Coded Domain Level Control for The AMR Speech Codec

In this paper we present a voicing controlled, speech parameter based frame loss concealment for frames that have been encoded with the Adaptive Multi-Rate (AMR) speech codec. The missing parameters are estimated by interpolation and extrapolation techniques that are chosen in dependence of the voicing state of the speech frames preceding and following the lost frames. The voicing controlled concealment outperforms the conventional extrapolation/muting based approach and it shows a consistent improvement over interpolation techniques that do not distinguish between voiced and unvoiced speech. The quality can be further improved if additional information about the predictor states of predictively encoded parameters is available from a redundant transmission in future packets.

This letter proposes an efficient uplink scheduling algorithm for voice over Internet protocol (VoIP) services with adaptive multi-rate (AMR) speech codec in IEEE 802.16e/m systems. The proposed scheduling algorithm adopts the random access scheme during silent-period to reduce the waste of uplink bandwidth considering the characteristics of AMR speech codec. The numerical results show that the proposed algorithm can increase the maximum supportable number of voice users by 26% compared to the conventional extended real-time polling service (ertPS).

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.

Lower rate codes can be constructed from a high rate code by inserting known dummy bits into the information bit sequence before encoding. These so-called insertion convolutional (IC) codes can individually adjust the amount of protection given to the information bits, and can be decoded using the same decoder as for the mother codes. We employed the recursive systematic insertion convolutional (RSIC) codes in adaptive multirate (AMR) speech transmission over GERAN (GSM/EDGE radio access network) 8PSK half rate (HR) voice carriers and good results were obtained. The unequal error protection (UEP) necessary for differently important speech encoded bits is realized and optimized by employing different rates of channel codes, followed by a systematic mapping of code bits into strong and weak bits of 8PSK symbols. As a result, the scheme for AMR speech transmission over 8PSK HR voice carriers significantly outperforms the scheme over GMSK HR voice carriers.

As adaptive multi-rate (AMR) speech applications become increasingly widespread, AMR-based steganography presents growing security risks. Conventional steganalysis methods often assume known embedding rates, limiting their practicality in real-world scenarios where embedding rates are unknown. To overcome this limitation, we introduce a novel framework that integrates a multi-scale transformer architecture with multi-task learning for joint classification and regression. The classification task effectively distinguishes between cover and stego samples, while the regression task enhances feature representation by predicting continuous embedding values, providing deeper insights into embedding behaviors. This joint optimization strategy improves model adaptability to diverse embedding conditions and captures the underlying relationships between discrete embedding classes and their continuous distributions. The experimental results demonstrate that our approach achieves higher accuracy and robustness than existing steganalysis methods across varying embedding rates.

The adaptive multi-rate (AMR) is the mandatory speech codec for GSM system, ranging from 12.2 kbps down to 4.75 kbps. To satisfy the requirement of need for both audio and video simultaneously, a kind of hardware and software of AMR speech encoder based on high-performance chips TMS320C64X DSP is proposed. Considering the limited resource of DSP chips, the optimization scheme is referred to later. The result shows that the CPU-load is reduced from 38 percent to 4 percent. And the time of processing each frame is also reduced greatly after optimizing. It is verified with all the test vectors provided by 3 GPP, and the stable operation on the real-time testing board was also confirmed. So, more resource is reserved for video coding on DSP chips. This is helpful for implementing both speech coder and video coder on one DSP chip.

This paper describes the VAD (voice activity detection) for controlling DTX (discontinuous transmission) of the GSM AMR (adaptive multi-rate) speech codec. The algorithm is based on spectral estimation and periodicity detection. The VAD contains a 9-band IIR filter bank, which divides input signals into frequency bands. The signal level at each band is calculated. Background noise is estimated in each sub-band. The VAD decision is computed by comparing input signal level and background noise estimate. The algorithm incorporates novel methods to estimate background noise and to detect periodic components based on open-loop pitch gain. A new method is also derived to detect correlated complex signals like music.

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.

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.

Identification of AMR decompressed audio

This paper presents a fast efficient codeword search algorithm for vector quantization (VQ) based on the Adaptive Multi-Rate Wideband (AMR-WB) speech codec. AMR-WB is the speech codec standardized by the 3GPP for the GSM and WCDMA 3G system. It is also selected as the ITU-T G.722.2 standards. In AMR-WB speech codec VQ codeword search section, full search algorithm is used to find matching codeword .This requires a huge amount of computation. For large-size codebooks and high-dimensional vector of AMR-WB, computational complexity is significantly higher.The proposed fast codeword search method selects the codeword ,which has the least absolute error component in VQ codebook, as the initial matching codeword. It can help to reduce computational complexity, whereas the reconstructed speech quality almost has no dropping. The article introduces the basic principle of the fast codeword search algorithm in detail. Simulation results show that the algorithm based on AMR-WB speech codec, can reduce the time of vector quantization codebook search and the w-PESQ of nine modes is no significant dropping.

The adaptive multi-rate wideband (AMR-WB) speech codec is widely used in modern mobile communication systems for high speech quality in handheld devices. Nonetheless, a major disadvantage is that vector quantization (VQ) of immittance spectral frequency (ISF) coefficients takes a considerable computational load in the AMR-WB coding. Accordingly, a binary search space-structured VQ (BSS-VQ) algorithm is adopted to efficiently reduce the complexity of ISF quantization in AMR-WB. This search algorithm is done through a fast locating technique combined with lookup tables, such that an input vector is efficiently assigned to a subspace where relatively few codeword searches are required to be executed. In terms of overall search performance, this work is experimentally validated as a superior search algorithm relative to a multiple triangular inequality elimination (MTIE), a TIE with dynamic and intersection mechanisms (DI-TIE), and an equal-average equal-variance equal-norm nearest neighbor search (EEENNS) approach. With a full search algorithm as a benchmark for overall search load comparison, this work provides an 87% search load reduction at a threshold of quantization accuracy of 0.96, a figure far beyond 55% in the MTIE, 76% in the EEENNS approach, and 83% in the DI-TIE approach.

Quantization of data is performed to reduce the bit rate required either for storage of data, or transmission between two or more communicators. Linear prediction (LP) is an efficient way to represent the short term spectrum of the speech signal. The line spectral frequency (LSF) transformation provides benefits over than linear prediction representations such as reflection coefficients, arc sine reflection coefficients, or log area ratios. Vector quantization of the filter parameters allows for a larger reduction in the bit rate to represent a set of parameters over scalar quantization. This reduction comes at the expense of using more computational complexity and greater amounts of storage. To reduce the burden of both storage and computation, various techniques and procedures have been developed. These techniques include full, multistage and split vector quantization, as well as the adaptive, variable dimension and finite state vector quantizers [1-8].

Linear predictive coding (LPC) parameters are widely used in various speech coding applications for representing the short-time spectral envelope information of speech [1]. For low bit rate speech coding applications, it is important to quantize these parameters using as few bits as possible. Considerable workhas been done in the past to develop both scalar and vector quantization procedures to quantize the LPC parameters [2, 3,4]. Scalar quantizers quantize each of the LPC parameters independently, while vector quantizers consider the entire set of LPC parameters as an entity and allow for direct minimization of quantization distortion. Because of this, the vector quantizers result in smaller distortion than the scalar quantizers at any given bit rate. The vector quantizers, however, have one major problem; their computational complexity is high. In our earlier paper [3], we have reported on a vector quantizer where the LPC parameter vector is split in the line spectral frequency (LSF) domain to overcome this complexity problem. We have shown that this quantizer can quantize the LPC parameters at 24 bits/frame with an average spectral distortion of 1 dB, less than 2% frames having spectral distortionl in the range 2-4 dB and no frame having spectral distortion greater than 4 dB.

This paper describes a full-rate (FR) candidate speech codec for a new Adaptive Multi-Rate (AMR) codec standard. The proposed codec is a multi-rate codec, operating in various modes that have different bit-rate partitionings between source and channel coding. The source coding is based on Algebraic Code Excited Linear Predictive Coding (ACELP). Convolutional coding is used for the channel coding. Subjective tests show that at poor channel error conditions with low C/I-ratios of 7 dB, the lowest mode of the proposed codec produces speech quality comparable to G.728 in clean channel condition. The codec also provides substantial improvement on error robustness over the Enhanced Full Rate (EFR) GSM codec, by selecting an optimal coding rate to suit the channel condition. The codec performance passes twenty-six out of the twenty-nine test conditions, conducted according to [2]. Therefore, the proposed codec has passed the ETSI qualification test.