Relevant bibliographies by topics / Adaptive multi-rate speech

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Academic literature on the topic 'Adaptive multi-rate speech'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Adaptive multi-rate speech"

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Abreu-Sernández, V., and C. García-Mateo. "Adaptive multi-rate speech coder for VoIP transmission." Electronics Letters 36, no. 23 (2000): 1978. http://dx.doi.org/10.1049/el:20001344.

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Sun, Congcong, Hui Tian, Chin-Chen Chang, et al. "Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting." Electronics 9, no. 3 (2020): 522. http://dx.doi.org/10.3390/electronics9030522.

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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.
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Dan, Zhengjia, Yue Zhao, Xiaojun Bi, Licheng Wu, and Qiang Ji. "Multi-Task Transformer with Adaptive Cross-Entropy Loss for Multi-Dialect Speech Recognition." Entropy 24, no. 10 (2022): 1429. http://dx.doi.org/10.3390/e24101429.

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At present, most multi-dialect speech recognition models are based on a hard-parameter-sharing multi-task structure, which makes it difficult to reveal how one task contributes to others. In addition, in order to balance multi-task learning, the weights of the multi-task objective function need to be manually adjusted. This makes multi-task learning very difficult and costly because it requires constantly trying various combinations of weights to determine the optimal task weights. In this paper, we propose a multi-dialect acoustic model that combines soft-parameter-sharing multi-task learning with Transformer, and introduce several auxiliary cross-attentions to enable the auxiliary task (dialect ID recognition) to provide dialect information for the multi-dialect speech recognition task. Furthermore, we use the adaptive cross-entropy loss function as the multi-task objective function, which automatically balances the learning of the multi-task model according to the loss proportion of each task during the training process. Therefore, the optimal weight combination can be found without any manual intervention. Finally, for the two tasks of multi-dialect (including low-resource dialect) speech recognition and dialect ID recognition, the experimental results show that, compared with single-dialect Transformer, single-task multi-dialect Transformer, and multi-task Transformer with hard parameter sharing, our method significantly reduces the average syllable error rate of Tibetan multi-dialect speech recognition and the character error rate of Chinese multi-dialect speech recognition.
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Tian, Hui, Meilun Huang, Chin-Chen Chang, Yongfeng Huang, Jing Lu, and Yongqian Du. "Steganalysis of Adaptive Multi-Rate Speech Using Statistical Characteristics of Pitch Delay." JUCS - Journal of Universal Computer Science 25, no. (9) (2019): 1131–50. https://doi.org/10.3217/jucs-025-09-1131.

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Steganography is a promising technique for covert communications. However, illegal United States of Americage of this technique would facilitate cybercrime activities and thereby pose a great threat to information security. Therefore, it is crucial to study its countermeasure, namely, steganalysis. In this paper, we aim to present an efficient steganalysis method for detecting adaptive-codebook based steganography in adaptive multi-rate (AMR) speech streams. To achieve this goal, we first design a new low-dimensional feature set for steganalysis, including an improved calibrated Markov transition probability matrix for the second-order difference of pitch delay values (IC-MSDPD) and the probability distribution of the odevity for pitch delay values (PDOEPD). The dimension of the proposed feature set is 14, far smaller than the feature set in the state-of-the-art steganalysis method. Employing the new feature set, we further present a steganalysis scheme for AMR speech based on support vector machines. The presented scheme is evaluated with a large number of AMR-encoded speech samples, and compared with the state-of-the-art one. The experimental results show that the proposed method is effective, and outperforms the state-of-the-art one in both detection accuracy and computational overhead.
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Syarif, Abdusy, and Ahmad Fachril. "PENERAPAN FITUR ADAPTIVE MULTI RATE (AMR) PADA JARINGAN GSM." CommIT (Communication and Information Technology) Journal 4, no. 1 (2010): 17. http://dx.doi.org/10.21512/commit.v4i1.531.

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Adaptive Mutlirate (AMR) is a feature that plays an important role in the efficiency of use of cell/voice channels and GSM networks in overall and it can improve sound quality dynamically based on actual measurements (real time) between Mobile Station (MS) and Base Transmitter Station (BTS). Resources used as analytical parameters are SQI (Speech Quality Index), MOS (Mean Opinion Score) and the sound quality on the network without and with AMR. Measurements using Test Equipment Mobile System (TEMS) while locking devices to the single channel and comparing them between the two types of network. Based on test results it is obtained that with voice channels with AMR can increase the value of SQI approximately 40% for fullrate channels and about 60% for half-rate channels producing a remarkable (excellent) level, with research and further measuring it is expected to produce better and more perfect sound quality.Kata kunci: AMR, SQI, GSM networkABSTRAK
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Qiu, Yiqin, Hui Tian, Lili Tang, Wojciech Mazurczyk, and Chin-Chen Chang. "Steganalysis of adaptive multi-rate speech streams with distributed representations of codewords." Journal of Information Security and Applications 68 (August 2022): 103250. http://dx.doi.org/10.1016/j.jisa.2022.103250.

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Tian, Hui, Yanpeng Wu, Chin-Chen Chang, et al. "Steganalysis of adaptive multi-rate speech using statistical characteristics of pulse pairs." Signal Processing 134 (May 2017): 9–22. http://dx.doi.org/10.1016/j.sigpro.2016.11.013.

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Liu, Ranran, Hongxiang Xu, Enxing Zheng, and Yifeng Jiang. "Adaptive filtering for intelligent sensing speech based on multi-rate LMS algorithm." Cluster Computing 20, no. 2 (2017): 1493–503. http://dx.doi.org/10.1007/s10586-017-0871-y.

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Sun, Congcong, Azizol Abdullah, Normalia Samian, and Nuur Alifah Roslan. "Steganalysis of Adaptive Multi-Rate Speech with Unknown Embedding Rates Using Multi-Scale Transformer and Multi-Task Learning Mechanism." Journal of Cybersecurity and Privacy 5, no. 2 (2025): 29. https://doi.org/10.3390/jcp5020029.

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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.
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Reddy, Akkireddy Mohan Kumar. "Optimized Multirate Wideband Speech Steganography for Improving Embedding Capacity Compared with Neighbor-Index-Division Codebook Division Algorithm." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 1362–76. http://dx.doi.org/10.47059/revistageintec.v11i2.1763.

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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.
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Book chapters on the topic "Adaptive multi-rate speech"

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Zhang, Xiuyan, and Guobin Tao. "The Research of Adaptive Modulation Technology in OFDM System." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_74.

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AbstractOrthogonal frequency division multiplexing (OFDM) as a special multi-carrier transmission technology has good resistance to narrow-band interference and frequency selective fading ability. Compared with traditional modulation techniques, adaptive modulation can enhance bandwidth efficiency and system capacity. Therefore, applying adaptive modulation in OFDM systems can take full advantage of spectrum resources, and it is suitable for the high-speed and reliable mobile communication systems in the future. The purpose of this paper is to improve traditional OFDM adaptive algorithms (Hughes-Hartogs, Chow) to realize bits allocation, power allocation better. In this paper, simulation results demonstrated that the improved Levin-Campello algorithm lowers algorithm’s complexity greatly and owns better flexibility, at the same time, it guarantees good the bit error rate (BER) performance and can be applied to speech communication (fixed rate) and data communication (variable rate) in wireless communication systems.
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Chen, Shi-Huang, Yaotsu Chang, and T. K. Truong. "An Improved Voice Activity Detection Algorithm for GSM Adaptive Multi-Rate Speech Codec Based on Wavelet and Support Vector Machine." In New Trends in Applied Artificial Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73325-6_91.

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Pustozerov, Evgenii, and Urs-Vito Albrecht. "Evaluation of a Multi-Axes Multi-Channel Heartbeat Detection Algorithm in Ballistocardiography." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250282.

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This study explores the potential for estimating heartbeats and heart rate variability (HRV) parameters using multiple multi-axis ballistocardiographic (BCG) sensors in recording disturbed by speech interference. The results demonstrate that an adaptive approach, which detects and interpolates J-peaks in disturbed signal parts, provides more accurate heartbeat evaluations than relying on any single BCG channel in the same recording.
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He, Weijun, and Qianhua He. "An adaptive multi-scale lattice vector quantization and its application in low bit rate speech coding." In Multimedia Technology IV. CRC Press, 2015. http://dx.doi.org/10.1201/b18262-24.

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Xie, Fengyun, Jiankun Dong, Shaoshi Yan, Yongqi Jiang, and Yu Fu. "Research on Gearbox Fault Diagnosis Based on PCA and AGA-BP Neural Network." In Advances in Transdisciplinary Engineering. IOS Press, 2020. http://dx.doi.org/10.3233/atde200240.

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As the key component of high-speed train bogie, the fault characteristics of gearbox are mainly reflected in its vibration signal. The vibration signals collected in the process of gearbox fault diagnosis are usually complex and changeable, and have strong randomness and contingency. A gearbox fault diagnosis method based on multi feature extraction, principal component analysis (PCA) and adaptive genetic algorithm is proposed to optimize the back propagation neural network analysis. The original vibration data in the gearbox fault diagnosis experiment published by Jiangsu qianpeng Diagnostic Engineering Co., Ltd. is extracted by multi eigenvalues. The feature set is reduced by PCA. The selected principal components are diagnosed and analyzed by AGA-BP neural network. The final diagnosis result is that the root mean square error (MSE) of AGA-BP neural network is 0.0116, and the recognition rate of gearbox fault is 100%.
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Wang, Zhen, and Jiang Yan. "Design of High-Speed Ethernet Data Loop Communication System Based on FPGA." In Advances in Transdisciplinary Engineering. IOS Press, 2025. https://doi.org/10.3233/atde250312.

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To address the dual requirements of network protocol processing and high-speed data transmission, this paper proposes a network protocol stack solution based on a reconfigurable hardware platform. The architecture integrates pipeline processing technology with an adaptive rate matching mechanism, implementing cross-protocol (ARP/UDP/IP/ICMP) collaborative processing and three-speed Ethernet (10/100/1000Mbps) adaptive switching capability on FPGA hardware-programmable platforms, providing high-performance communication infrastructure for industrial IoT and edge computing scenarios. At the hardware architecture level, the system adopts a hierarchical protocol processing design. The underlying layer establishes a dual-port memory architecture and asynchronous data buffering unit, achieving multi-protocol parallel processing through dynamic priority arbitration mechanism. The middle layer integrates configurable rate matching modules, completing protocol parsing and encapsulation using a hardware description language (HDL)-developed protocol controller cluster (ARP/IP/UDP/ICMP). Considering chip ecosystem characteristics, a minimal IP dependency strategy is adopted, implementing core functional modules exclusively based on fundamental storage units (FIFO/RAM), significantly enhancing design portability. In functional verification, hardware simulation platforms confirm that the protocol stack possesses core capabilities including full-duplex UDP communication, active/passive address resolution, and network diagnostics (Ping). These technological breakthroughs not only resolve the contradiction between network protocol processing capability and transmission bandwidth in existing embedded devices, but also provide crucial technical support for establishing autonomous industrial communication systems.
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Sharma, Sheelesh Kumar, and Ram Jee Dixit. "Applications of Parallel Data Processing for Biomedical Imaging." In Applications of Parallel Data Processing for Biomedical Imaging. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2426-4.ch001.

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This Chapter explores the use of parallel data processing in biomedical imaging to improve diagnostic performance, reduce processing times, and enable prompt decision-making in clinical settings. It examines imaging modalities like CT, MRI, ... etc and how parallel data processing algorithms enable quick reconstruction of high-resolution pictures. Parallel computing architectures like GPUs and multi-core CPUs are used to increase computational efficiency. Cluster computing and distributed computing are considered scalable solutions for large-scale biomedical imaging datasets. Parallelized adaptive algorithms speed up convergence of iterative reconstruction techniques, at the same time as parallel noise reduction techniques beautify photograph first-rate. The research concludes that parallel information processing is crucial for making better studies and patient care within the field of biomedical imaging and offers significant benefits in terms of speed, efficiency, and the capacity to handle large datasets.
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Zhou, Jian, Qianqian Cheng, and Shuijie Wang. "Task Allocation Mechanism of UAV." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230844.

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In the past few decades, as a prominent representative of the third generation of intelligent robots, drones have taken the lead in developing from single task to multi machine collaborative work. Research has shown that using multiple drones to search for target areas can expand the search field, improve the efficiency of drones in completing tasks, reduce energy consumption during flight, and reduce environmental instability. Thus, high-precision positioning can be further carried out to improve the target hit rate. However, clustered drones also face some new challenges, such as the robustness of multi-agent topology, real-time performance of online perception mechanisms, reliability of information exchange, and loose coupling of collaborative systems. The method proposed in this paper improves the problem that the traditional Ant colony optimization algorithms is easy to fall into the local optimum and the Rate of convergence is slow. The obstacle avoidance factor is added to the calculation formula of the state transition probability, and an improved Pheromone volatilization coefficient based on the Gaussian distribution is given, so that the Pheromone volatilization factor changes from a fixed value to an adaptive value that changes with time, which makes the path obtained by the algorithm better and greatly speeds up the Rate of convergence of the algorithm. In this system, all UAVs can share their original data through direct communication between them. Even if one UAV fails, the whole task will not be affected.
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Conference papers on the topic "Adaptive multi-rate speech"

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Nieminen, Toni P. "Floating-point adaptive multi-rate wideband speech codec." In 7th International Conference on Spoken Language Processing (ICSLP 2002). ISCA, 2002. http://dx.doi.org/10.21437/icslp.2002-533.

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Paksoy, E., J. Carlos de Martin, A. McCree, et al. "An adaptive multi-rate speech coder for digital cellular telephony." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.758095.

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Duong, Lyndon R., Bohan Li, Cheng Chen, and Jingning Han. "Multi-Rate Adaptive Transform Coding for Video Compression." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10095879.

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Wang, Dusheng, Lizhong Li, and Jiankang Zhang. "An Adaptive Variable Low Bit Rate Multi-Band Excitation Speech Coder." In 2007 2nd IEEE Conference on Industrial Electronics and Applications. IEEE, 2007. http://dx.doi.org/10.1109/iciea.2007.4318810.

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Villette, S., M. Stefanovic, and A. Kondoz. "Split band LPC based adaptive multi-rate GSM candidate." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.758109.

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Fingscheidt, Tim, Stefanie Aalburg, Sorel Stan, and Christophe Beaugeant. "Network-based vs. distributed speech recognition in adaptive multi-rate wireless systems." In 7th International Conference on Spoken Language Processing (ICSLP 2002). ISCA, 2002. http://dx.doi.org/10.21437/icslp.2002-602.

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Shahbazi, Ali, Amir Hossein Rezaei, Abolghasem Sayadiyan, and Saeed Mosayyebpour. "Data Transmission over GSM Adaptive Multi Rate Voice Channel Using Speech-Like Symbols." In 2010 International Conference on Signal Acquisition and Processing (ICSAP). IEEE, 2010. http://dx.doi.org/10.1109/icsap.2010.72.

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Tan, Zheng-hua, Paul Dalsgaard, and Borge Lindberg. "Adaptive Multi-Frame-Rate Scheme for Distributed Speech Recognition Based on a Half Frame-Rate Front-End." In 2005 IEEE 7th Workshop on Multimedia Signal Processing. IEEE, 2005. http://dx.doi.org/10.1109/mmsp.2005.248653.

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Nishimura, Akira. "Data Hiding in Pitch Delay Data of the Adaptive Multi-Rate Narrow-band Speech Codec." In 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2009. http://dx.doi.org/10.1109/iih-msp.2009.83.

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Mertz, Frank, Herve Taddei, Imre Varga, and Peter Vary. "Voicing controlled frame loss concealment for adaptive multi-rate (AMR) speech frames in voice-over-IP." In 8th European Conference on Speech Communication and Technology (Eurospeech 2003). ISCA, 2003. http://dx.doi.org/10.21437/eurospeech.2003-356.

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Reports on the topic "Adaptive multi-rate speech"

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Atkinson, David, Andrew Catellier, and Stephen Voran. Intelligibility of the Adaptive Multi-Rate Speech Coder in Emergency-Response Environments. Institute for Telecommunication Sciences, 2012. https://doi.org/10.70220/rhw30z86.

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