Adaptive Multi-rate Research Articles

Steganalysis of adaptive multi-rate speech with unknown embedding rates using clustering and ensemble learning

With the widespread use of adaptive multi-rate (AMR) speech-based applications, AMR speech-based steganography has witnessed significant growth. Consequently, steganalysis approaches have garnered attention to mitigate network security risks associated with AMR speech-based steganography. However, existing studies often assume known embedding rates of test samples, leaving steganography detection under unknown embedding rates—an encountered practical scenario—unresolved. To tackle this challenge, this paper presents a novel detection scheme for AMR speech-based steganography, skillfully combining clustering and ensemble learning. The training phase utilizes K-means clustering to pre-classify speech samples, grouping them into distinct clusters based on their feature distribution and embedding rates. Subsequently, a classifier based on extreme gradient boosting (XGBoost) is trained for each cluster. The experimental results demonstrate that the proposed scheme exhibits significant improvements in terms of recall rate when compared to existing steganalysis techniques.

A time adaptive multirate Dirichlet–Neumann waveform relaxation method for heterogeneous coupled heat equations

AbstractWe consider partitioned time integration for heterogeneous coupled heat equations. First and second order multirate, as well as time‐adaptive Dirichlet‐Neumann Waveform relaxation (DNWR) methods are derived. In 1D and for implicit Euler time integration, we analytically determine optimal relaxation parameters for the fully discrete scheme.We test the robustness of the relaxation parameters on the second order multirate method in 2D. DNWR is shown to be very robust and consistently yielding fast convergence rates, whereas the closely related Neumann‐Neumann Waveform relaxtion (NNWR) method is slower or even diverges.The waveform approach naturally allows for different timesteps in the subproblems. In a performance comparison for DNWR, the time‐adaptive method dominates the multirate method due to automatically finding suitable stepsize ratios. Overall, we obtain a fast, robust, multirate and time adaptive partitioned solver for unsteady conjugate heat transfer.

MFPD-LSTM: A steganalysis method based on multiple features of pitch delay using RNN-LSTM

In recent years, adaptive multi-rate (AMR) steganalysis based on pitch delay has become a hot research area for researchers. The main reason is that the steganalysis algorithm based on AMR can sensitively detect the change in the statistical characteristics of the pitch delay before and after the secret information is embedded, and achieve high accuracy. At present, many excellent AMR steganalysis algorithms based on pitch delay have been proposed. However, when these algorithms are aimed at short-term, low-embedding-rate speech samples, there is room for improvement in accuracy and performance. In this paper, a Recurrent Neural Network-Long-Short Term Memory (RNN-LSTM) is used to design a steganalysis method through multi-feature fusion based on pitch delay (MF-PD). This method extracts the pitch delay sequence in the speech information stream, mines four features that characterize the statistical feature of the intra-frame and the inter-frame, and establishes the feature matrix. Then construct the RNN-LSTM model, the dropout layer is introduced to avoid over-fitting and the model is pruned to improve the efficiency. Experimental results show that when the speech sample length is 0.1s, this method can achieve a detection accuracy of more than 87%, which is significantly higher than other steganalysis algorithms. For samples with low embedding rate, the method proposed in this paper can also achieve better performance, which satisfies the need for short time and low embedding rate sample detection.

On the Implementation of an Adaptive Multirate Framework for Coupled Transport and Flow

This paper presents a multirate in time approach for coupled flow and transport problems combined with goal-oriented error control based on the Dual Weighted Residual (DWR) method. The focus is on the implementation of the multirate concept regarding different time scales for the underlying subproblems. Key ingredients are an arbitrary degree discontinuous Galerkin time discretization, an a posteriori error representation for the transport problem coupled with flow and the concept of space-time slabs based on tensor-product spaces. From the latter, a space-time mesh adaptation with highly economical grids is developed. The performance of the approach is studied carefully by numerical convergence examples as well as an example of physical interest for convection-dominated transport.

Steganalysis of adaptive multi-rate speech streams with distributed representations of codewords

With the wide application of adaptive multi-rate (AMR) speech coder, steganography and steganalysis based on AMR coded speech streams have become a hot spot in the field of information hiding. Quantization Index Modulation (QIM)-based steganography is one of the most effective approaches to hide secret information into AMR coded speech streams with excellent imperceptibility and robustness. So far, accurate detection for QIM-based steganography in short-length or low-embedding-rate speech streams remains an open problem, though some approaches can complete detection in a short time. To address this challenge, we first analyze and verify the characteristics of QIM-based steganography, and present a novel and high time-efficient steganalysis model based on distributed representations. Specifically, a codeword embedding layer is introduced to capture distributed representations with a denser space; then we introduce a bidirectional Long Short-Term Memory (LSTM) layer and propose a gated attention mechanism to provide contextual distribution features with better generalization capabilities; finally, a Multi-Layer Perceptron (MLP) classifier is designed to distinguish normal or steganographic objects. The experimental results demonstrate that the proposed model can effectively detect QIM-based steganography in AMR speech streams and outperform the state-of-the-art ones.

Real‐time structural displacement estimation by fusing asynchronous acceleration and computer vision measurements

AbstractAlthough displacement measurement is essential for many civil infrastructure applications, the precise estimation of structural displacement remains a challenge. In this study, a structural displacement estimation technique was developed by fusing asynchronous acceleration and computer vision measurements using a Kalman filter. First, the scale factor, which converts translation from vision measurements (in pixel units) into displacement (in length units), is automatically computed using a natural target (i.e., without any artificial target or any prior knowledge of the target size). Second, an improved feature matching algorithm was developed to better trace the natural target within the computer vision. Third, an adaptive multirate Kalman filter was formulated such that asynchronous computer vision and acceleration measurements with different sampling rates could be seamlessly combined to improve displacement estimation. The feasibility and effectiveness of the proposed displacement estimation technique were validated by performing shaking table, four‐story building model, and steel box girder pedestrian bridge tests. In all tests, the proposed technique was able to accurately estimate displacements with root mean square errors of less than 3 mm.

Optimized Multirate Wideband Speech Steganography for Improving Embedding Capacity Compared with Neighbor-Index-Division Codebook Division Algorithm

Aim: The main motive of this study is to perform Adaptive Multi Rate Wideband (AMR-WB) Speech Steganography in network security to produce the stego speech with less loss of quality while increasing embedding capacities. Materials and Methods: TIMIT Acoustic-Phonetic Continuous Speech Corpus dataset consists of about 16000 speech samples out of which 1000 samples are taken and 80% pretest power for analyzing the speech steganography. AMR-WB Speech steganography is performed by Diameter Neighbor codebook partition algorithm (Group 1) and Neighbor Index Division codebook division algorithm (Group 2). Results: The AMR-WB speech steganography using DN codebook partition obtained average quality rate of 2.8893 and NID codebook division algorithm obtained average quality rate of 2.4196 in the range of 300bps embedding capacity. Conclusion: The outcomes of this study proves that the decrease in quality in NID is twice more than the DN based steganography while increasing the embedding capacities.

Deep convolutional neural networks for double compressed AMR audio detection

Detection of double compressed (DC) adaptive multi-rate (AMR) audio recordings is a challenging audio forensic problem and has received great attention in recent years. Here, the authors propose to use convolutional neural networks (CNN) for DC AMR audio detection. The CNN is used as (i) an end-to-end DC AMR audio detection system and (ii) a feature extractor. The end-to-end system receives the audio spectrogram as the input and returns the decision whether the input audio is single compressed (SC) or DC. As a feature extractor in turn, it is used to extract discriminative features and then these features are modelled using support vector machines (SVM) classifier. Our extensive analysis conducted on four different datasets shows the success of the proposed system and provides new findings related to the problem. Firstly, double compression has a considerable impact on the high frequency components of the signal. Secondly, the proposed system yields great performance independent of the recording device or environment. Thirdly, when previously altered files are used in the experiments, 97.41% detection rate is obtained with the CNN system. Finally, the cross-dataset evaluation experiments show that the proposed system is very effective in case of a mismatch between training and test datasets.

An Antiforensic Method against AMR Compression Detection

Adaptive multirate (AMR) compression audio has been exploited as an effective forensic evidence to justify audio authenticity. Little consideration has been given, however, to antiforensic techniques capable of fooling AMR compression forensic algorithms. In this paper, we present an antiforensic method based on generative adversarial network (GAN) to attack AMR compression detectors. The GAN framework is utilized to modify double AMR compressed audio to have the underlying statistics of single compressed one. Three state-of-the-art detectors of AMR compression are selected as the targets to be attacked. The experimental results demonstrate that the proposed method is capable of removing the forensically detectable artifacts of AMR compression under various ratios with an average successful attack rate about 94.75%, which means the modified audios generated by our well-trained generator can treat the forensic detector effectively. Moreover, we show that the perceptual quality of the generated AMR audio is well preserved.

AN EFFICIENT SPEECH GENERATIVE MODEL BASED ON DETERMINISTIC/STOCHASTIC SEPARATION OF SPECTRAL ENVELOPES

The paper presents a speech generative model that provides an efficient way of generating speech waveform from its amplitude spectral envelopes. The model is based on hybrid speech representation that includes deterministic (harmonic) and stochastic (noise) components. The main idea behind the approach originates from the fact that speech signal has a determined spectral structure that is statistically bound with deterministic/stochastic energy distribution in the spectrum. The performance of the model is evaluated using an experimental low-bitrate wide-band speech coder. The quality of reconstructed speech is evaluated using objective and subjective methods. Two objective quality characteristics were calculated: Modified Bark Spectral Distortion (MBSD) and Perceptual Evaluation of Speech Quality (PESQ). Narrow-band and wide-band versions of the proposed solution were compared with MELP (Mixed Excitation Linear Prediction) speech coder and AMR (Adaptive Multi-Rate) speech coder, respectively. The speech base of two female and two male speakers were used for testing. The performed tests show that overall performance of the proposed approach is speaker-dependent and it is better for male voices. Supposedly, this difference indicates the influence of pitch highness on separation accuracy. In that way, using the proposed approach in experimental speech compression system provides decent MBSD values and comparable PESQ values with AMR speech coder at 6,6 kbit/s. Additional subjective listening testsdemonstrate that the implemented coding system retains phonetic content and speaker’s identity. It proves consistency of the proposed approach.

Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting

Steganalysis of adaptive multi-rate (AMR) speech is a hot topic for controlling cybercrimes grounded in steganography in related speech streams. In this paper, we first present a novel AMR steganalysis model, which utilizes extreme gradient boosting (XGBoost) as the classifier, instead of support vector machines (SVM) adopted in the previous schemes. Compared with the SVM-based model, this new model can facilitate the excavation of potential information from the high-dimensional features and can avoid overfitting. Moreover, to further strengthen the preceding features based on the statistical characteristics of pulse pairs, we present the convergence feature based on the Markov chain to reflect the global characterization of pulse pairs, which is essentially the final state of the Markov transition matrix. Combining the convergence feature with the preceding features, we propose an XGBoost-based steganalysis scheme for AMR speech streams. Finally, we conducted a series of experiments to assess our presented scheme and compared it with previous schemes. The experimental results demonstrate that the proposed scheme is feasible, and can provide better performance in terms of detecting the existing steganography methods based on AMR speech streams.

Detection of AMR double compression using compressed-domain speech features

The adaptive multi-rate (AMR) audio codec is an established standard of speech signal compression, for either transmitting speech signal over mobile networks or storing digital audio on handheld devices. The widespread use of AMR codec and the high availability of tampering software have increased authentication cases in court. AMR double compression detection is a challenging engineering problem and a topic of multimedia forensics. As a general rule, a double compressed AMR file cannot be considered an original file. In this paper, a new method based on support vector machine (SVM) is proposed to classify single and double compressed AMR digital audio. Instead of using the decoded speech waveform, the proposed method uses only compressed-domain speech features. Specific parameters are extracted from encoded AMR files and used to create a set of statistical features. After applying robust scaling to features and selecting the SVM model, recursive feature elimination with correlation bias reduction (RFE-CBR) is used to determine the best number of features to maximize accuracy in SVM classification. The experiments reveal that the proposed algorithm can discriminate single and double compressed AMR speech, outperforming the published methods. The average accuracies using TIMIT database and CARIOCA1 database, the last recorded from landline phone calls, are about 99%. Other experiments, including frame offset attack and noise addition, found that the method is robust and reliable.

A Secure AMR Fixed Codebook Steganographic Scheme Based on Pulse Distribution Model

Adaptive multi-rate (AMR), a popular audio compression standard, is widely used in mobile communication and mobile Internet applications and has become a novel carrier for hiding information. To improve the statistical security, this paper presents a steganographic scheme in the AMR fixed codebook (FCB) domain based on the pulse distribution model (PDM-AFS), which is obtained from the distribution characteristics of the FCB value in the cover audio. The pulse positions in stego audio are controlled by message encoding and random masking to make the statistical distribution of the FCB parameters close to that of the cover audio. The experimental results show that the statistical security of the proposed scheme is better than that of the existing schemes. Furthermore, the hiding capacity is maintained compared with the existing schemes. The average hiding capacity can reach 2.06 kbps at an audio compression rate of 12.2 kbps, and the auditory concealment is good. To the best of our knowledge, this is the first secure AMR FCB steganographic scheme that improves the statistical security based on the distribution model of the cover audio. This scheme can be extended to other audio compression codecs under the principle of algebraic code excited linear prediction (ACELP), such as G.723.1 and G.729.

Steganalysis of AMR Based on Statistical Features of Pitch Delay

The calibrated matrix of the second-order difference of the pitch delay (C-MSDPD) feature has been proven to be effective in detecting steganography based on pitch delay. In this article, a new steganalysis scheme based on multiple statistical features of pitch delay is present. Analyzing the principle of the adaptive multi-rate (AMR) codec, the pitch delay values in the same frame is divided into groups, in each of which, a pitch delay has a closer correlation with the other ones. To depict the characteristic of the pitch delay, two new types of statistical features are adopted in this article. The new features and C-MSDPD feature are together employed to train a classifier based on support vector machine (SVM). The experimental result shows that, the proposed scheme outperforms the existing one at different embedding bit rates and with different speech lengths.

Steganalysis of Adaptive Multi-Rate Speech Using Statistical Characteristics of Pitch Delay

Steganalysis of Adaptive Multi-Rate Speech Using Statistical Characteristics of Pitch Delay

AMR Compressed-Domain Analysis for Multimedia Forensics Double Compression Detection

An audio recording must be authentic to be admitted as evidence in a criminal prosecution so that the speech is saved with maximum fidelity and interpretation mistakes are prevented. AMR (adaptive multi-rate) encoder is a worldwide standard for speech compression and for GSM mobile network transmission, including 3G and 4G. In addition, such encoder is an audio file format standard with extension AMR which uses the same compression algorithm. Due to its extensive usage in mobile networks and high availability in modern smartphones, AMR format has been found in audio authenticity cases for forgery searching. Such exams compound the multimedia forensics field which consists of, among other techniques, double compression detection, i. e., to determine if a given AMR file was decompressed and compressed again. AMR double compression detection is a complex engineering problem whose solution is still underway. In general terms, if an AMR file is double compressed, it is not an original one and it was likely doctored. The published works in literature about double compression detection are based on decoded waveform AMR files to extract features. In this paper, a new approach is proposed to AMR double compression detection which, in spite of processing decoded audio, uses its encoded version to extract compressed-domain linear prediction (LP) coefficient-based features. By means of feature statistical analysis, it is possible to show that they can be used to achieve AMR double compression detection in an effective way, so that they can be considered a promising path to solve AMR double compression problem by artificial neural networks.

Mutual Information, the Linear Prediction Model, and CELP Voice Codecs

We write the mutual information between an input speech utterance and its reconstruction by a code-excited linear prediction (CELP) codec in terms of the mutual information between the input speech and the contributions due to the short-term predictor, the adaptive codebook, and the fixed codebook. We then show that a recently introduced quantity, the log ratio of entropy powers, can be used to estimate these mutual informations in terms of bits/sample. A key result is that for many common distributions and for Gaussian autoregressive processes, the entropy powers in the ratio can be replaced by the corresponding minimum mean squared errors. We provide examples of estimating CELP codec performance using the new results and compare these to the performance of the adaptive multirate (AMR) codec and other CELP codecs. Similar to rate distortion theory, this method only needs the input source model and the appropriate distortion measure.

A scalable speech coding scheme using compressive sensing and orthogonal mapping based vector quantization

A novel scalable speech coding scheme based on Compressive Sensing (CS), which can operate at bit rates from 3.275 to 7.275 kbps is designed and implemented in this paper. The CS based speech coding offers the benefit of combined compression and encryption with inherent de-noising and bit rate scalability. The non-stationary nature of speech signal causes the recovery process from CS measurements very complex due to the variation in sparsifying bases. In this work, the complexity of the recovery process is reduced by assigning a suitable basis to each frame of the speech signal based on its statistical properties. As the quality of the reconstructed speech depends on the sensing matrix used at the transmitter, a variant of Binary Permuted Block Diagonal (BPBD) matrix is also proposed here which offers a better performance than that of the commonly used Gaussian random matrix. To improve the coding efficiency, formant filter coefficients are quantized using the conventional Vector Quantization (VQ) and an orthogonal mapping based VQ is developed for the quantization of CS measurements. The proposed coding scheme offers the listening quality for reconstructed speech similar to that of Adaptive Multi rate - Narrowband (AMR-NB) codec at 6.7 kbps and Enhanced Voice Services (EVS) at 7.2 kbps. A separate de-noising block is not required in the proposed coding scheme due to the inherent de-noising property of CS. Scalability in bit rate is achieved in the proposed method by varying the number of random measurements and the number of levels for orthogonal mapping in the VQ stage of measurements.

Steganalysis of AMR Speech Based on Multiple Classifiers Combination

In this paper, we focus on steganalysis in adaptive multi-rate (AMR) speech streams, whose goal is to detect covert communication behaviors effectively to prevent illegal uses of AMR steganography. Differing from the existing methods for speech steganalysis, we present a novel steganalysis scheme based on multiple classifiers combination. Specifically, we design a new combined classifier model, whose main idea is as follows. First of all, steganalysis features are respectively inputted into two different classifier sets to obtain the first and second types of prediction results. Then, the second type of prediction results are viewed as a special type of features and fed into another classifier set to obtain the third type of prediction results. Finally, all the three types of prediction results are fused to achieve a final detection result. The presented steganalysis scheme is comprehensively evaluated using two types of state-of-the-art features for fixed codebook (FCB) parameters, namely, pulse-pair characteristics-based features and pulse-correlation based features, and compared with the existing methods based on support vector machines (SVM). The experimental results demonstrate that the proposed scheme is feasible and effective, and significantly outperforms the previous SVM-based methods for detecting steganography in FCB domains in every case.

RSA – QoS: A Resource Loss Aware Scheduling Algorithm for Enhancing the Quality of Service in Mobile Networks

Adaptive Multi-Rate Codec is one of the codecs which is used for making voice calls. It helps to connect people who are scattered in various geographical areas. It adjusts its bit-rate according to the user’s channel conditions. It plays a vital role in providing an improved speech quality of voice connection in Long Term Evolution (LTE). There are some constraints which need to be addressed in providing this service profitably. Quality of Service (QoS) is the dominant mechanism which determines the quality of the speech in communication. On several occasions, number of users are trying to access the same channel simultaneously by standing in a particular region for a longer period of time. It refers to Multi-user channel sharing problem which leads to resource loss very often. The main aim of this paper is to develop a novel RSA - QoS scheduling algorithm for reducing the Resource Loss Ratio. Eventually, it increases the throughput.The simulation result shows that the RSA - QoS increases the number of users for accessing the resources better than the existing algorithms in terms of resource loss and throughput. Ultimately, it enhances the QoS in Mobile Networks.