MATLAB® Software for the Code Excited Linear Prediction Algorithm: The Federal Standard-1016

Ramamurthy, Karthikeyan Natesan; Spanias, Andreas

doi:10.2200/s00252ed1v01y201001ase003

Cited by 17 publications

(9 citation statements)

References 38 publications

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“…1. In CELP, LP is applied to estimate the vocal tract parameters by predicting the current samples of speech frame as a weighted linear combination of previous samples [12] , [14] . Linear Prediction analysis (LP-analysis) is implemented on the framed voice signal s(n) to compute linear prediction coefficients â(i; m) of m order LP-analysis filter A(z) by applying autocorrelation estimate and Levinson-Durbin algorithm (L-D) [14] .…”

Section: A Code Excited Linear Prediction Encoder Processingmentioning

confidence: 99%

High Quality Low Bitrate Voice Codec for Transmission Over Advanced Lte

Edan

Khamiss

2021

Iraqi Journal of ICT

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In mobile communication systems bit-rate reductions while maintaining an acceptable voice quality are necessary to achieve efficiency in channel bandwidth utilization and users satisfaction. As Long-Term Evolution(LTE) converging towards all-IP solutions and supporting VOIP service, the voice signals are converted into coded digital bit-stream and sent over the network. This paper proposes the implementation of codebook excited linear prediction (CELP) voice codec algorithm based on two source-rates of low 9.6Kbps and medium 16Kbps for achieving a perceptible level of voice quality, while efficiently using available bandwidth during the transmission over advanced LTE. The architecture of proposed CELP codec model is implemented to decompose the voice signal into a set of parameters that characterize each particular frame at the encoder part, these parameters are quantized and encoded for transmission to the decoder. The investigation showed that the configuration of the link and the applied CELP codec mode mainly influence on the obtained voice capacity and quality. The quantifying also shows that the voice quality can be traded for the enhanced capacity, since the low rate codec will produce lower voice quality than higher rate codec. Also, this paper is achieved, during theconfiguration of the system with higher channel quality indicator (CQI) index, increasing in the capacity gain to a saturated value of about 500 and 1000 users per cell over 5MHz bandwidth for transmit diversity (TD) and Open-Loop Spatial Multiplexing (OLSM) respectively and up to 1000 and 2000 users per cell over 10MHz channel bandwidth for TD and OLSM respectively.

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Section: A Code Excited Linear Prediction Encoder Processingmentioning

confidence: 99%

High Quality Low Bitrate Voice Codec for Transmission Over Advanced Lte

Edan

Khamiss

2021

Iraqi Journal of ICT

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“…This research explores several tools for feature extraction and pattern matching. Audio features and related methods previously used in speech compression [43][44][45][46][47], voice recognition [48][49][50][51], and speech disorder detection [52][53] will be explored. In addition, a variety of ML approaches [54][55][56][57][58][62][63] will be explored with the emphasis on sparse deep learning methods including Graph Models and Multi-layer Embeddings (GrAMME) [61].…”

Section: Covid-19 Sound Spectral Analysismentioning

confidence: 99%

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Spanias

2020

2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA

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This paper accompanies the keynote speech at IISA-2020 and describes federally funded workforce development research grants and supplements in the area of sensors and machine learning. These programs operate under the auspices of the Sensor Signal and Information Processing (SenSIP) center which is also an Industry University Cooperative Research Center (I/UCRC) sponsored by the National Science Foundation (NSF) and I/UCRC industry members. The first program is an NSF REU site which has trained more than 30 students working on sensor hardware design and machine learning algorithm development. The second program is the NSF IRES site which is collaborative with the University of Cyprus and is focused on sensors and machine learning for energy systems. The most recent program funded by NSF is a Research Experiences for Teachers (RET) program that started in June 2020. This program embeds teachers and community college faculty in SenSIP machine learning projects. Another state funded program in which SenSIP is a partner is MedTech ventures. Our partner MedTech works on training medical technology students, entrepreneurs and engineers to create smart medical solutions for preventive healthcare. SenSIP also received NSF supplements to train students in using machine learning for COVID-19 detection.

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“…This exposed students to the aspects of developing combined hardware-software projects for arts. Furthermore, it helped students understand how apps can be developed to deliver unique arts and media experiences [32,37].…”

Section: Applications In Education and Outreachmentioning

confidence: 99%

Development of Hardware and Softeare for a Game-like Wireless Sound Distribution System

Dharmadhikari

Spanias²,

Ingalls

2016

Modelling, Identification and Control / 834: Parallel and Distributed Computing and Networks / 835: Software Engineering

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Several music players have evolved in multi-dimensional and surround sound systems. The audio players are implemented as software applications for different audio hardware systems. Digital formats and wireless networks allow for audio content to be readily accessible on smart networked devices. Therefore, different audio output platforms ranging from multispeaker high end surround systems to single unit Bluetooth speakers have been developed. A large body of research has been carried out in audio processing, beamforming, sound fields etc. and new formats are developed to create realistic audio experiences. An emerging trend is seen towards high definition AV systems, virtual reality gears as well as gaming applications with multidimensional audio. Next generation media technology is concentrating around Virtual reality experience and devices. It has applications not only in gaming but all other fields including medical, entertainment, engineering, and education. All such systems also require realistic audio corresponding with the visuals. In the project presented in this thesis, a new portable audio hardware system is designed and developed along with a dedicated mobile android application to render immersive surround sound experiences with real-time audio effects. The tablet and mobile phone allow the user to control or "play" with sound directionality and implement various audio effects including sound rotation, spatialization, and other immersive experiences. The thesis describes the hardware and software design, provides the theory of the sound effects, and presents demonstrations of the sound application that was created. ii Dedicated to my Mother and Father iii ACKNOWLEDGMENTS Firstly, I would like to express my deepest gratitude and thank my advisor, Dr. Andreas Spanias, who not only taught and motivated me to pursue research, but also helped me achieve certain level of confidence and maturity. I would also like to thank Dr. Pavan Turaga and Prof. Todd Ingalls for taking out time helping throughout my thesis and agreeing to be a part of my thesis defense committee. Without their valuable time, support and guidance, I could not have finished this work. My Master's term at Arizona State University was productive as well as enjoyable. Thanks to SenSIP LAB and School of Arts, Media and Engineering for providing resources and allowing use of their facilities. Many thanks to their members who made this journey easier and wonderful. Special thanks to Dr. Andreas Spanias for financially assisting me during my Master's study. In addition, I am grateful to have had the opportunity to work with the following people at ASU and I would like to thank them for supporting me in various ways. Thank you Prof. Loren Olson, Assegid Kidane, Peter Weisman and Aaron. I would also like to thank the graduate university staff Lynn, Jenna, Cynthia, Toni, Esther, Darleen and Heather for their timely and kind assistance. A large portion of this journey was shared with my fellow lab-mates Jongmin,

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MATLAB® Software for the Code Excited Linear Prediction Algorithm: The Federal Standard-1016

Cited by 17 publications

References 38 publications

High Quality Low Bitrate Voice Codec for Transmission Over Advanced Lte

High Quality Low Bitrate Voice Codec for Transmission Over Advanced Lte

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Development of Hardware and Softeare for a Game-like Wireless Sound Distribution System

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