Multi-Subspace Echo Hiding Based on Time-Frequency Similarities of Audio Signals

Wang, Shengbei; Yuan, Weitao; Unoki, Masashi

doi:10.1109/taslp.2020.3013785

Cited by 16 publications

(6 citation statements)

References 24 publications

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“…The introduced score will be employed as an evaluation metric to select the optimal dimension of D (n) , which will be associated with the highest orthogonality degree. In (15), we maximize f b (A) while minimizing f w (A) leading to maximizing f (A). In DSSC, its optimization process requires only the proper selection of the dimension d of D. We can achieve quasiorthogonality between the bioacoustic subspaces by generating the appropriate D. Formally, we can obtain D as follows:…”

Section: F Orthogonality Degree Between Biacoustic Subspacesmentioning

confidence: 99%

“…Labeled data of some species may be scarce or may not even be accessible (e.g., data from endangered species) due to the difficulty of recording them, demanding training under small-scale datasets [5], [6]. Recently, new bioacoustics methods have emerged based on the subspace analysis theory of the autocorrelation matrix to circumvent the issues mentioned above [14], [15]. Subspacebased methods group signals into clusters called subspaces.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, new bioacoustics methods have emerged based on the subspace analysis theory of the autocorrelation matrix to circumvent the issues mentioned above [14], [15]. Subspacebased methods group signals into clusters called subspaces.…”

Section: Introductionmentioning

confidence: 99%

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Discriminative Singular Spectrum Analysis for Bioacoustic Classification

Gatto¹,

Santos²,

Colonna³

et al. 2020

Interspeech 2020

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Automatic analysis of bioacoustic signals is a fundamental tool to evaluate the vitality of our planet. Frogs and bees, for instance, may act like biological sensors providing information about environmental changes. This task is fundamental for ecological monitoring and includes many challenges such as nonuniform signal length processing, degraded target signal due to environmental noise, and the scarcity of the labeled samples for training machine learning. To tackle these challenges, we present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently. The proposed classifier does not require a large amount of training data and handles nonuniform signal length natively. Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces generated by applying Singular Spectrum Analysis (SSA). Then, a subspace is designed to expose discriminative features. The proposed model shares end-to-end capabilities, which is desirable in modern machine learning systems. This formulation provides a segmentation-free and noise-tolerant approach to represent and classify bioacoustic signals and a highly compact signal descriptor inherited from SSA. The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species. Experimental results on three bioacoustic datasets have shown the competitive performance of the proposed method compared to commonly employed methods for bioacoustics signal classification in terms of accuracy.

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Section: F Orthogonality Degree Between Biacoustic Subspacesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discriminative Singular Spectrum Analysis for Bioacoustic Classification

Gatto¹,

Santos²,

Colonna³

et al. 2020

Interspeech 2020

View full text Add to dashboard Cite

show abstract

“…In the past, various approaches, including quantization index modulation (QIM) [18,19], spread spectrum (SS) [20][21][22], echo-hiding [23][24][25], and patchwork [4,26], had been 2 VOLUME XX, 2021 developed to deliver effective blind audio watermarking. The embedded objects are either random sequences or binary information.…”

Section: Introductionmentioning

confidence: 99%

FFT-Based Dual-Mode Blind Watermarking for Hiding Binary Logos and Color Images in Audio

Lee

2023

IEEE Access

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Watermarking technology is a feasible approach to prevent copyright infringement. The practicality of the watermark itself affects the acceptance and penetration of relevant applications. In this study, a dual-mode FFT-based audio watermarking framework is devised to achieve both binary and numeric data hiding concurrently using respective modulations. We demonstrate: (1) the adaptive magnitude modulation (AMM) applied to large-magnitude FFT coefficients is competent to deliver efficient and effective binary watermarking at a rate of 678.46 bits per second; (2) the phase modulation (PM) in charge of the embedding of pixel values directly into medium-size FFT coefficients can operate at a rate of 508.85 numbers per second. With a tactical selection of embedding positions and strengths, both the FFT-based AMM and PM exhibit exceptional robustness against commonly encountered attacks. According to the perceptual evaluation of audio quality (PEAQ) metric, the resulting audio quality remains indistinguishable from the original audio, even at the aforementioned high payload capacity. INDEX TERMSDual-mode blind audio watermarking; Adaptive magnitude modulation; Phase modulation; Color image watermark.

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“…Several methods proposed previously involved in inserting watermarks in the time domain [11–21] or frequency domain [3–10, 22–25]. In this study, we focus on the frequency domain especially the wavelet domain.…”

Section: Introductionmentioning

confidence: 99%

Digital audio signal watermarking using minimum‐energy scaling optimisation in the wavelet domain

Hsu

Tu²,

Yang³

et al. 2020

IET signal process.

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This work's contributions include three innovative concepts, an improved model, two‐stage Lagrange principle, and minimum‐energy scaling optimisation, for quantisation audio watermarking in the wavelet domain. First, discrete wavelet transform (DWT) multi‐coefficients quantisation, composed of arbitrary scaling on the lowest DWT coefficients, and the group‐based signal‐to‐noise ratio (SNR) of these coefficients is connected in a model. Then, the two‐stage Lagrange principle and minimum‐energy approach play two essential roles to obtain the optimal scaling factors. With the proposed scheme, the best fidelity and robustness of embedded audio can be attained and the perceptual evaluation of audio quality (PEAQ) test with an illustration of the relationship between SNR and PEAQ is also performed as well. Simulation results show that each watermarked audio by the proposed method attains a high SNR, good PEAQ, and a low bit error rate (BER). The SNR of most watermarked audios in their method is above 35 or even above 40 and the corresponding subjective difference grade of PEAQ is close to 0. In terms of comparing BER, most of their BER is as low as 2% or less indicating better robustness against many attacks, such as re‐sampling, amplitude scaling, and mp3 compression.

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Multi-Subspace Echo Hiding Based on Time-Frequency Similarities of Audio Signals

Cited by 16 publications

References 24 publications

Discriminative Singular Spectrum Analysis for Bioacoustic Classification

Discriminative Singular Spectrum Analysis for Bioacoustic Classification

FFT-Based Dual-Mode Blind Watermarking for Hiding Binary Logos and Color Images in Audio

Digital audio signal watermarking using minimum‐energy scaling optimisation in the wavelet domain

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