Perceptual Audio Hashing Functions

Özer, Hamza; Sankur, B.; Memon, Nasir; Anarım, Emin

doi:10.1155/asp.2005.1780

Cited by 40 publications

(32 citation statements)

References 17 publications

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“…For speech signals, the first 13 cepstrum coefficients are often utilized [28]; hence, we considered first 13 MFCC features for the proposed CBCD task.…”

Section: Mfccs Extractionmentioning

confidence: 99%

A novel framework for CBCD using integrated color and acoustic features

Roopalakshmi¹

2014

Int J Multimed Info Retr

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Most studies in content-based video copy detection (CBCD) concentrate on visual signatures, while only very few efforts are made to exploit audio features. The audio data, if present, is an essential source of a video; hence, the integration of visual-acoustic fingerprints significantly improves the copy detection performance. Based on this aspect, we propose a new framework, which jointly employs color-based visual features and audio fingerprints for detecting the duplicate videos. The proposed framework incorporates three stages: First, a novel visual fingerprint based on spatio-temporal dominant color features is generated; Second, mel-frequency cepstral coefficients are extracted and compactly represented as acoustic signatures; Third, the resultant multimodal signatures are jointly used for the CBCD task, by employing combination rule and weighting strategies. The results of experiments on TRECVID 2008 and 2009 datasets, demonstrate the improved efficiency of the proposed framework compared to the reference methods against a wide range of video transformations.

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“…For speech signals, the first 13 cepstrum coefficients are often utilized [28]; hence, we considered first 13 MFCC features for the proposed CBCD task.…”

Section: Mfccs Extractionmentioning

confidence: 99%

A novel framework for CBCD using integrated color and acoustic features

Roopalakshmi¹

2014

Int J Multimed Info Retr

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“…Given this, the proposed algorithm firstly performs the intensity-loudness transform (ILT) to the speech signal after preprocessing. The nonlinear relationships between loudness L and intensity I are shown as in (4).…”

Section: B Wavelet Decompositionmentioning

confidence: 99%

“…The features extracted are mainly that logarithmic cepstrum coefficient [4], linear spectrum frequencies [5], MFCCs [6] and LPCCs [7]. Other feature extraction methods are also widely adopted, such as Hilbert transform spectrum estimation [8], temporal modulation normalization [9] and bark-bands energy [10], etc.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Time-Frequency Domain Speech Perceptual Hashing Authentication Algorithm Based on Discrete Wavelet Transform

Zhang

Xing

Yibo

et al. 2014

2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing

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According to the situation that speech authentication algorithms are not appropriated for real-time speech content authentication, a novel speech perceptual hashing authentication algorithm based on discrete wavelet transform with combination of time-frequency domain features was proposed. Firstly, by discrete wavelet transform (DWT), a new signal in frequency domain is generated from the original speech signal after pre-processing and intensity-loudness transform (ILT). Next, the algorithm partitions low frequency wavelet decomposition coefficients into equal-sized and nonoverlapping blocks, and then computes logarithmic short-time energy of each block to obtain speech signal's features in frequency domain. Finally, combining with spectral flux features (SFF) of speech signal in time domain, a ternary perceptual hashing sequence is created. Experiment results show that ternary form is better to stand for hashing digest than binary form; the proposed algorithm has a good robustness against content preserving operations, discrimination, compactness and high efficiency, and detects the tamper localization as well.

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“…This algorithm does not reflect the distortions related to compression, especially at medium and high bit rates. Özer et al use periodicity estimators and a singular value decomposition of the Mel frequency cepstrum coefficient (MFCC) matrix [3]. Sukkittanon and Atlas propose frequency modulation features [4].…”

Section: A Systems That Use Features Based On a Single Bandmentioning

confidence: 99%

Distortion Estimation in Compressed Music Using Only Audio Fingerprints

Doets

Lagendijk

2008

IEEE Trans. Audio Speech Lang. Process.

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Abstract-An audio fingerprint is a compact yet very robust representation of the perceptually relevant parts of an audio signal. It can be used for content-based audio identification, even when the audio is severely distorted. Audio compression changes the fingerprint slightly. We show that these small fingerprint differences due to compression can be used to estimate the signal-to-noise ratio (SNR) of the compressed audio file compared to the original. This is a useful content-based distortion estimate, when the original, uncompressed audio file is unavailable. The method uses the audio fingerprints only. For stochastic signals distorted by additive noise, an analytical expression is obtained for the average fingerprint difference as function of the SNR level. This model is based on an analysis of the Philips robust hash (PRH) algorithm. We show that for uncorrelated signals, the bit error rate (BER) is approximately inversely proportional to the square root of the SNR of the signal. This model is extended to correlated signals and music. For an experimental verification of our proposed model, we divide the field of audio fingerprinting algorithms into three categories. From each category, we select an algorithm that is representative for that category. Experiments show that the behavior predicted by the stochastic model for the PRH also holds for the two other algorithms.

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Perceptual Audio Hashing Functions

Cited by 40 publications

References 17 publications

A novel framework for CBCD using integrated color and acoustic features

A novel framework for CBCD using integrated color and acoustic features

An Efficient Time-Frequency Domain Speech Perceptual Hashing Authentication Algorithm Based on Discrete Wavelet Transform

Distortion Estimation in Compressed Music Using Only Audio Fingerprints

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