Audio Fingerprinting with Higher Matching Depth at Reduced Computational Complexity

Kamesh, V; Pampana, Nagarjuna; Sinha, Mohit; Bandopadhaya, Shuvabrata

doi:10.1109/icrieece44171.2018.9009156

Cited by 1 publication

(2 citation statements)

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“…In order to further test the retrieval performance of audio fingerprints, the proposed method uses F-score index to evaluate the audio fingerprints and obtain the comparison data shown in Table 2. The calculation method of F-score index is shown in (8).…”

Section: Robustness and Retrieval Performance Analysismentioning

confidence: 99%

“…Sun et al [7] optimized the Shazam fingerprint, proposed a method based on the dynamic region feature peak pair selection, which improved the accuracy of audio fingerprint retrieval. Kamesh et al [8] selected peaks in the alternate time periods of the spectrogram to extract audio fingerprints, and sorted the amplitudes of the time periods in descending order to increase the frequency matching depth, effectively reducing the computational complexity and improving the retrieval efficiency and retrieval accuracy. Sun et al [9] proposed an efficient audio fingerprint retrieval method based on subband spectral centroids, set seed segments to select subbands that need to extract features, extracts audio fingerprints based on subband spectral centroids, and set a hit count threshold during the retrieval phase, improved the recall rate and precision rate, but the query index time is longer.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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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Section: Robustness and Retrieval Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%