Sub-fingerprint masking for a robust audio fingerprinting system in a real-noise environment for portable consumer devices

Son, Wooram; Cho, Hyun-Tae; Yoon, Kyoungro; Lee, Seok-Pil

doi:10.1109/tce.2010.5439139

Cited by 20 publications

(15 citation statements)

References 7 publications

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“…Thus, audio fingerprinting systems have been developed for music files on mobile devices [1]. In addition, automatic music recommendation systems have been developed, which perform automatic genre classification, music emotion classification, and music similarity query [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-Classifier Based on a Query-by-Singing/Humming System

Nam

Park

2015

Symmetry

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Abstract:With the increase in the number of music files on various devices, it can be difficult to locate a desired file, especially when the title of the song or the name of the singer is not known. We propose a new query-by-singing/humming (QbSH) system that can find music files that match what the user is singing or humming. This research is novel in the following three ways: first, the Fourier descriptor (FD) method is proposed as the first classifier; it transforms the humming or music waveform into the frequency domain. Second, quantized dynamic time warping (QDTW) using symmetrical search space and quantized linear scaling (QLS) are used as the second and third classifiers, respectively, which increase the accuracy of the QbSH system compared to the conventional DTW and LS methods. Third, five classifiers, which include the three already mentioned along with the conventional DTW using symmetrical search space and LS methods, are combined using score level fusion, which further enhances performance. Experimental results with the 2009 MIR-QbSH corpus and the AFA MIDI 100 databases show that the proposed method outperforms those using a single classifier and other fusion methods.

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Section: Introductionmentioning

confidence: 99%

Multi-Classifier Based on a Query-by-Singing/Humming System

Nam

Park

2015

Symmetry

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“…In practice, however, this method still needs further improvement in order to be used in real noise environments. To this end, Wooram proposed a sub-fingerprint masking method for creating a robust fingerprint scheme in real noise environments [4]. This algorithm is very robust in realnoise environments, but uses only 5-bit hash values for its subfingerprint, using a mask generated by predominant pitch * Corresponding author, Email: hschoi@daejin.ac.kr Manuscript received Apr.…”

Section: Introductionmentioning

confidence: 99%

Conjoined Audio Fingerprint based on Interhash and Intra hash Algorithms

Kim¹,

Choi²

2015

International Journal of Contents

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“…Philips scheme proposed by Haitsma and Kalker [4] is proven to be the most accurate audio fingerprinting scheme in a relatively noise-free environment. Wooram [5] uses predominant pitch extraction to devise an approach of sub-fingerprint masking, which improves the robustness of Philips scheme. The system developed by Wang [6] has become a successful commercial application.…”

Section: Introductionmentioning

confidence: 99%

Blind Source Separation for a Robust Audio Recognition Scheme in Multiple Sound-Sources Environment

Han¹,

Zhou²,

Li³

et al. 2015

Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Abstract-In multiple sound-sources environment, robustness is a major challenge for audio recognition system based on audio fingerprinting, because mixed audio signals may make recognition rate has a significant decline. This paper proposes a novel audio fingerprinting method, which uses blind source separation to divide mixed audio signals into independent components and each is close to its original sound-source, then the classical Philips scheme can perform accurately identifying. Experimental results show that novel scheme is quite robust in noisy conditions where uncertain audio signals mixed by various numbers of sound-source, even though the feature of each original sound-source and their mixed model are unknown.

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Sub-fingerprint masking for a robust audio fingerprinting system in a real-noise environment for portable consumer devices

Abstract: Abstract

Cited by 20 publications

References 7 publications

Multi-Classifier Based on a Query-by-Singing/Humming System

Multi-Classifier Based on a Query-by-Singing/Humming System

Conjoined Audio Fingerprint based on Interhash and Intra hash Algorithms

Blind Source Separation for a Robust Audio Recognition Scheme in Multiple Sound-Sources Environment

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