A novel spread spectrum audio watermarking scheme based on time-frequency characteristics

Esmaili, Shahrzad; Krishnan, Sridhar; Raahemifar, Kaamran

doi:10.1109/ccece.2003.1226299

Cited by 7 publications

(6 citation statements)

References 10 publications

(6 reference statements)

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“…In this chapter, we examine a spread spectrum watermarking scheme that inserts a watermark into the audio file using time and frequency characteristics simultaneously [20]. This approach reduces the computational complexity compared to techniques examining time and frequency separately while maintaining transparency.…”

Section: Otivationmentioning

confidence: 99%

Content Based Audio Watermarking and Retrieval Using Time-Frequency Analysis

Esmaili¹

2021

Preprint

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This research focuses on the application of joint time-frequency (TF) analysis for watermarking and classifying different audio signals. Time frequency analysis which originated in the 1930s has often been used to model the non-stationary behaviour of speech and audio signals. By taking into consideration the human auditory system which has many non-linear effects and its masking properties, we can extract efficient features from the TF domain to watermark or classify signals. This novel audio watermarking scheme is based on spread spectrum techniques and uses content-based analysis to detect the instananeous mean frequency (IMF) of the input signal. The watermark is embedded in this perceptually significant region such that it will resist attacks. Audio watermarking offers a solution to data privacy and helps to protect the rights of the artists and copyright holders. Using the IMF, we aim to keep the watermark imperceptible while maximizing its robustness. In this case, 25 bits are embedded and recovered witin a 5 s sample of an audio signal. This scheme has shown to be robust against various signal processing attacks including filtering, MP3 compression, additive moise and resampling with a bit error rate in the range of 0-13%. In addition content-based classification is performed using TF analysis to classify sounds into 6 music groups consisting of rock, classical, folk, jazz and pop. The features that are extracted include entropy, centroid, centroid ratio, bandwidth, silence ratio, energy ratio, frequency location of minimum and maximum energy. Using a database of 143 signals, a set of 10 time-frequncy features are extracted and an accuracy of classification of around 93.0% using regular linear discriminant analysis or 92.3% using leave one out method is achieved.

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Section: Otivationmentioning

confidence: 99%

Content Based Audio Watermarking and Retrieval Using Time-Frequency Analysis

Esmaili¹

2021

Preprint

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“…These methods exploit the weakness of human auditory system to embed the watermark data in the regions of audio signals that human ears are unable to perceive the distortion caused by data hiding process. These methods include phase modulation [1], echo hiding [2], spread spectrum [3], patchwork algorithm [4] and methods based on manipulation of low-frequency components of audio signal in the temporal domain [5] or in the wavelet domain [6,7]. In other group of methods, watermark data is embedded into audio signal by changing middle frequency components in the time domain [8] or in the frequency domain [9].…”

Section: Introductionmentioning

confidence: 99%

Adaptive and Robust Audio watermarking in Wavelet Domain

Pooyan

Delforouzi

2007

Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007)

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This paper presents a robust method of audio watermarking in the wavelet domain. In this method, the watermarked data is encrypted, then combined with a synchronization code and embedded in lowfrequency coefficients of wavelet transform. In this paper two techniques of quantization are employed for data embedding process. The magnitude of quantization step and embedding strength is adaptively determined according to the characteristics of the human auditory system (HAS). Therefore data embedding distortion is imperceptible for human ears. The experimental results show that the proposed watermarking scheme is transparence and very robust against common attacks such as additive Gaussian noise, low-pass filtering, resampling, requantization, shifting, cropping and MPEG compression.

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“…Most of the existing audio watermarking techniques embed the watermarks in the time domain / frequency domain where as there are few techniques which embed the data in cepstrum or compressed domain. Spread Spectrum technique is most popular technique and is used by many researchers in their implementations [2,3,4,5,6,7]. Amplitude scaled Spread Spectrum (SS) sequence is embedded in the host audio signal which can be detected via a correlation technique.…”

Section: Introductionmentioning

confidence: 99%

Adaptive spread spectrum Audio watermarking for indian musical signals by low frequency modification

Patil¹,

Apte²

2009

2009 3rd International Conference on Anti-Counterfeiting, Security, and Identification in Communication

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Spread spectrum Audio watermarking technique based on SNR (signal to noise ratio) is implemented in wavelet-DCT domain. The technique is developed for blind detection of the watermark. To check the robustness of the technique the watermarked signal is passed through different signal processing attacks and is observed that the watermark remains present after all signal processing attacks. The technique is also tested with the subjective listening test and it is confirmed that the there is no perceptible difference between the watermarked signal and the original host audio signal.

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A novel spread spectrum audio watermarking scheme based on time-frequency characteristics

Cited by 7 publications

References 10 publications

Content Based Audio Watermarking and Retrieval Using Time-Frequency Analysis

Content Based Audio Watermarking and Retrieval Using Time-Frequency Analysis

Adaptive and Robust Audio watermarking in Wavelet Domain

Adaptive spread spectrum Audio watermarking for indian musical signals by low frequency modification

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