Audio Watermarking Scheme Using IMFs and HHT for Forensic Applications

Audio signals and applications are numerous and ubiquitous. Most of these applications especially those on the Internet require authentication and proof(s) of ownership. There are several efficient methods in the literature address these crucial and critical concerns. In this paper, the authors present a new non-blind audio watermarking scheme for forensic audio authentication and proof of ownership. The proposed scheme is based on empirical mode decomposition and Hilbert Haung Transformation (HHT). In this method, the audio signal is decomposed into frames of 1024 sample each. These frames are further decomposed into its several mono-component signals called Intrinsic Mode Functions (IMF). These Intrinsic Mode Functions will serve as the addressee for the watermark. In this research, the chosen watermark is a pseudo random number generated by Matlab-7, which is added to the highest and lowest IMFs of each frame of the decomposed signal. This is done to accommodate for time scale modification attacks as well as MP3 compression respectively. Experimental results show that the watermarked audio signals maintained high fidelity of more than 20 dBs which meets the International Federation of Phonographic Industry requirements. The results also show that the proposed scheme is robust against signal processing attacks such as MP3, time scale modification, and resizing attacks.

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Content Based Audio Watermarking and Retrieval Using Time-Frequency Analysis

10.32920/ryerson.14655894.v1 ◽

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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Histogram-Based Audio Watermarking Against Time-Scale Modification and Cropping Attacks

IEEE Transactions on Multimedia ◽

10.1109/tmm.2007.906580 ◽

2007 ◽

Vol 9 (7) ◽

pp. 1357-1372 ◽

Cited By ~ 104

Author(s):

Shijun Xiang ◽

Jiwu Huang

Keyword(s):

Time Scale ◽

Audio Watermarking ◽

Time Scale Modification

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Evaluation of time-scale modification methods for audio signals on mobile devices with android OS

2014 Proceedings of the 21st International Conference Mixed Design of Integrated Circuits and Systems (MIXDES) ◽

10.1109/mixdes.2014.6872239 ◽

2014 ◽

Author(s):

Michal Wlodarczyk ◽

Przemyslaw Sekalski

Keyword(s):

Mobile Devices ◽

Time Scale ◽

Audio Signals ◽

Android Os ◽

Time Scale Modification

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Time-scale modification of audio signals with combined harmonic and wavelet representations

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing ◽

10.1109/icassp.1997.599669 ◽

2002 ◽

Cited By ~ 15

Author(s):

K.N. Hamdy ◽

A.H. Tewfik ◽

Ting Chen ◽

S. Takagi

Keyword(s):

Time Scale ◽

Audio Signals ◽

Time Scale Modification ◽

Wavelet Representations

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Mel-scale sub-band modelling for perceptually improved time-scale modification of speech and audio signals

2017 Twenty-third National Conference on Communications (NCC) ◽

10.1109/ncc.2017.8077073 ◽

2017 ◽

Author(s):

Neeraj Sharma ◽

Shreepad Potadar ◽

Srikanth Raj Chetupalli ◽

T.V. Sreenivas

Keyword(s):

Time Scale ◽

Audio Signals ◽

Time Scale Modification

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BLIND AUDIO SEPARATION AND CONTENT ANALYSIS IN THE TIME-SCALE DOMAIN

International Journal of Semantic Computing ◽

10.1142/s1793351x07000184 ◽

2007 ◽

Vol 01 (03) ◽

pp. 307-318 ◽

Cited By ~ 1

Author(s):

ATMAN JBARI ◽

ABDELLAH ADIB ◽

DRISS ABOUTAJDINE

Keyword(s):

Time Scale ◽

Wavelet Transformation ◽

High Efficiency ◽

Audio Signal ◽

Scale Space ◽

Audio Signals ◽

Audio Recordings ◽

Orthogonality Constraint ◽

High Order Statistics ◽

Source Signals

In this paper, we address the problem of Blind Audio Separation (BAS) by content evaluation of audio signals in the Time-Scale domain. Most of the proposed techniques rely on independence or at least uncorrelation assumption of the source signals exploiting mutual information or second/high order statistics. Here, we present a new algorithm, for instantaneous mixture, that considers only different time-scale source signature properties. Our approach lies in wavelet transformation advantages and proposes for this a new representation; Spatial Time Scale Distributions (STSD), to characterize energy and interference of the observed data. The BAS will be allowed by joint diagonalization, without a prior orthogonality constraint, of a set of selected diagonal STSD matrices. Several criteria will be proposed, in the transformed time-scale space, to assess the separated audio signal contents. We describe the logistics of the separation and the content rating, thus an exemplary implementation on synthetic signals and real audio recordings show the high efficiency of the proposed technique to restore the audio signal contents.

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Time-Scale Modification of Audio Signals Using Enhanced WSOLA With Management of Transients

IEEE Transactions on Audio Speech and Language Processing ◽

10.1109/tasl.2007.909444 ◽

2008 ◽

Vol 16 (1) ◽

pp. 106-115 ◽

Cited By ~ 26

Author(s):

Shahaf Grofit ◽

Yizhar Lavner

Keyword(s):

Time Scale ◽

Audio Signals ◽

Time Scale Modification

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Audio watermarking robust against time-scale modification and MP3 compression

Signal Processing ◽

10.1016/j.sigpro.2008.03.019 ◽

2008 ◽

Vol 88 (10) ◽

pp. 2372-2387 ◽

Cited By ~ 68

Author(s):

Shijun Xiang ◽

Hyoung Joong Kim ◽

Jiwu Huang

Keyword(s):

Time Scale ◽

Audio Watermarking ◽

Time Scale Modification ◽

Mp3 Compression

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Frame-synchronous Blind Audio Watermarking for Tamper Proofing and Self-Recovery

Advances in Technology Innovation ◽

10.46604/aiti.2020.4138 ◽

2020 ◽

Vol 5 (1) ◽

pp. 18-32

Author(s):

Hwai-Tsu Hu ◽

Ying-Hsiang Lu

Keyword(s):

Audio Signal ◽

Audio Watermarking ◽

Frame Synchronization ◽

Content Authentication ◽

Blind Audio Watermarking ◽

Synchronization Mechanism ◽

Related Data ◽

Perceptual Evaluation ◽

Reed Solomon Code ◽

Watermarking Scheme

This paper presents a lifting wavelet transform (LWT)-based blind audio watermarking scheme designed for tampering detection and self-recovery. Following 3-level LWT decomposition of a host audio, the coefficients in selected subbands are first partitioned into frames for watermarking. To suit different purposes of the watermarking applications, binary information is packed into two groups: frame-related data are embedded in the approximation subband using rational dither modulation; the source-channel coded bit sequence of the host audio is hidden inside the 2nd and 3rd -detail subbands using 2N-ary adaptive quantization index modulation. The frame-related data consists of a synchronization code used for frame alignment and a composite message gathered from four adjacent frames for content authentication. To endow the proposed watermarking scheme with a self-recovering capability, we resort to hashing comparison to identify tampered frames and adopt a Reed–Solomon code to correct symbol errors. The experiment results indicate that the proposed watermarking scheme can accurately locate and recover the tampered regions of the audio signal. The incorporation of the frame synchronization mechanism enables the proposed scheme to resist against cropping and replacement attacks, all of which were unsolvable by previous watermarking schemes. Furthermore, as revealed by the perceptual evaluation of audio quality measures, the quality degradation caused by watermark embedding is merely minor. With all the aforementioned merits, the proposed scheme can find various applications for ownership protection and content authentication.

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