A Robust and Time-Efficient Fingerprinting Model for Musical Audio

Sert, Mustafa; Baykal, Buyurman; Yazıcı, Adnan

doi:10.1109/isce.2006.1689505

Cited by 7 publications

(8 citation statements)

References 10 publications

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Section: Resultsmentioning

confidence: 99%

“…In [ 45 ] is proposed a system for audio fingerprinting that starts with preprocessing and framing of the audio signal. Afterwards, a general feature extraction paradigm, extended with a descriptor based on structural similarity analysis with MPEG-7 Audio Spectrum Flatness (ASF), is applied to the signal [ 45 ]. The last step, before the fingerprint construction, consists of the structural analysis that results only the feature vector of the expressive audio piece [ 45 ].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review

Pires

Santos

Pombo

et al. 2018

Sensors

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An increase in the accuracy of identification of Activities of Daily Living (ADL) is very important for different goals of Enhanced Living Environments and for Ambient Assisted Living (AAL) tasks. This increase may be achieved through identification of the surrounding environment. Although this is usually used to identify the location, ADL recognition can be improved with the identification of the sound in that particular environment. This paper reviews audio fingerprinting techniques that can be used with the acoustic data acquired from mobile devices. A comprehensive literature search was conducted in order to identify relevant English language works aimed at the identification of the environment of ADLs using data acquired with mobile devices, published between 2002 and 2017. In total, 40 studies were analyzed and selected from 115 citations. The results highlight several audio fingerprinting techniques, including Modified discrete cosine transform (MDCT), Mel-frequency cepstrum coefficients (MFCC), Principal Component Analysis (PCA), Fast Fourier Transform (FFT), Gaussian mixture models (GMM), likelihood estimation, logarithmic moduled complex lapped transform (LMCLT), support vector machine (SVM), constant Q transform (CQT), symmetric pairwise boosting (SPB), Philips robust hash (PRH), linear discriminant analysis (LDA) and discrete cosine transform (DCT).

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review

Pires

Santos

Pombo

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…In [2], the square root of the mean energy across the time concatenating the standard deviation of the RMS power is used to form a fingerprint. The MPEG-7 audio descriptors-Audio Spectrum Flatness and Audio Signature are used to form the fingerprint in [3]. And in [4], a two-layer OPCA technique is used to generate the noise-resistant fingerprinting.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we use the weighted MPEG-7 descriptor: Audio Spectrum Flatness [3] [9] to generate our audio feature because the perceptual feature computed using ASF can efficiently characterize the audios and be robust to a variety of audio distortions. Otherwise, we use many effective filters to reduce distortions, especially the noise and speed-change, and make use of two ear process functions in Human Auditory System (HAS) [10] to enhance the property of the audio data.…”

Section: Introductionmentioning

confidence: 99%

A Robust Feature Extraction Algorithm for Audio Fingerprinting

Chen

Huang

2008

Advances in Multimedia Information Processing - PCM 2008

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Abstract. In this paper, we present a new feature extraction algorithm which can generate robust and reliable feature in a fingerprint system. This algorithm is referred to as weighted ASF (WASF). The feature in our algorithm is extracted based on a MPEG-7 descriptor-Audio Spectrum Flatness (ASF) and Human Auditory System (HAS). It also applies several effective filters to improve the feature robustness and uses another MPEG-7 descriptor: Audio Signature (AS) to reduce the feature dimension and increase the feature compactness. The smooth filter bank can efficiently resist the noise distortion in addition to some other common distortions such as sampling rate change and amplitude normalization, while the first order inverse filter can effectively resist the speed-change distortion with 90.1% discrimination for the 5% speed acceleration distortion. This algorithm is tested under several audio distortions: sampling rate change, noise addition, data compression and speedchange and so on. For these distortions, the WASF algorithm can get discrimination more than 90%. The MFCC feature and another MPEG-7 descriptor-Audio spectrum Centroid (ASC) are also considered.

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“…With a few exceptions (like [5]), there are, nevertheless, some shared aspects, which can be summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

On audio recognition performance via robust hashing

Bellettini

Mazzini

2007

2007 International Symposium on Intelligent Signal Processing and Communication Systems

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In recent years, automated audio recognition is arising as a relevant issue. One of the most successful approaches has proved to be the Philips method [1], by Haitsma et al.. Our work exploits this framework in order to allow for a better tuning of the algorithm. In particular, we show that more accurate hashes do not always lead to a better performance, which can then be attained with an inferior computational cost. A simple and efficient search method is also proposed.Index Terms-Audio fingerprint, content based audio identification, music information retrieval.

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A Robust and Time-Efficient Fingerprinting Model for Musical Audio

Cited by 7 publications

References 10 publications

Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review

Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review

A Robust Feature Extraction Algorithm for Audio Fingerprinting

On audio recognition performance via robust hashing

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