Efficient and portable content-based music retrieval system

Chiang, Yen-Lin; Lee, Yuan-Shan; Hsieh, Wen-Chi; Wang, Jia-Ching

doi:10.1109/icot.2014.6956622

Cited by 1 publication

(4 citation statements)

References 7 publications

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“…However, under noisy environments, the performance might degrade due to the mismatch between the noisy feature and the clean-trained model. Motivated by this concern, this paper extends the previous work [6]. Noise effects are further reduced by applying Approximate Karbunen–Loeve transform ( AKLT ) [7] for preprocessing.…”

Section: Introductionmentioning

confidence: 86%

“…Hence, we can use Chroma features to identify all kinds of songs, even cover versions [17]. This research extends our previous work [6]. Compared with [6], a new feature vector containing 39-MFCCs features and 12-Chroma features are extracted.…”

Section: A) Music Content Representationmentioning

confidence: 94%

“…The adapted SAX with (6) is a more feasible and convenient way than the original SAX with (5). Both (5) and (6) are increasing functions for all real numbers x, while (5) contains the Gaussian error function (erf), whose inverse function does not commonly exist in many standard libraries in popular programming languages such as C + + and Java. However, (6) has a "special function", the arctan function, whose inverse function is identical to the tangent function in almost all standard libraries of the major programming languages.…”

Section: C) Paa and The Adapted Saxmentioning

confidence: 99%

“…Such a design of the music database provides high efficiency and accuracy due to its good algorithms, but it lacks in portability since the implementation is too complicated. Inspired by Huang [2], a QBS-CBMR system was proposed in our previous work [6]. An entropy-weighting mechanism was developed to determine the final similarity.…”

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confidence: 99%

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Robust and efficient content-based music retrieval system

Lee

Chiang

Lin

et al. 2016

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This work proposes a query-by-singing (QBS) content-based music retrieval (CBMR) system that uses I . I N T R O D U C T I O NDigital music data on the Internet are explosively growing. Therefore, applications of content-based music retrieval (CBMR) system are more and more popular. Searching music by a particular melody of a song directly is more convenient than by a name of a song for people. Moreover, according to the survey from the United Nations [1], the 21st century will witness even more rapid population ageing than did the century just past; therefore, it is important to develop an efficient and accurate way to retrieve the music data.A CBMR method is a more effective approach for a music retrieval system than the text-based method. A CBMR system aims to retrieve and query music by acoustic features of music, while a text-based music retrieval system only takes names, lyrics, and ID3 tags of songs into consideration.Query-by-singing (QBS) is a popular method in CBMR. Many approaches based on QBS have been developed currently. Huang [2] proposed a QBS system by extracting the pitches and the volumes of the music. The data

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Section: Introductionmentioning

confidence: 86%

Section: A) Music Content Representationmentioning

confidence: 94%

Section: C) Paa and The Adapted Saxmentioning

confidence: 99%

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confidence: 99%

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