification and retrieval in audio data and proposes a course of the preprocessing for a new contents-based retrieval methods. Because the feature vector according to sampling value changes, the existing audio data analysis is problem that same music is appraised by other music. Therefore, we propose waveform information extraction method of PCM data for retrieval audio data of various format to contents-based. If this method is used. we can find that audio datas that get into sampling in various format are same data. And it may be applied in contents-based music retrieval system. To verity the performance of the method, an experiment was done feature extraction using STFT and waveform information extraction using PCM data. As a result, we could know that the method to propose is effective more.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.3
• /
• pp.99-106
• /
• 2008

Recently, digital music retrieval is using in many fields (Web portal. audio service site etc). In existing fields, Meta data of music are used for digital music retrieval. If Meta data are not right or do not exist, it is hard to get high accurate retrieval result. Contents based information retrieval that use music itself are researched for solving upper problem. In this paper, we propose Same music recognition method using similarity measurement. Feature data of digital music are extracted from waveform of music using Simplified MFCC (Mel Frequency Cepstral Coefficient). Similarity between digital music files are measured using DTW (Dynamic time Warping) that are used in Vision and Speech recognition fields. We success all of 500 times experiment in randomly collected 1000 songs from same genre for preying of proposed same music recognition method. 500 digital music were made by mixing different compressing codec and bit-rate from 60 digital audios. We ploved that similarity measurement using DTW can recognize same music.

• The Journal of the Korea Contents Association
• /
• v.10 no.8
• /
• pp.130-137
• /
• 2010

An advancement and wide use of portable multimedia devices make it easy to spread digital sound source, enabling users to use portable multimedia devices and search and enjoy digital sound sources and related contents. In opposition the off-line musical service market with CD stagnates gradually and in the crisis where the off-line musical service market vanishes away. In this paper, we propose the mobile music album information retrieval system which combines the barcode and a cellular phone musical service. To get digital sound sources and related information from music albums, the proposed retrieval system uses one-dimensional barcodes attached to music albums and obtains information on related albums from the sound source servers. Also, to reflect user preferences in search results, the system uses search frequency by music album, and searches albums in music genres selected by users in the order of the recent preference. The proposed mobile music album retrieval system is implemented on the basis of WIPI, and is now providing pilot services.

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

• The KIPS Transactions:PartA
• /
• v.10A no.6
• /
• pp.737-748
• /
• 2003

With the popularity of digital music contents, querying and retrieving music contents efficiently from database has become essential. In this paper, we propose a Fast Melody Finder (FMF) that can retrieve melodies fast and efficiently from music database using frequently queried tunes. This scheme is based on the observation that users have a tendency to memorize and query a small number of melody segments, and indexing such segments enables fast retrieval. To handle those tunes, FMF transcribes all the acoustic and common music notational inputs into a specific string such as UDR and LSR. We have implemented a prototype system and showed on its performance through various experiments.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.5
• /
• pp.43-52
• /
• 2013

In recent years, a large amount of music content is distributed in the Internet environment. In order to retrieve the music content effectively that user want, various studies have been carried out. Especially, it is also actively developing music recommendation system combining emotion model with MIR(Music Information Retrieval) studies. However, in these studies, there are several drawbacks. First, structure of emotion model that was used is simple. Second, because the emotion model has not designed for Korean language, there is limit to process the semantic of emotional words expressed with Korean. In this paper, through extending the existing emotion model, we propose a new emotion model KOREM(KORean Emotional Model) based on Korean. And also, we design and implement ontology using emotion model proposed. Through them, sorting, storage and retrieval of music content described with various emotional expression are available.

• The Journal of Korean Association of Computer Education
• /
• v.8 no.5
• /
• pp.109-118
• /
• 2005

In this paper, we present a music retrieval system that compares the geometric structure of a melody specified by a user with those in a music database. The system finds matches between a query melody and melodies in the database by analyzing both structural and contextual features. The retrieval method is based on the geometric hashing algorithm which consists of two steps; the preprocessing step and the recognition step. During the preprocessing step, we divide a melody into several fragments and analyze the pitch and duration of each note of the fragments to find a structural feature. To find a contextual feature, we find a main chord for each fragment. During the recognition step, we divide the query melody specified by a user into several fragments and search through all fragments in the database that are structurally and contextually similar to the melody. A vote is cast for each of the fragments and the music whose total votes are the maximum is the music that contains a matching melody against the query melody. Using our approach, we can find similar melodies in a music database quickly. We can also apply the method to detect plagiarism in music.

• International Journal of Contents
• /
• v.11 no.4
• /
• pp.1-6
• /
• 2015

In practice, the most important performance parameters for music information retrieval (MIR) service are robustness of fingerprint in real noise environments and recognition accuracy when the obtained query clips are matched with the an entry in the database. To satisfy these conditions, we proposed a conjoined fingerprint algorithm for use in massive MIR service. The conjoined fingerprint scheme uses interhash and intrahash algorithms to produce a robust fingerprint scheme in real noise environments. Because the interhash and intrahash algorithms are masked in the predominant pitch estimation, a compact fingerprint can be produced through their relationship. Experimental performance comparison results showed that our algorithms were superior to existing algorithms, i.e., the sub-mask and Philips algorithms, in real noise environments.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.5
• /
• pp.637-644
• /
• 2011

Being digitalized, the music can be easily purchased and delivered to the users. However, there is still some difficulty to find the music which fits to someone's taste using traditional music information search based on musician, genre, tittle, album title and so on. In order to reduce the difficulty, the contents-based or the emotion-based music retrieval has been proposed and developed. In this paper, we propose new method to determine the importance of MPEG-7 low-level audio descriptors which are multi-dimensional vectors for the emotion-based music retrieval. We measured the mutual similarities of musics which represent a pair of emotions expressed by opposite meaning in terms of each multi-dimensional descriptor. Then rough approximation, and inter- and intra similarity ratio from the similarity relation are used for determining the importance of a descriptor, respectively. The set of weights based on the importance decides the aggregated similarity measure, by which emotion-based music retrieval can be achieved. The proposed method shows better result than previous method in terms of the average number of satisfactory musics in the experiment emotion-based retrieval based on content-based search.