Time-Frequency Analysis of Digital Audio Watermarking

2008 ◽  
pp. 187-204 ◽  
Author(s):  
Sridhar Krishnan ◽  
Behnaz Ghoraani
Keyword(s):  
Digital Rights Management ◽  
Audio Watermarking ◽  
Digital Audio ◽  
Audio Signals ◽  
Time Frequency ◽  
Digital Multimedia ◽  
Digital Audio Watermarking ◽  
Data Authentication ◽  
Common Signal

In this book chapter, we present an overview of our time-frequency (TF) based audio watermarking methods. First, a motivation on the necessity of data authentication, and an introduction in Digital Rights Management (DRM) to protect digital multimedia contents is presented. TF techniques provide flexible means to analyze non-stationary audio signals. We have explained the joint TF domain for watermark representation, and have employed pattern recognition schemes for watermark detection. In this chapter; we introduce two watermarking methods; embedding non-linear and linear TF signatures as watermarking signatures. Robustness of the proposed methods against common signal manipulations is also studied in this chapter.

Audio Watermarking Through Parametric Synthesis Models

2008 ◽  
pp. 50-81
Author(s):  
Yi-Wen Liu
Keyword(s):  
Binary Data ◽  
Linear Process ◽  
Audio Watermarking ◽  
Digital Audio ◽  
Audio Signals ◽  
Parametric Synthesis ◽  
Digital Audio Watermarking ◽  
Grid Points ◽  
Sound Source Segregation

This chapter promotes the use of parametric synthesis models in digital audio watermarking. It argues that, because human auditory perception is not a linear process, the optimal hiding of binary data in digital audio signals should consider parametric transforms that are generally nonlinear. To support this argument, an audio watermarking algorithm based on aligning frequencies of spectral peaks to grid points is presented as a case study; its robustness is evaluated and benefits are discussed. Toward the end, research directions are suggested, including watermark-aided sound source segregation, cocktail watermarking, and counter-measure against arithmetic collusive attacks.

Introduction to Digital Audio Watermarking

2008 ◽  
pp. 1-10 ◽  
Author(s):  
Nedeljko Cvejic ◽  
Tapio Seppänen
Keyword(s):  
Signal Processing ◽  
Digital Watermarking ◽  
Audio Watermarking ◽  
Digital Audio ◽  
Bit Rate ◽  
Digital Audio Watermarking ◽  
General Overview ◽  
Essential Properties ◽  
Common Signal

This chapter gives a general overview of the audio watermarking fundamental definitions. Audio watermarking algorithms are characterized by five essential properties, namely: perceptual transparency, watermark bit rate, robustness, blind/informed watermark detection and security. Chapter also reviews the most common signal processing manipulations that are frequently applied to the watermarked audio in order to prevent detection of the embedded watermark. Finally, several application areas for digital audio watermarking are presented and advantages of digital watermarking over standard technologies examined.

scholarly journals Development of Algorithm for Audio Watermarking Embedding and Decoding using Patchwork Method under Attacks

2020 ◽  
Vol 9 (3) ◽  
pp. 3289-3294
Keyword(s):  
Signal Processing ◽  
Audio Signal ◽  
Audio Watermarking ◽  
Digital Audio ◽  
Discrete Cosine Transforms ◽  
Digital Multimedia ◽  
Digital Audio Watermarking ◽  
Secured Data ◽  
Number Of Segments ◽  
Made In

To protect digital multimedia content from unauthorized reproduction, digital audio watermarking played crucial role. Audio watermarking for the patchwork method has a relatively good level of perception quality.The challengesbetween security, robustness, and imperceptibility is contemporary area of researchand remains relevant issues. This paper introduces discrete cosine transforms (DCT)-based audio watermarking process using the patchwork method for conventional and advanced signal processing attacks. In the first stage of the watermarking audio signal is divided into an equal number of segments and its sub-segments, and then its coefficients are computed. After eliminating high-frequency related coefficients, remaining coefficients are used to form frame pairs of equal length. Watermarks are embedded in a frame using specific criteria and secured data key.The adjustments are made in such a way that the identification ofWatermarked pairs of DCT frames is done in the decoding process by applying the selection criteria used during the embedding process. From watermarked frames, watermark data is extracted by using a secure data key. The proposed audio watermarking algorithm is implemented and tested under conventional and advance signal processing attacks for robustness, imperceptibility, security, and data payload.

Three Techniques of Digital Audio Watermarking

2008 ◽  
pp. 104-122 ◽  
Author(s):  
Say Wei Foo
Keyword(s):  
Audio Watermarking ◽  
Extraction Technique ◽  
Blind Watermarking ◽  
Digital Audio ◽  
Original Signal ◽  
Time Frequency ◽  
Digital Audio Watermarking ◽  
Peak Point ◽  
Watermarking Scheme

Based on the requirement of watermark recovery, watermarking techniques may be classified under one of three schemes: non-blind watermarking scheme, blind watermarking schemes with and without synchronization information. For the non-blind watermarking scheme, the original signal is required for extracting the watermark and hence only the owner of the original signal will be able to perform the task. For the blind watermarking schemes, the embedded watermark can be extracted even if the original signal is not readily available. Thus, the owner does not have to keep a copy of the original signal. In this chapter, three audio watermarking techniques are described to illustrate the three different schemes. The time-frequency technique belongs to the non-blind watermarking scheme; the multiple-echo hiding technique and the peak-point extraction technique fall under the blind watermarking schemes with and without synchronization information respectively.

scholarly journals A Multi-Level Robust and Perceptually Transparent Blind Audio Watermarking Scheme Using Wavelets

2015 ◽  
Vol 39 (4) ◽  
pp. 529-539 ◽  
Author(s):  
Farooq Husain ◽  
Omar Farooq ◽  
Ekram Khan
Keyword(s):  
Audio Watermarking ◽  
Digital Audio ◽  
Audio Signals ◽  
Blind Audio Watermarking ◽  
Single Level ◽  
Digital Audio Watermarking ◽  
Perceptual Evaluation ◽  
Payload Capacity ◽  
Multi Level ◽  
Watermarking Scheme

Abstract In this paper, a robust and perceptually transparent single-level and multi-level blind audio watermarking scheme using wavelets is proposed. A randomly generated binary sequence is used as a watermark, and wavelet function coding is used to embed the watermark sequence in audio signals. Multi-level watermarking is used to enhance payload capacity and can be used for a different level of security. The robustness of the scheme is evaluated by applying different attacks such as filtering, sampling rate alteration, compression, noise addition, amplitude scaling, and cropping. The simulation results obtained show that the proposed watermarking scheme is resilient to various attacks except cropping. Perceptual transparency of watermark is measured by using Perceptual Evaluation of Audio Quality (PEAQ) basic model of ITU-R (PEAQ ITU-R BS.1387) on Speech Quality Assessing Material (SQAM) given by European Broadcasting Union (EBU). Average Objective Difference Grade (ODG) measured for this method is -0.067 and -0.080 for single-level and multi-level watermarked audio signals, respectively. In the proposed single-level digital audio watermarking scheme, the payload capacity is increased by 19.05% as compared to the single-level Chirp-Based Digital Audio Watermarking (CB-DAWM) scheme.

Audio Watermarking Art of Hiding Copyright Information for Piracy Control

2020 ◽  
Vol 10 (2) ◽  
pp. 276-289
Author(s):  
Tribhuwan Kumar Tewari
Keyword(s):  
Digital Media ◽  
Audio Watermarking ◽  
Digital Music ◽  
Multimedia Content ◽  
Digital Audio ◽  
Digital Multimedia ◽  
Digital Audio Watermarking ◽  
Copyright Information ◽  
The Future ◽  
Illegal Copying

Background & Objective: Revolution in digital multimedia is a boon to music industries, music creators for decades as digital music can be created, stored replicated and transferred easily and efficiently. But digitalization of multimedia becomes a curse when the multimedia content is illegally and freely distributed, shared across the network online or offline. Countering the illegal copying and distribution of digital media is the driving force behind the evolution of copyright protection and digital watermarking techniques. Methods: This paper presents the problem of piracy and the overview of the evolution of different digital audio watermarking techniques in time, transformed and compression domains to counter the problem of music piracy. The limitations of the audio watermarking techniques and the future scope for improvement are also presented. Results & Conclusion: This paper summarizes the evolution of audio watermarking techniques and reviews the existing watermarking techniques applied on audios. The limitations of the audio watermarking techniques and the future scope for improvement are also proposed. Additionally, the preliminaries for audio and brief of the properties which are exploited for watermarking of audio are presented.

scholarly journals Content Based Audio Watermarking and Retrieval Using Time-Frequency Analysis

2021 ◽  
Author(s):  
Shahrzad Esmaili
Keyword(s):  
Frequency Analysis ◽  
Spread Spectrum ◽  
Data Privacy ◽  
Audio Signal ◽  
Maximum Energy ◽  
Audio Watermarking ◽  
Audio Signals ◽  
Time Frequency Analysis ◽  
Linear Discriminant ◽  
Time Frequency

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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