A low-complexity audio fingerprinting technique for embedded applications

Plapous, Cyril; Berrani, Sid-Ahmed; Besset, Benoit; Rault, Jean-Bernard

doi:10.1007/s11042-017-4505-4

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…ey then form a fully automated 3D pixel segmentation of the pulmonary nodules, blood vessels, thoracic cavity, and lung parenchyma through a unified feature set and classifier and use a probabilistic classifier to determine the relationship between the candidate node and the lung tissue structure. Plapous et al [9] first separated the lung parenchyma from the CT image, calculated the statistical parameters (such as mean, standard deviation, skewness, peak, fifth standard deviation, and sixth central moment) from the segmentation results, and finally obtained better classification results using BP neural network.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis

Shao

Zhou

Wang³

et al. 2021

Journal of Healthcare Engineering

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Different segmentation of lung nodules using the same segmentation algorithm can easily lead to excessive segmentation errors. Therefore, it is necessary to design an effective segmentation algorithm to improve image segmentation accuracy. Based on the hidden Markov model, this study processed the ultrasound images of pulmonary nodules to improve their diagnostic results. At the same time, this study was combined with the ultrasound image of lung nodules to process the ultrasound images. In addition, this study combines the convex hull algorithm for image processing, uses the improved vector method to repair, improves image recognizability, establishes a reliable feature extraction algorithm, and establishes a comprehensive diagnostic model. Finally, this study designed the test for performance analysis. Through experimental research, it can be seen that the model constructed in this study has certain clinical effects and can provide theoretical reference for subsequent related research.

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Section: Introductionmentioning

confidence: 99%

Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis

Shao

Zhou

Wang³

et al. 2021

Journal of Healthcare Engineering

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“…When querying the speech, the first 20s of the query speech Q is used to extract the audio fingerprint hQ by the audio fingerprint method based on feature dimension reduction and feature combination, calculating the bit error rate (BER) in the linear index table by the normalized Hamming distance between the audio fingerprint and hx in the audio fingerprint library. The normalized Hamming distance formula is shown in (5).…”

Section: Audio Fingerprint Retrievalmentioning

confidence: 99%

“…For examples, Chu et al [4] proposed an energy band calculation method based on the peak, which improves the robustness of audio fingerprints and has good robustness to volume changes and amplitude changes. Plapous et al [5] used the IIR filter instead of the Fourier transform to extract audio fingerprints, and conducted down-sampling on the extracted fingerprints, which effectively improved the retrieval speed, but with poor robustness to noise. Chen et al [6] proposed an improved Philips fingerprint based on wavelet transform, which can realize the accurate retrieval of 1s speech segments and have high robustness to the retrieval of short speech segments.…”

Section: Introductionmentioning

confidence: 99%

Audio Fingerprint Retrieval Method Based on Feature Dimension Reduction and Feature Combination

2021

KSII TIIS

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In order to solve the problems of the existing audio fingerprint method when extracting audio fingerprints from long speech segments, such as too large fingerprint dimension, poor robustness, and low retrieval accuracy and efficiency, a robust audio fingerprint retrieval method based on feature dimension reduction and feature combination is proposed. Firstly, the Mel-frequency cepstral coefficient (MFCC) and linear prediction cepstrum coefficient (LPCC) of the original speech are extracted respectively, and the MFCC feature matrix and LPCC feature matrix are combined. Secondly, the feature dimension reduction method based on information entropy is used for column dimension reduction, and the feature matrix after dimension reduction is used for row dimension reduction based on energy feature dimension reduction method. Finally, the audio fingerprint is constructed by using the feature combination matrix after dimension reduction. When speech's user retrieval, the normalized Hamming distance algorithm is used for matching retrieval. Experiment results show that the proposed method has smaller audio fingerprint dimension and better robustness for long speech segments, and has higher retrieval efficiency while maintaining a higher recall rate and precision rate.

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“…This indicates that it is relevant and still relevant today. A recent study, for example, improved the system by replacing the FFT with a filter bank (Plapous et al, 2017). Another study (Coover and Han, 2014) improved its robustness against noise.…”

Section: Replicating An Acoustic Fingerprinting Systemmentioning

confidence: 99%

A Case for Reproducibility in MIR: Replication of ‘A Highly Robust Audio Fingerprinting System’

Six

Bressan

Leman

2018

Transactions of the International Society for Music Information Retrieval

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Claims made in many Music Information Retrieval (MIR) publications are hard to verify due to the fact that (i) often only a textual description is made available and code remains unpublished-leaving many implementation issues uncovered; (ii) copyrights on music limit the sharing of datasets; and (iii) incentives to put effort into reproducible research-publishing and documenting code and specifics on data-is lacking. In this article the problems around reproducibility are illustrated by replicating an MIR work. The system and evaluation described in 'A Highly Robust Audio Fingerprinting System' is replicated as closely as possible. The replication is done with several goals in mind: to describe difficulties in replicating the work and subsequently reflect on guidelines around reproducible research. Added contributions are the verification of the reported work, a publicly available implementation and an evaluation method that is reproducible.

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A low-complexity audio fingerprinting technique for embedded applications

Cited by 4 publications

References 9 publications

Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis

Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis

Audio Fingerprint Retrieval Method Based on Feature Dimension Reduction and Feature Combination

A Case for Reproducibility in MIR: Replication of ‘A Highly Robust Audio Fingerprinting System’

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