Ing Yann Soon

Blind and robust audio watermarking scheme based on SVD-DCT

Singular value decomposition (SVD) is a new and important transform technique in robust digital watermarking due to its different properties from the traditional transforms such as Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). In this paper, we propose a new, blind and robust audio watermarking scheme based on SVD-DCT with the synchronization code technique. We embed a binary watermark into the high-frequency band of the SVD-DCT block blindly. Chaotic sequence is adopted as the synchronization code and inserted into the host signal. Experimental results show that the proposed watermarking method is comparable to, if not, better than SVD based method and several selected typical audio watermarking methods, even in the presence of various common signal processing attacks.

Noisy speech enhancement using discrete cosine transform

Abstract This paper illustrates the advantages of using the Discrete Cosine Transform (DCT) as compared to the standard Discrete Fourier Transform (DFT) for the purpose of removing noise embedded in a speech signal. The derivation of the Minimum Mean Square Error (MMSE) filter based on the statistical modelling of the DCT coefficients is shown. Also shown is the derivation of an over-attenuation factor based on the fact that speech energy is not always present in the noisy signal at all times or in all coefficients. This over-attenuation factor is useful in suppressing any musical residual noise which may be present. The proposed methods are evaluated against the noise reduction filter proposed by Y. Ephraim and D. Malah (1984), using both Gaussian distributed white noise as well as recorded fan noise, with favourable results.

A robust audio watermarking scheme based on lifting wavelet transform and singular value decomposition

In this paper, a new and robust audio watermarking scheme based on lifting wavelet transform (LWT) and singular value decomposition (SVD) is proposed. Specifically, the watermark data is inserted in the LWT coefficients of the low frequency subband taking advantage of SVD and quantization index modulation (QIM). The use of QIM renders our scheme blind in nature. Furthermore, the synchronization code technique is also integrated with hybrid LWT-SVD audio watermarking. Experimental results demonstrate that the proposed LWT-SVD method is not only robust to both general signal processing and desynchronization attacks but also outperform the selected previous studies.

An algorithm for mixing matrix estimation in instantaneous blind source separation

Sparsity of signals in the frequency domain is widely used for blind source separation (BSS) when the number of sources is more than the number of mixtures (underdetermined BSS). In this paper we propose a simple algorithm for detection of points in the time-frequency (TF) plane of the instantaneous mixtures where only single source contributions occur. Samples at these points in the TF plane can be used for the mixing matrix estimation. The proposed algorithm identifies the single-source-points (SSPs) by comparing the absolute directions of the real and imaginary parts of the Fourier transform coefficient vectors of the mixed signals. Finally, the SSPs so obtained are clustered using the hierarchical clustering algorithm for the estimation of the mixing matrix. The proposed idea for the SSP identification is simpler than the previously reported algorithms.

Underdetermined Convolutive Blind Source Separation via Time–Frequency Masking

In this paper, we consider the problem of separation of unknown number of sources from their underdetermined convolutive mixtures via time-frequency (TF) masking. We propose two algorithms, one for the estimation of the masks which are to be applied to the mixture in the TF domain for the separation of signals in the frequency domain, and the other for solving the permutation problem. The algorithm for mask estimation is based on the concept of angles in complex vector space. Unlike the previously reported methods, the algorithm does not require any estimation of the mixing matrix or the source positions for mask estimation. The algorithm clusters the mixture samples in the TF domain based on the Hermitian angle between the sample vector and a reference vector using the well known k -means or fuzzy c -means clustering algorithms. The membership functions so obtained from the clustering algorithms are directly used as the masks. The algorithm for solving the permutation problem clusters the estimated masks by using k-means clustering of small groups of nearby masks with overlap. The effectiveness of the algorithm in separating the sources, including collinear sources, from their underdetermined convolutive mixtures obtained in a real room environment, is demonstrated.

Robust SVD-Based Audio Watermarking Scheme With Differential Evolution Optimization

In this paper, a robust audio watermarking scheme based on singular value decomposition (SVD) and differential evolution (DE) using dither modulation (DM) quantization algorithm is proposed. Two novel SVD-based algorithms, lifting wavelet transform (LWT)-discrete cosine transform (DCT)-SVD and discrete wavelet transform (DWT)-DCT-SVD, are developed for audio copyright protection. In our method, LWT\DWT is first applied to decompose the host signal and obtain the corresponding approximate coefficients followed by DCT to take advantage of “energy compaction” property. SVD is further performed to acquire the singular values and enhance the robustness of the scheme. The adaptive DM quantization is adopted to quantize the singular values and embed the watermark. To withstand desynchronization attacks, synchronization code is inserted using audio statistical characteristics. Furthermore, the conflicting problem of robustness and imperceptibility is effectively resolved by the DE optimization. Simulation results demonstrate that both the LWT-DCT-SVD and DWT-DCT-SVD methods not only have good imperceptibility performance, but also resist general signal processing, hybrid and desynchronization attacks. Compared with the previous DWT-DCT, support vector regression (SVR)-DWT-DCT and DWT-SVD methods, our method obtains more robustness against the selected attacks.

Speech enhancement using 2-D Fourier transform

This paper presents an innovative way of using the two-dimensional (2-D) Fourier transform for speech enhancement. The blocking and windowing of the speech data for the 2-D Fourier transform are explained in detail. Several techniques of filtering in the 2-D Fourier transform domain are also proposed. They include magnitude spectral subtraction, 2-D Wiener filtering as well as a hybrid filter which effectively combines the one-dimensional (1-D) Wiener filter with the 2-D Wiener filter. The proposed hybrid filter compares favorably against other techniques using an objective test.

A spectral filtering method based on hybrid wiener filters for speech enhancement

It is well known that speech enhancement using spectral filtering will result in residual noise. Residual noise which is musical in nature is very annoying to human listeners. Many speech enhancement approaches assume that the transform coefficients are independent of one another and can thus be attenuated separately, thereby ignoring the correlations that exist between different time frames and within each frame. This paper, proposes a single channel speech enhancement system which exploits such correlations between the different time frames to further reduce residual noise. Unlike other 2D speech enhancement techniques which apply a post-processor after some classical algorithms such as spectral subtraction, the proposed approach uses a hybrid Wiener spectrogram filter (HWSF) for effective noise reduction, followed by a multi-blade post-processor which exploits the 2D features of the spectrogram to preserve the speech quality and to further reduce the residual noise. This results in pleasant sounding speech for human listeners. Spectrogram comparisons show that in the proposed scheme, musical noise is significantly reduced. The effectiveness of the proposed algorithm is further confirmed through objective assessments and informal subjective listening tests.

Wavelet for speech denoising

This paper presents the use of the wavelet transform for noise reduction in noisy speech signals. The use of different wavelets and different orders have been evaluated for their suitability as a transform for speech noise removal. The wavelets evaluated are the biorthogonal wavelets, Daubechies wavelets, coiflets as well as symlets. Also two different means of filtering the noise in the transformed coefficients are presented. The first method is based on magnitude subtraction while the second method is based on the Wiener filter with a priori signal to noise ratio estimation.

Enhanced Itakura measure incorporating masking properties of human auditory system

A new enhanced Itakura (E-Itakura) speech distortion measure is proposed in this paper. It incorporates masking properties of the human auditory system into the original Itakura measure. Inaudible noise components masked by speech signals are excluded from the calculation of the E-Itakura measure, while the intrinsic advantage of the Itakura measure is retained. The proposed new measure has been compared with the original Itakura distortion, frequency-weighted Itakura spectral distortion, cepstral distance and Bark spectral distortion measures. The comparison results show that the correlation between the original Itakura measure with speech quality has been improved from 0.73 to 0.89 with the incorporation of the enhancement feature, and that the E-Itakura measure offers a more consistent indication of the subjective quality of speech.