DeAR: A Deep-learning-based Audio Re-recording Resilient Watermarking

Audio watermarking is widely used for leaking source tracing. The robustness of the watermark determines the traceability of the algorithm. With the development of digital technology, audio re-recording (AR) has become an efficient and covert means to steal secrets. AR process could drastically destroy the watermark signal while preserving the original information. This puts forward a new requirement for audio watermarking at this stage, that is, to be robust to AR distortions. Unfortunately, none of the existing algorithms can effectively resist AR attacks due to the complexity of the AR process. To address this limitation, this paper proposes DeAR, a deep-learning-based audio re-recording resistant watermarking. Inspired by DNN-based image watermarking, we pioneer a deep learning framework for audio carriers, based on which the watermark signal can be effectively embedded and extracted. Meanwhile, in order to resist the AR attack, we delicately analyze the distortions that occurred in the AR process and design the corresponding distortion layer to cooperate with the proposed watermarking framework. Extensive experiments show that the proposed algorithm can resist not only common electronic channel distortions but also AR distortions. Under the premise of high-quality embedding (SNR=25.86dB), in the case of a common re-recording distance (20cm), the algorithm can effectively achieve an average bit recovery accuracy of 98.55%.Comment: Accepted by AAAI202

A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design

Audio fingerprinting, also named as audio hashing, has been well-known as a powerful technique to perform audio identification and synchronization. It basically involves two major steps: fingerprint (voice pattern) design and matching search. While the first step concerns the derivation of a robust and compact audio signature, the second step usually requires knowledge about database and quick-search algorithms. Though this technique offers a wide range of real-world applications, to the best of the authors' knowledge, a comprehensive survey of existing algorithms appeared more than eight years ago. Thus, in this paper, we present a more up-to-date review and, for emphasizing on the audio signal processing aspect, we focus our state-of-the-art survey on the fingerprint design step for which various audio features and their tractable statistical models are discussed.Comment: http://www.iaria.org/conferences2015/PATTERNS15.html ; Seventh International Conferences on Pervasive Patterns and Applications (PATTERNS 2015), Mar 2015, Nice, Franc

Semi fragile audio crypto-watermarking based on sparse sampling with partially decomposed Haar matrix structure

In the recent era the growth of technology is tremendous and at the same time, the misuse of technology is also increasing with an equal scale. Thus the owners have to protect the multimedia data from the malicious and piracy. This has led the researchers to the new era of cryptography and watermarking. In the traditional security algorithm for the audio, the algorithm is implemented on the digital data after the traditional analog to digital conversion. But in this article, we propose the crypto – watermarking algorithm based on sparse sampling to be implemented during the analog to digital conversion process only. The watermark is generated by exploiting the structure of HAAR transform. The performance of the algorithm is tested on various audio signals and the obtained SNR is greater than 30dB and the algorithm results in good robustness against various signal attacks such as echo addition, noise addition, reverberation etc

Implementation of the DSSS method in watermarking digital audio objects

The paper presents the results of implementation in the Matlab environment for watermarking embedder and extractor based on the Direct Sequence Spread Spectrum (DSSS). A block diagram of watermarking system, an analysis of watermarked signal reproduced as well as watermarking system robustness to degrading factors: lossy compression, signal-to-noise ratio (SNR) as well as a change in sampling frequency, were shown

Evaluation of Audio Compression Artifacts

This paper deals with subjective evaluation of audio-coding systems. From this evaluation, it is found that, depending on the type of signal and the algorithm of the audio-coding system, different types of audible errors arise. These errors are called coding artifacts. Although three kinds of artifacts are perceivable in the auditory domain, the author proposes that in the coding domain there is only one common cause for the appearance of the artifact, inefficient tracking of transient-stochastic signals. For this purpose, state-of-the art audio coding systems use a wide range of signal processing techniques, including application of the wavelet transform, which is described here.

A study on number theoretic construction and prediction of two dimensional acoustic diffusers for architectural applications

Thesis (Doctoral)--İzmir Institute of Technology, Architecture, İzmir, 2011Includes bibliographical references (leaves: 72-77)Text in English; Abstract: Turkish and Englishxiv, 172 leavesDefined as the scattering of sound independent from angle, optimum diffusion is very important for the perception of musical sound. For this purpose, Schroeder used mathematical number sequences to propose ʼreflection phase grating diffusersʼ, of two main types: Single plane or one-dimensional (1D) diffusers that scatter sound into a hemi-disc, and two dimensional (2D) diffusers that scatter into a hemisphere to disperse strong specular reflections without removing sound energy from the space, which is the main advantage of these devices. Currently, two methods are used to design 2D diffusers:Product Array and Folding Array Methods. Both are based on number theory and used methodologically in the field of acoustics, producing results that offer limited diffusion characteristics and design solutions for a variety of architectural spaces ranging from concert halls to recording studios where Schroeder diffusers are widely used. This dissertation proposes Distinct Sums Property Method originally devised for watermarking digital images, to construct adoptable 2D diffusers through number theoretical construction and prediction. At first, quadratic residue sequence based on prime number 7 is selected according to its autocorrelation properties as the Fourier transform of good autocorrelation properties gives an even scattered energy distribution. Then Distinct Sums Property Method is applied to construct 2D arrays from this sequence from which well depths and widths are calculated. Third, the aimed scattering and diffusion properties of the modeled 2D diffuser are predicted by Boundary Element Method which gives approximate results in accordance with the measurements based on Audio Engineering Society Standards. Fourth, polar responses (i.e. the scattering diagrams for specific angles) in each octave band frequency are obtained. Finally, predicted diffusion coefficients for uniform scattering are calculated and compared to the reference flat surfaceʼs coefficients and previous studieʼs results

Real-time Loudspeaker Distance Estimation with Stereo Audio

Knowledge on how a number of loudspeakers are positioned relative to a listening position can be used to enhance the listening experience. Usually, these loudspeaker positions are estimated using calibration signals, either audible or psycho-acoustically hidden inside the desired audio signal. In this paper, we propose to use the desired audio signal instead. Specifically, we treat the case of estimating the distance between two loudspeakers playing back a stereo music or speech signal. In this connection, we develop a real-time maximum likelihood estimator and demonstrate that it has a variance in the millimetre range in a real environment for even a modest sampling frequency