A Highly Robust Audio Fingerprinting System With an Efficient Search Strategy

Haitsma, JA Jaap; Kalker, A.A.C.M.

doi:10.1076/jnmr.32.2.211.16746

Cited by 335 publications

(482 citation statements)

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“…If we require a successful search rate of 90%, this system will not work for bit errors greater than about 0.136. This corresponds with empirical observations that this method does not work well for higher bit errors [9,5]. In general, Equation 1 allows us to predict the performance of a fingerprint hash for any bit error rate.…”

Section: Fingerprint Hashsupporting

confidence: 76%

“…We briefly describe the fundamentals of hash-based search for music identification, based on the work of Haitsma and Kalker [9]. To distinguish between this method and other hashed based methods in this paper, we denote this method Fingerprint Hash.…”

Section: Fingerprint Hashmentioning

confidence: 99%

“…For a basic fingerprint hash as in [9], hash table accesses τ = ℓ and the average number of fingerprints in a bucket b = nℓ 2 ψ , leading to the number of candidate fingerprints returned as…”

Section: Fingerprint Hashmentioning

confidence: 99%

“…In [9] each fingerprint consists of 8192 bits. This fingerprint is disjointly sub-divided into 256 subfingerprints of 32 bits.…”

Section: Fingerprint Hashmentioning

confidence: 99%

“…For music identification, one common search algorithm is based on hashing subfingerprints [9]. Section 2 describes three hash-based methods, fingerprint hashing and its derivative extended fingerprint hashing, and the Locality Sensitive Hash-based sample hash.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of extended fingerprint hashing and locality sensitive hashing for binary audio fingerprints

Moravec

Cox

2011

Proceedings of the 1st ACM International Conference on Multimedia Retrieval

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Hash tables have been proposed for the indexing of highdimensional binary vectors, specifically for the identification of media by fingerprints. In this paper we develop a new model to predict the performance of a hash-based method (Fingerprint Hashing) under varying levels of noise. We show that by the adjustment of two parameters, robustness to a higher level of noise is achieved. We extend Fingerprint Hashing to a multi-table range search (Extended Fingerprint Hashing) and show this approach also increases robustness to noise. We then show the relationship between Extended Fingerprint Hashing and Locality Sensitive Hashing and investigate design choices for dealing with higher noise levels. If index size must be held constant, the Extended Fingerprint Hash is a superior method. We also show that to achieve similar performance at a given level of noise a Locality Sensitive Hash requires nearly a six-fold increase in index size which is likely to be impractical for many applications.

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Section: Fingerprint Hashsupporting

confidence: 76%

Section: Fingerprint Hashmentioning

confidence: 99%

Section: Fingerprint Hashmentioning

confidence: 99%

“…In [9] each fingerprint consists of 8192 bits. This fingerprint is disjointly sub-divided into 256 subfingerprints of 32 bits.…”

Section: Fingerprint Hashmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A comparison of extended fingerprint hashing and locality sensitive hashing for binary audio fingerprints

Moravec

Cox

2011

Proceedings of the 1st ACM International Conference on Multimedia Retrieval

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Audio Watermarking Based on Vector Quantization Index Modulation Using Audio Fingerprint

Nakaya

Wada

2015

Elect Comm in Japan

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SUMMARY Development of high‐quality digital audio watermarking with robustness to data compression and watermark attacks such as cropping is an important research topic. Recently, an effective watermarking method using vector quantization index modulation (VQIM) was proposed to improve the watermark performance. In this paper, a new auditory‐band MDCT‐based VQIM‐type watermarking using audio fingerprint (AFP) is proposed to enhance the performance. The parameter values of VQIM are essential for audio quality and robustness, which are determined as individual codebook parameters. The parameters are estimated using AFP in the watermark detection process. In simulations, the effectiveness of the method was examined to show its robustness to watermark attacks. The audio quality was evaluated based on PEAQ.

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Two‐pass search strategy using accumulated band energy histogram for HMM‐based identification of perceptually identical music

Myung

Kim

Park

et al. 2013

Int J Imaging Syst Tech

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ABSTRACT:In this article, we present an efficient two-pass search strategy for the implementation of a Hidden Markov Model (HMM)-based music identification system. In our previous work, we demonstrated a single-pass HMM-based music identification system, considering its application to music copyright protection. This conventional system showed very robust performance to signal-level variations between perceptually identical music files. However, it requires heavy computation for search. In the proposed two-pass search system, the conventional single-pass search is extended to two-pass. In pass 1 of the proposed method, a queried music produces an accumulated band energy histogram which is a set of normalized sums of band energies for each frequency bin. This histogram is compared to all of the histograms for the registered music files. The system generates a list of small number of most probable music files among the all of the registered music files. In pass 2, HMM-based search is applied only to candidate music files selected in pass 1. Using the proposed two-pass strategy, we successfully implemented a HMM-based music identification system, which maintains the same level robustness to signal level variations between perceptually identical music files but also produces the identification result very quickly.

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A Highly Robust Audio Fingerprinting System With an Efficient Search Strategy

Cited by 335 publications

References 0 publications

A comparison of extended fingerprint hashing and locality sensitive hashing for binary audio fingerprints

A comparison of extended fingerprint hashing and locality sensitive hashing for binary audio fingerprints

Audio Watermarking Based on Vector Quantization Index Modulation Using Audio Fingerprint

Two‐pass search strategy using accumulated band energy histogram for HMM‐based identification of perceptually identical music

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