Audio fingerprinting robust against reverberation and noise based on quantification of sinusoidality

Shibuya, Takashi; Abe, Mototsugu; Nishiguchi, Masayuki

doi:10.1109/icme.2013.6607520

Cited by 11 publications

(9 citation statements)

References 7 publications

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“…In [ 28 ], the audio fingerprinting methods proposed has several steps, these are framing, application of FFT or quadratically interpolated FFT (QIFFT), time averaging, peak detection, quadratic interpolation, sinusoidal quantification, frequency-axial discretization, and time-axial warping. A fingerprint that represents the distribution of pseudosinusoidal components in the time-frequency domain is generated, showing results with an accuracy around 96% and precision of 100% [ 28 ].…”

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

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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%

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

“…This section presents the matching techniques and algorithms, including the sliding window method [16] used in all similarity matching processes, the efficient matching algorithm that only compares the samples of two audios at fixed interval, and various thresholds that can speedup matching processes.…”

Section: Matching With Fixed Interval Sampling and Thresholdmentioning

confidence: 99%

An Efficient Cascaded Filtering Retrieval Method for Big Audio Data

Yao

Wang

Niu

2015

2015 IEEE International Conference on Multimedia Big Data

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Fast audio retrieval is crucial for many important applications and yet demanding due to the high dimension nature and increasingly larger volume of audios in the internet. Although audio fingerprinting can greatly reduce its dimension while keeping audio identifiable, the dimension of audio fingerprints is still too high to scale up for big audio data. The tradeoff between the accuracy and the efficiency prevents the further reducing of the dimension of fingerprints. This paper proposes a multi-stage filtering strategy for audio retrieval, with the beginning stages focusing on speed up by using a middle fingerprint with much smaller size to quickly filtering the most likely audios, and the ending stages emphasizing on accuracy by applying an accurate and robust fingerprint on the small set of the most likely audios. A notion called middle fingerprint is devised with considerable small dimension for quickly filtering out most irrelevant audios. A matching algorithm is developed to reduce the computational complexity by comparing the samples at fixed interval of two audios with thresholds. By using the middle fingerprint, audio retrieval can get a speed gain of 12 times on average compared with the Fibonacci Hashing retrieval. By combing the Fibonacci hashing algorithm with the middle filtering retrieval and the matching algorithm, we propose an efficient cascaded filtering retrieval methods, which can further improve the retrieval by 250 times on average. After applying MP3 conversion, resampling, and random shearing, the recall rates of the method are all above 99.47%, and the theoretical accuracy is close to 100%.

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“…This section presents the matching techniques and algorithms, including the sliding window method [15] used in all similarity matching processes, the efficient matching algorithm that only compares the samples of two audios at fixed intervals, and various thresholds that can speed up matching processes.…”

Section: Matching With Fixed Interval Sampling and Thresholdmentioning

confidence: 99%

An Efficient Cascaded Filtering Retrieval Method for Big Audio Data

Yao

Wang

Niu

2015

IEEE Trans. Multimedia

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Fast audio retrieval is crucial for many important applications and yet demanding due to the high dimension nature and increasingly larger volume of audios on the Internet. Although audio fingerprinting can greatly reduce its dimension while keeping audio identifiable, the dimension for audio fingerprints is still too high to scale up for big audio data. The tradeoff between accuracy (measured by precision and recall rate) and efficiency (measured by retrieval time) prevents further reduction in the dimension of fingerprints. This paper shows that a multi-stage filtering strategy can achieve both speedup and high accuracy, with the beginning stages focusing on speedup and the end stage emphasizing accuracy. With this strategy, an efficient cascaded filtering retrieval method is proposed that consists of filtering with Fibonacci hashing, the middle fingerprint, thresholds to quickly select candidate audios, and refining with an accurate and robust fingerprint on the candidate audios. Experiments with 500 000 audios show that the proposed method can achieve a speed gain more than 28 K times that of the Fibonacci hashing retrieval. After applying MP3 conversion, resampling, white noise addition, and background noise addition, the recall rates of the method are all above 99.45%, and the precision is the same as the Philips audio fingerprint, which is close to 100%.Index Terms-Audio middle fingerprint, big audio data, cascade filtering retrieval, content-based retrieval, Philips audio fingerprint.

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Audio fingerprinting robust against reverberation and noise based on quantification of sinusoidality

Cited by 11 publications

References 7 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

An Efficient Cascaded Filtering Retrieval Method for Big Audio Data

An Efficient Cascaded Filtering Retrieval Method for Big Audio Data

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