Robust audio retrieval method based on anti‐noise fingerprinting and segmental matching

Zhang, Xueshuai; Ge, Zhan; Wang, Wenchao; Zhang, Pengyuan; Yan, Yonghong

doi:10.1049/el.2019.3086

Cited by 3 publications

(15 citation statements)

References 10 publications

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“…The proposed method using FFMAP is utilized effectively to search manipulated audio data. Previous studies extracted fingerprints using spectral peaks [16][17][18][19][20][21][22], but these were affected by the characteristics of the spectral peaks to change irregularly according to manipulations. Utilizing the fundamental frequency, the proposed method captured and characterized the essence of the audio content.…”

Section: Discussionmentioning

confidence: 99%

“…We will take this as our reference method in the present paper, because it is the latest publication on this topic, and it reports high precision results for a certain range of speed and noise manipulation. Most audio fingerprinting algorithms applied approaches to extract audio fingerprint from spectrograms [16][17][18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

“…They showed that this approach is computationally efficient, and delivers high identification accuracy through experiments. Zhang et al (2020) [21] proposed an audio retrieval method to modify both the fingerprinting and the matching to improve robustness against noise interference. The authors obtained a specific fingerprint from the intrinsic silent segments in both template and test audio in order to reduce the random bit errors caused by the noise.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Robust Audio Fingerprinting Using a New Hashing Method

2020

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To enhance the tracking performance of illegal audio copies, we introduce a robust audio fingerprinting method against various attacks in this paper. Most audio fingerprints consist of the information in the frequency band of audio. These fingerprinting methods may lose the uniqueness of the audio fingerprint by irregular movement such as an attack with pitch value changes. The proposed fingerprint method in a fundamental frequency band makes up for the weakness of existing methods generated from frequency domain. Using the geometrical property of the proposed method, a new hashing method is employed in the similarity calculation process to compare the audio contents. In order to prove the validity of proposed algorithm, we experiment for six environments such as tempo, pitch, speed modification, noise addition, low pass filter and high pass filter. The proposed method shows the highest level of performance in most experimental environments. Especially, with respect to the tempo, pitch, and speed manipulation experiments, the proposed method archives the precision rate of the range from 95 to 100% according to the degree of manipulation, which compares favorably with the precision rate obtained by traditional approaches, and yields a precision rate between 85 and 100% in noise addition and filtering experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Robust Audio Fingerprinting Using a New Hashing Method

2020

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“…In terms of robustness analysis, this experiment uses the feature dimension reduction algorithms of Section 3.2.2 and Section 3.2.3 to extract MFCC feature and LPCC feature of the original speech to construct audio fingerprint, this paper compares the audio fingerprint based on the combined features with the audio fingerprints of two features and the existing audio fingerprint methods of [6,7,22,23,25,27], where [6,7] is an improved method based on Shazam fingerprint, [22,23,25,27] is an improved method based on Philips fingerprint. As shown in Table 1, the combined feature is more robust than MFCC and LPCC that under the same feature dimension reduction process.…”

Section: Robustness and Retrieval Performance Analysismentioning

confidence: 99%

“…Yao et al [24] proposed an audio fingerprint retrieval method based on the turning point alignment fingerprint matching algorithm, this scheme can effectively enhance the anti-time scaling ability and improve the retrieval accuracy. Zhang et al [25] divided the audio fingerprint into multiple sub-fingerprints by distinguishing between silent and voiced segments, and improved it in the audio fingerprint extraction stage and matching stage, which is highly robust to noisy environments. Liang et al [26] proposed an audio fingerprint algorithm based on double fingerprint recognition of short speech segments, and constructed two groups of audio fingerprints for retrieval and matching through Wallish transform and threshold determination of spectrum map, which improved robustness to a certain extent.…”

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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Anime Audio Retrieval Based on Audio Separation and Feature Recognition

Li,

Xu,

Jin

2024

International Journal of Intelligent Systems

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This paper proposes an anime audio retrieval method based on audio separation and feature recognition techniques, aiming to help users conveniently locate their desired audio segments and enhance the overall user experience. Additionally, by establishing an audio fingerprint database and a corresponding copyright information management system, it becomes possible to track and manage the audio content within anime, effectively preventing piracy and unauthorized use, thereby improving the management and protection of audio resources. Traditional methods for anime audio feature recognition suffer from issues like low efficiency and subjective factors. In contrast, the proposed approach overcomes these limitations by automatically separating and extracting audio fingerprints from different audio sources within anime and creating an anime audio fingerprint database for fast retrieval. The paper utilizes an improved audio separation model based on the efficient channel attention mechanism to separate the anime audio. Subsequently, feature recognition is performed on the separated anime audio, employing a contrastive learning-based audio fingerprint retrieval method for anime audio fingerprinting. Experimental results demonstrate that the proposed algorithm effectively alleviates the issue of poor audio separation performance in anime audio, while also improving retrieval efficiency and accuracy, meeting the demands for anime audio content retrieval.

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Robust audio retrieval method based on anti‐noise fingerprinting and segmental matching

Cited by 3 publications

References 10 publications

A Robust Audio Fingerprinting Using a New Hashing Method

A Robust Audio Fingerprinting Using a New Hashing Method

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

Anime Audio Retrieval Based on Audio Separation and Feature Recognition

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