Modified Discrete Cosine Transform Research Articles

A Universal Audio Steganalysis Scheme Based on Multiscale Spectrograms and DeepResNet

Given the popularity of audio and video applications, compressed audio has become an important carrier of covert communication on the Internet. Many novel compressed audio steganography schemes have emerged that offer good hiding capability and aural concealment. In this paper, a universal steganalysis scheme called MultiSpecNet is proposed to detect steganography based on multiple embedding domains (advanced audio coding (AAC) and MPEG-1 Audio Layer III (MP3)), which are currently the two most popular compressed audio standards. The basic idea is that modification of either domain by a steganography scheme will change the time-frequency relationship of the audio signal after decoding. The proposed approach adopts the spectrogram as the input feature to extract richer information. DeepResNet is used to learn the distinguishing feature representations, and multiscale spectrograms are used to enrich the feature diversity. The experimental results show that the proposed scheme is effective at detecting different steganography schemes based on the AAC and MP3 embedding domains. The detection accuracy of the proposed scheme is higher than that achieved by other state-of-the-art schemes. Using spectrograms as the input, DeepResNet achieves better performance than schemes using quantized modified discrete cosine transform (MDCT) coefficients and mel-spectrogram, although the quantized MDCT coefficient is the parameter modified by the steganography schemes directly and mel-spectrogram is very popular and effective for general audio signal analysis. To the best of our knowledge, this work is the first audio steganalysis scheme that can detect multiple steganography schemes in both the MP3 and AAC embedding domains. The method proposed in this paper can be extended to audio steganalysis for other codecs or for audio forensics purposes.

Efficient Data Compression of ECG Signal Based on Modified Discrete Cosine Transform

This paper introduced an efficient compression technique that uses the compressive sensing (CS) method to obtain and recover sparse electrocardiography (ECG) signals. The recovery of the signal can be achieved by using sampling rates lower than the Nyquist frequency. A novel analysis was proposed in this paper. To apply CS on ECG signal, the first step is to generate a sparse signal, which can be obtained using Modified Discrete Cosine Transform (MDCT) on the given ECG signal. This transformation is a promising key for other transformations used in this search domain and can be considered as the main contribution of this paper. A small number of wavelet components can describe the ECG signal as related work to obtain a sparse ECG signal. A sensing technique for ECG signal compression, which is a novel area of research, is proposed. ECG signals are introduced randomly between any successive beats of the heart. MIT-BIH database can be represented as the experimental database in this domain of research. The MIT-BIH database consists of various ECG signals involving a patient and standard ECG signals. MATLAB can be considered as the simulation tool used in this work. The proposed method's uniqueness was inspired by the compression ratio (CR) and achieved by MDCT. The performance measurement of the recovered signal was done by calculating the percentage root mean difference (PRD), mean square error (MSE), and peak signal to noise ratio (PSNR) besides the calculation of CR. Finally, the simulation results indicated that this work is one of the most important works in ECG signal compression.

Primary Gastro-Intestinal Lymphoma and Gastro-Intestinal Adenocarcinoma: An Initial Study of CT Texture Analysis as Quantitative Biomarkers for Differentiation.

Background: To explore the potential role of computed tomography (CT) texture analysis and an imaging biomarker in differentiating primary gastro-intestinal lymphoma (PGIL) from gastro-intestinal adenocarcinoma (GIAC). Methods: A total of 131 patients with surgical pathologically PGIL and GIAC were enrolled in this study. Histogram parameters of arterial and venous phases extracted from contrast enhanced modified discrete cosine transform (MDCT) images were compared between PGIL and GIAC by Mann–Whitney U tests. The optimal parameters for differentiating these two groups were obtained through receiver operating characteristic (ROC) curves and the area under the curve (AUC) was calculated. Results: Compared with GIAC, in arterial phase, PGIL had statistically higher 5th, 10th percentiles (p = 0.003 and 0.011) and statistically lower entropy (p = 0.001). In the venous phase, PGIL had statistically lower mean, median, 75th, 90th, 95th percentiles, and entropy (p = 0.036, 0.029, 0.007, 0.001 and 0.001, respectively). For differentiating PGIL from GIAC, V-median + A-5th percentile was an optimal parameter for combined diagnosis (AUC = 0.746, p < 0.0001), and the corresponding sensitivity and specificity were 81.7 and 64.8%, respectively. Conclusion: CT texture analysis could be useful for differential diagnosis of PGIL and GIAC.

A Hybrid Transform for Reduction of Peak to Average Power Ratio in OFDM System

The performance of orthogonal frequency multiplexing (OFDM) communication systems is affected by the peak to average power ratio (PAPR). The research work uses a hybrid transform for reducing this PAPR value. In recent years, OFDM is more prevalent in the research field, particularly in wireless communication. OFDM system is found to have a high PAPR value. This high PAPR leads to nonlinear distortions that result in low power efficiency. Hence there is a need to minimize PAPR. Hadamard transform is a commonly used technique for the reduction of PAPR. However, greater efficiency can be carried out using a hybrid way to reducing the PAPR of the signals applying OFDM. Modified Discrete Cosine Transform (MDCT) combined with nonlinear companding technique, is suggested to minimize PAPR, described as a hybrid transform. It is shown that the hybrid transform achieves a better lessening of PAPR and minimized Bit Error Rate (BER) in the OFDM communication system when compared with the basic OFDM, Hadamard transforms, and Companding technique.

Detection of AAC compression using MDCT-based features and supervised learning

ABSTRACT Audio files are frequent targets of malicious users who seek illegal profit trading with fake-quality content. For increasing the confidence in the integrity of audio files, the detection of fake-quality content is an important task. This paper proposes a method for detecting Advanced Audio Coding (AAC) compression on suspicious WAV files, in which the variance of the Modified Discrete Cosine Transform (MDCT) characterises four compression bitrates: uncompressed, 64 kbps, 128 kbps, and 256 kbps. This scheme takes advantage of the reduction of the variance of the high-frequency MDCT coefficients in compressed signals. Data obtained from MDCT coefficients generate a high-dimensional feature space. Hence, Principal Component Analysis, followed by Linear Discriminant Analysis, is used for projecting the high-dimensional data onto a lower-dimensional space. Besides, six supervised learning algorithms are compared for classifying four compression bitrates. The experiments show that using audio samples with 20 seconds and 1024 MDCT coefficients, an accuracy of 93% is reached with a Bayesian classifier. Collaterally, the detection between uncompressed and compressed signals attains an accuracy of 97% with Multinomial Logistic Regression. In conclusion, the proposed approach can detect previous AAC compression and can be potentially used when it is unfeasible to recover the suspicious signal completely.

Frequency Domain Long-Term Prediction for Low Delay General Audio Coding

In this paper we propose a long-term prediction method for low delay transform domain general audio coders. This Frequency Domain Joint Harmonics Prediction (FDJHP) method operates directly in the Modified Discrete Cosine Transform (MDCT) domain and can enhance the coding efficiency, even under very low frequency resolutions. We compare this new method with state-of-the-art MDCT based methods by analyzing bitrate savings and by a listening test using test signals with strong harmonic components. The results indicate that it outperforms an existing method, which also directly operates in the frequency domain. Additionally, we show how it can be combined with the existing techniques into an adaptive system, where the different methods can complement each other.

The DCT domain sparsity-assisted detection and recovery of impulsively disturbed samples

A sparsity-assisted algorithm for the detection and removal of impulsive disturbances is presented. It is assumed that the uncorrupted signal is sparse or highly concentrated in the discrete cosine transform (DCT) domain or the windowed modified DCT (MDCT) domain. To detect whether a specific signal sample is corrupted by a disturbance, the gradient of the sparsity measure is estimated in the space of the signal samples by varying the values of signal samples. Samples whose variations caused the highest estimated gradient values are considered to be far (dislocated) from the sparsity measure minimum. They are labeled as disturbed and further classified as unavailable. Subsequently, these samples are reconstructed using a compressive sensing reconstruction approach. Numerical results support the presented theory and indicate potential applicability in audio signal processing, particularly in the removal of impulsive disturbances and concealment of unavailable, disturbed or lost data.

Synchrosqueezing Transform Based Powerline Interference Reduction in ECG Recording

This paper presents an adaptive ECG enhancement procedure based on Synchrosqueezing Transform (SST) to eliminate Powerline interference (PLI) from ECG signal. This work also incorporates the principles of modified discrete cosine transform (MDCT) and wiener filter. PLI is a major source of artifacts in the ECG signal which can affect its interpretation. Separating PLI from ECG signal poses a great challenge in the ECG analysis. The existing PLI removal techniques suffer from two major drawbacks such as Mode Mixing, inability to deal with non-stationary characteristics of signal. In this paper, we propose SST based wiener filtering approaches which can overcome the limitation of existing PLI suppression techniques. The proposed approaches undergo three stages of operation: mode decomposition, mode determination and peak restoration to filter out PLI from ECG recording. The mode decomposition uses SST to decompose the corrupted ECG signal into a sum of well separated intrinsic mode functions (IMFs). The objective is to filter out PLI from these IMFs. To do so, mode determination step which is based on Kurtosis and Crest factor is applied to categorize decomposition result into groups such as signal mode and noisy mode. Direct subtraction of the noisy mode from the corrupted ECG observation results in ECG signal with reduced peak since noise mode carries part of signal components in addition to interference. Hence, to restore the peak, wiener filter is applied on noisy mode to estimate actual PLI component. Finally, Noise free ECG signal is reconstructed by subtracting estimated PLI from the corrupted ECG signal. This paper discusses four possible PLI suppression methods which are derived by combining SST domain with wiener filter in various ways. Simulations are carried out to test the effectiveness of proposed methods. It is evident from the simulation results that the proposed methods can remove PLI of 50 Hz and its harmonics. The proposed techniques effectively removed PLI in both real and artificial ECG signals and to test its performance they are compared with state of the art methods. The SST based filtering methods outperformed other methods under the condition of PLI frequency variations. The experimental results also suggest that the SST based wiener filtering with modified reference approach offers better PLI suppression than all other methods.

An SVD-based adaptive robust speech steganography using MDCT coefficient

Speech is one of the essential ways of communication. The study of speech steganography provides great value in information security. To improve imperceptibility and robustness of speech steganography, the characteristics of speech signals should be fully taken into account. In this paper, a robust speech steganographic scheme based on Singular Value Decomposition (SVD) and Modified Discrete Cosine Transform (MDCT) is proposed. Firstly, Voice Activity Detector (VAD) is used to detect voiced frames from speech signals, along with MDCT with Kaiser Bessel Derived (KBD) window being performed on each frame. Then the MDCT coefficients are selected from a certain frequency range and divided into a pair of segments. The two largest singular values of the paired segments are modified respectively according to their value difference to embed secret message. The thresholds are adaptively adjusted according to the largest singular values. Extensive experiments are carried out to compare the proposed method with three other methods from imperceptibility, robustness, capacity, and security. The experimental results show that under the simulation parameters β = 320, Nk = 58, fl = 100 Hz, fh = 3 kHz, and α = 0.61, the proposed method has striking advantages to resist common robust attacks and the state-of-the-art steganalysis attacks while maintaining good imperceptibility.

Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior.

By means of compressive sampling (CS), a sparse signal can be efficiently recovered from its far fewer samples than that required by the Nyquist–Shannon sampling theorem. However, recovering a speech signal from its CS samples is a challenging problem, as it is not sparse enough on any existing canonical basis. To solve this problem, we propose a method which combines the approximate message passing (AMP) and Markov chain that exploits the dependence between the modified discrete cosine transform (MDCT) coefficients of a speech signal. To reconstruct the speech signal from CS samples, a turbo framework, which alternately iterates AMP and belief propagation along the Markov chain, is utilized. In addtion, a constrain is set to the turbo iteration to prevent the new method from divergence. Extensive experiments show that, compared to other traditional CS methods, the new method achieves a higher signal-to-noise ratio, and a higher perceptual evaluation of speech quality (PESQ) score. At the same time, it maintaines a better similarity of the energy distribution to the original speech spectrogram. The new method also achieves a comparable speech enhancement effect to the state-of-the-art method.

Audio watermarking technique in frequency domain: comparative study MDCT Vs DCT

Digital watermarking is the art of hiding information in a digital document in order to protect it. The inserted mark and the marked document can be an image, an audio or a video. In this article, we will present a comparative study between two variants of a digital audio watermarking technique operating in the frequency domain. In the first variant, the time-frequency mapping is performed by Modified Discrete Cosine Transform (MDCT). For the second variant, the time-frequency mapping is performed by the Discrete Cosine Transform (DCT). We will study the contribution of each transformation, point of view robustness against different types of attacks delivered by Stirmark audio, imperceptibility by using a statistical approach by calculating the SNR and an objective approach by calculating the ODG notes given by PEAQ and capacity of insertion. Finally, to highlight our results, we will compare the two variants of the proposed technique with some other existing techniques.

New Speech Compression Technique based on Filter Bank Design and Psychoacoustic Model

In this paper, a new speech compression technique is proposed. This technique applies a Psychoacoustic Model and a general approach for Filter Bank Design using optimization. It is evaluated and compared with a compression technique using a MDCT (Modified Discrete Cosine Transform) Filter Bank of 32 Filters and a Psychoacoustic Model. This evaluation and comparison is performed by calculating bits before and after compression, PSNR (Peak Signal to Noise Ratio), NRMSE (Normalized Root Mean Square Error), SNR (Signal to Noise Ratio) and PESQ (Perceptual evaluation of speech quality) computations. The two techniques are tested and applied to a number of speech signals that are sampled at 8 kHz. The results obtained from this evaluation show that the proposed technique outperforms the second compression technique (based on a Psychoacoustic Model and MDCT filter Bank) in terms of Bits after compression and compression ratio. In fact, the proposed technique yields higher values for the compression ratio than the second compression technique. Moreover, the proposed compression technique presents reconstructed speech signals with acceptable perceptual qualities. This is justified by the values of SNR, PSNR and NRMSE and PESQ.

Efficient MDCT Recursive Structure for VLSI Implementation

Modified discrete cosine transform (MDCT) is a computationally intensive fundamental processing block for mapping time into frequency in various versions of MPEG audio coding. An efficient algorithm for MDCT computation is a basic requirement to realize low-cost audio codec. This paper introduces a new recursive algorithm and architecture for MDCT. The proposed architecture requires minimum hardware and execution time. Folding of the input sequence is performed multiple times in the subsequent groups which results in a significant reduction in real multiplication and real addition operations. Also, fewer number of execution cycles are required to generate the output through this algorithm. The proposed structure is suitable for parallel VLSI implementation as it is simple, regular and modular.

Modified DCT-based Audio Watermarking Optimization using Genetics Algorithm

Ease process digital data information exchange impact on the increase in cases of copyright infringement. Audio watermarking is one solution in providing protection for the owner of the work. This research aims to optimize the insertion parameters on Modified Discrete Cosine Transform (M-DCT) based audio watermarking using a genetic algorithm, to produce better audio resistance. MDCT is applied after reading host audio, then embedding in MDCT domain is applied by Quantization Index Modulation (QIM) technique. Insertion within the MDCT domain is capable of generating a high imperceptible watermarked audio due to its overlapping frame system. The system is optimized using genetic algorithms to improve the value of imperceptibility and robustness in audio watermarking. In this research, the average SNR reaches 20 dB, and ODG reaches -0.062. The subjective quality testing on the system obtains an average MOS of 4.22 out of five songs tested. In addition, the system is able to withstand several attacks. The use of M-DCT in audio watermaking is capable of producing excellent imperceptibility and better watermark robustness.

Sliding Discrete Fourier Transform with Kernel Windowing [Lecture Notes

The sliding discrete Fourier transform (SDFT) is an efficient method for computing the N -point DFT of a given signal starting at a given sample from the N -point DFT of the same signal starting at the previous sample [1]. However, the SDFT does not allow the use of a window function, generally incorporated in the computation of the DFT to reduce spectral leakage, as it would break its sliding property. This article will show how windowing can be included in the SDFT by using a kernel derived from the window function, while keeping the process computationally efficient. In addition, this approach allows for turning other transforms, such as the modified discrete cosine transform (MDCT), into efficient sliding versions of themselves.

HMM speech synthesis based on MDCT representation

Hidden Markov model (HMM) based text-to-speech (TTS) has become one of the most promising approaches, as it has proven to be a particularly flexible and robust framework to generate synthetic speech. However, several factors such as mel-cepstral vocoder and over-smoothing are responsible for causing quality degradation of synthetic speech. This paper presents an HMM speech synthesis technique based on the modified discrete cosine transform (MDCT) representation to cope with these two issues. To this end, we use an analysis/synthesis technique based on MDCT that guarantees a perfect reconstruction of the signal frame from feature vectors and allows for a 50% overlap between frames without increasing the data vector, in contrast to the conventional mel-cepstral spectral parameters that do not ensure direct speech waveform reconstruction. Experimental results show that a sound of good quality, conveniently evaluated using both objective and subjective tests, is obtained.

A scalable wideband speech codec using the wavelet packet transform based on the internet low bitrate codec

Most recent speech codecs employ code excited linear prediction (CELP) and transmit side information to improve speech quality under packet loss. Another approach to achieve high robustness to packet loss is to use the frame independent coding scheme based on the internet low bitrate codec (iLBC). The scalable wideband speech codec based on the iLBC was previously presented and outperformed G.729.1 at most bit rates according to the objective quality. This paper presents improvements to the previous work. Specifically, we employ the wavelet packet transform (WPT) instead of the modified discrete cosine transform (MDCT) to enhance the quality, and evaluate the proposed codec based on both the objective and subjective quality measures. The objective quality evaluation results show that clear improvement is achieved and that the proposed codec outperforms G.729.1 at the bit rate of 18 kbps or higher under clean channel conditions and has higher robustness to packet loss than G.729.1. The informal subjective test results also show similar trends.

Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review.

An increase in the accuracy of identification of Activities of Daily Living (ADL) is very important for different goals of Enhanced Living Environments and for Ambient Assisted Living (AAL) tasks. This increase may be achieved through identification of the surrounding environment. Although this is usually used to identify the location, ADL recognition can be improved with the identification of the sound in that particular environment. This paper reviews audio fingerprinting techniques that can be used with the acoustic data acquired from mobile devices. A comprehensive literature search was conducted in order to identify relevant English language works aimed at the identification of the environment of ADLs using data acquired with mobile devices, published between 2002 and 2017. In total, 40 studies were analyzed and selected from 115 citations. The results highlight several audio fingerprinting techniques, including Modified discrete cosine transform (MDCT), Mel-frequency cepstrum coefficients (MFCC), Principal Component Analysis (PCA), Fast Fourier Transform (FFT), Gaussian mixture models (GMM), likelihood estimation, logarithmic moduled complex lapped transform (LMCLT), support vector machine (SVM), constant Q transform (CQT), symmetric pairwise boosting (SPB), Philips robust hash (PRH), linear discriminant analysis (LDA) and discrete cosine transform (DCT).

A Psychoacoustic-Based Multiple Audio Object Coding Approach via Intra-Object Sparsity

Rendering spatial sound scenes via audio objects has become popular in recent years, since it can provide more flexibility for different auditory scenarios, such as 3D movies, spatial audio communication and virtual classrooms. To facilitate high-quality bitrate-efficient distribution for spatial audio objects, an encoding scheme based on intra-object sparsity (approximate k-sparsity of the audio object itself) is proposed in this paper. The statistical analysis is presented to validate the notion that the audio object has a stronger sparseness in the Modified Discrete Cosine Transform (MDCT) domain than in the Short Time Fourier Transform (STFT) domain. By exploiting intra-object sparsity in the MDCT domain, multiple simultaneously occurring audio objects are compressed into a mono downmix signal with side information. To ensure a balanced perception quality of audio objects, a Psychoacoustic-based time-frequency instants sorting algorithm and an energy equalized Number of Preserved Time-Frequency Bins (NPTF) allocation strategy are proposed, which are employed in the underlying compression framework. The downmix signal can be further encoded via Scalar Quantized Vector Huffman Coding (SQVH) technique at a desirable bitrate, and the side information is transmitted in a lossless manner. Both objective and subjective evaluations show that the proposed encoding scheme outperforms the Sparsity Analysis (SPA) approach and Spatial Audio Object Coding (SAOC) in cases where eight objects were jointly encoded.

Nonuniform Orthogonal Filterbanks Based on MDCT Analysis/Synthesis and Time-Domain Aliasing Reduction

In this letter we describe nonuniform orthogonal modified discrete cosine transform (MDCT) filterbanks and time-domain aliasing reduction (TDAR). By adding a postprocessing step to the MDCT, our method allows for arbitrary nonuniform frequency resolutions using subband merging with smooth windowing and overlap in frequency. This overlap allows for an improved temporal compactness of the impulse response, which is especially useful for audio coders. The postprocessing step comprises another lapped MDCT transform along the frequency axis and TDAR along each subband signal.