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Rate Control for Entropy-Coded LPC: Application to Packet-Based Fronthauling

IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第9期70卷 5898-5911页

作者： Ramalho, Leonardo Fortuna, Joary Lu, Chenguang Berg, Miguel Almeida, Igor Klautau, Aldebaro Fed Univ Para Comp & Telecommun Engn Dept BR-66615170 Belem Para Brazil Ericsson Res S-16480 Kista Sweden Ericsson Res Brazil BR-13337300 Indaiatuba Brazil

linear prediction and entropy coding are widely used in speech, audio, video, and other coding systems. Rate-distortion optimization (RDO) is another relevant coding technique, which is especially useful when the encoder performance depends on tunable parameters from distinct coding subsystems. This work presents a RDO strategy for variable-rate encoders that use linear prediction and entropy coding. The method is suitable when the signal to be compressed is short-term stationary and the prediction order varies over signal segments. The second contribution of this paper is to customize the method to the compression of time-domain OFDM-based cellular radio signals. The method enables a 4G / 5G packet-based fronthaul to flexibly trade-off rate and distortion depending on the amount of information to be transmitted and, consequently, benefit from multiplexing gain. Simulation results with LTE signals show that the proposed method achieves improved performance with a relatively low computational cost, while operating with time-domain signals under different traffic scenarios.

关键词： Quantization (signal) Distortion Loading Entropy Power capacitors Encoding Rate distortion theory Data compression linear predictive coding entropy codes fronthaul

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Ultrasound- and MRI-based Speech Synthesis Applying Neural Networks 25

Ultrasound- and MRI-based Speech Synthesis Applying Neural N...

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25th International Carpathian Control Conference (ICCC)

作者： Trencsenyi, Reka Czap, Laszlo Univ Debrecen Dept Elect & Elect Engn Debrecen Hungary Univ Miskolc Inst Automat & Infocommun Miskolc Hungary

ISBN: (纸本)9798350350708;9798350350715

Starting from 2D dynamic ultrasound and MRI sources recording the movement of the vocal organs and the speech signal of the speaker in a simultaneous and synchronised manner, we produce machine speech by means of artificial intelligence. As visual objects, we use tongue and palate contours fitted automatically to the anatomic boundaries of the ultrasound and MRI images, and for training, we extract geometric information from these contours, as the change of their shape fundamentally describes the movement of the vocal organs during articulation. The geometric data consist of radial distances between the tongue and palate contours and coefficients of the discrete cosine transform of the curves, respectively. Relying on this dataset, parameters connected to the acoustic content of the speech signal are trained by the network. These parameters can be interpreted in the framework of the acoustic tube model of the vocal tract, and according to this, reflection coefficients and areas of the articulation channel are to be trained. In this study, vowels, words, and sentences are synthesised using linear predictive coding and the acoustic tube model.

关键词： speech synthesis acoustic tube model linear predictive coding neural networks anatomic contour tracking ultrasound and MRI records

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Modeling and Reproducing Textile Sensor Noise: Implications for Textile-Compatible Signal Processing Algorithms

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第1期26卷 243-253页

作者： Tian, Yupeng Kabir, Muammar Abdizadeh, Mohammad Poursartip, Behnaz Mahnam, Amin Bhattachan, Presish Eskandarian, Ladan Meghrazi, Milad Alizadeh Mellal, Idir Popovic, Milos R. Lankarany, Milad Univ Hlth Network UHN Krembil Res Inst Toronto ON M5T 1M8 Canada Univ Hlth Network UHN KITE Res Inst Toronto ON M5G 2A2 Canada Univ Toronto Inst Biomed Engn Toronto ON M5S 3G9 Canada Myant Inc Toronto ON M9W 1B6 Canada Univ Waterloo Waterloo ON N2L 3G1 Canada Univ Toronto Dept Mat Sci & Engn Toronto ON M5S 3G9 Canada Univ Toronto Inst Biomed Engn Toronto ON M5S Canada Univ Toronto Dept Physiol Toronto ON M5S 1A8 Canada

Smart textiles provide an opportunity to simultaneously record various electrophysiological signals, e.g., ECG, from the human body in a non-invasive and continuous manner. Accurate processing of ECG signals recorded using textile sensors is challenging due to the very low signal-to-noise ratio (SNR). Signal processing algorithms that can extract ECG signals out of textile-based electrode recordings, despite low SNR are needed. Presently, there are no textile ECG datasets available to develop, test and validate these algorithms. In this paper we attempted to model textile ECG signals by adding the textile sensor noise to open access ECG signals. We employed the linear predictive coding method to model different features of this noise. By approximating the linear predictive coding residual signals using Kernel Density Estimation, an artificial textile ECG noise signal was generated by filtering the residual signal with the linear predictive coding coefficients. The synthetic textile sensor noise was added to the MIT-BIH Arrhythmia Database (MITDB), thus creating Textile-like ECG dataset consisting of 108 trials (30 min each). Furthermore, a Python code for generating textile-like ECG signals with variable SNR was also made available online. Finally, to provide a benchmark for the performance of R-peak detection algorithms on textile ECG, the five common R-peak detection algorithms: Pan & Tompkins, improved Pan & Tompkins (in Biosppy), Hamilton, Engelse, and Khamis, were tested on textile-like MITDB. This work provides an approach to generating noisy textile ECG signals, and facilitating the development, testing, and evaluation of signal processing algorithms for textile ECGs.

关键词： Textiles Electrocardiography Feature extraction Electrodes Motion artifacts Signal processing algorithms Signal to noise ratio Kernel density estimation linear predictive coding R-peak detection textile sensor noise (TSN)

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Audio Watermarking for Security and Non-Security Applications

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IEEE ACCESS 2022年 10卷 12654-12677页

作者： Charfeddine, Maha Mezghani, Eya Masmoudi, Salma Ben Amar, Chokri Alhumyani, Hesham Univ Sfax Natl Engn Sch Sfax ENIS REGIM REs Grp Intelligent Machines Sfax 3038 Tunisia Taif Univ Coll Comp & Informat Technol Dept Comp Engn At Taif 21944 Saudi Arabia

The digitization of audiovisual data is significantly increasing. Thus, to guarantee the protection of the intellectual properties of this digital content, watermarking has appeared as a solution. Watermarking can be used in reality in several types of applications that target two different contexts: the first for security applications and the second for non-security ones. In this paper, we carry a big interest in studying these two types of applications. Moreover, we propose a first digital watermarking scheme for security copyright protection applications, where we have involved neural network architecture in the insertion and detection processes, and integrated some masking phenomena of the human psychoacoustic model with linear predictive coding spectral envelope estimation of the audio file. Experiments proved the efficiency of exploiting perceptual masking with spectral envelope consideration in terms of imperceptibility and robustness results. In addition, we suggest a second audio watermarking technique for non-security content characterization applications based on a deep learning classification architecture. In this scheme, the extracted watermark advises about the audio class: music or speech, speaker gender, and emotion. The reported results indicated that the suggested scheme achieved a higher performance at the classification level, as well as at the watermarking properties.

关键词： Watermarking Security Robustness Standards Estimation Computer architecture Transform coding Copyright protection human psychoacoustic model linear predictive coding audio content characterization deep learning architecture

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State-space approach to linear predictive coding of speech - A comparative assessment

State-space approach to linear predictive coding of speech -...

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8th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Irshad, Azeem Salman, Muhammad Natl Univ Sci & Technol Coll Elect & Mech Engn Islamabad Pakistan

ISBN: (纸本)9781467363228;9781467363204

Speech coders are fundamental component in telecommunication and multimedia infrastructure. Several systems like, mobile telephony, voice over internet protocol (VOIP), audio conferencing etc., rely on efficient speech coding. Speech coders strive to provide low-bit rate maintaining the same speech quality and intelligibility. linear predictive coding uses spectral properties of the speech to "optimize" the coder's performance for human ear. In this paper we perform a comparative assessment of speech coding performance of some state-space filters to give designers an insight into capabilities of these filters. The filters considered are Kalman filter, state-space recursive least-squares (SSRLS) and SSRLS with adaptive memory (SS-RLSWAM). The results of RLS and LMS are also quoted. The performance is judged in terms of perceptual evaluation of speech quality (PESQ) and prediction gain.

关键词： Speech coding Kalman Filter SSRLS SSRL-SWAM linear predictive coding

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Improving the Performance of Zero-Resource Children's ASR System through Formant and Duration Modification based Data Augmentation 14

Improving the Performance of Zero-Resource Children's ASR Sy...

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14th IEEE International Conference on Signal Processing and Communications (SPCOM)

作者： Shahnawazuddin, Syed Kumar, Vinit Kumar, Avinash Ahmad, Waquar NIT Patna Dept ECE Patna Bihar India NIT Sikkim Dept ECE Sikkim India NIT Calicut Dept ECE Kozhikode India

ISBN: (数字)9781665482509

ISBN: (纸本)9781665482509

Developing an automatic speech recognition (ASR) system for children's speech is extremely challenging due to the unavailability of data from the child domain for the majority of the languages. Consequently, in such zero-resource scenarios, we are forced to develop an ASR system using adults' speech for transcribing data from child speakers. However, differences in formant frequencies and speaking-rate between the two groups of speakers degrade recognition performance. To reduce the said mismatch, out-of-domain data augmentation approaches based on formant and duration modification are proposed in this work. For that purpose, formant frequencies of adults' speech training data are up-scaled using warping of linear predictive coding coefficients. Next, the speaking-rate of adults' data is also increased through time-scale modification. Due to simultaneous altering of formant frequencies and duration of adults' speech and then pooling the modified data into training, the acoustic mismatch due to the aforementioned factors gets reduced. This, in turn, enhances the recognition performance significantly. Additional improvement is obtained by combining the recently reported voice-conversion-based data augmentation technique with the proposed ones. On combining the proposed and voice-conversion-based data augmentation techniques, a relative reduction of nearly 32:3% in word error rate over the baseline is obtained.

关键词： Training Speech coding Error analysis Conferences Training data Signal processing linear predictive coding

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Web Based Voice Assistant for Railways Using Deep Learning Approach 2nd

Web Based Voice Assistant for Railways Using Deep Learning A...

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2nd International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

作者： Vadamodula, Prasad Cristin, R. Daniya, T. GMRIT Dept Comp Sci & Engn Rajam India GMRIT Dept Informat Technol Rajam India

ISBN: (纸本)9789811925412;9789811925405

Speech recognition is the ability of machine or program to respond to spoken commands. It works using algorithms through acoustic and language modelling. It is a technique which enables hands-free control of various devices to interact with machines. This project deals with developing a speaking machine using the deep learning algorithm named linear predictive coding (LPC) or linear predictive Speech Synthesis (LPSS) which can be used by customers or passengers in a railway station for an enquiry. This helps the people to enquire the details of trains for the limited questionnaire, identified by the name of the train with reference station location and its availability of trains at the real-time for required destinations. This system gives the information about the platform number for all the train arrivals with the names of the trains and the details of the ticket fare for different destinations. These can be done through giving the voice input to the developed system.

关键词： Communication Deep learning Interaction and speech recognition linear predictive coding linear predictive speech synthesis

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Challenges in Developing a Real-Time Bee-Counting Radar

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SENSORS 2023年第11期23卷 5250-5250页

作者： Williams, Samuel M. Aldabashi, Nawaf Cross, Paul Palego, Cristiano Bangor Univ Sch Comp Sci & Engn Bangor LL57 2DG Wales Bangor Univ Sch Nat Sci Bangor LL57 2DG Wales

Detailed within is an attempt to implement a real-time radar signal classification system to monitor and count bee activity at the hive entry. There is interest in keeping records of the productivity of honeybees. Activity at the entrance can be a good measure of overall health and capacity, and a radar-based approach could be cheap, low power, and versatile, beyond other techniques. Fully automated systems would enable simultaneous, large-scale capturing of bee activity patterns from multiple hives, providing vital data for ecological research and business practice improvement. Data from a Doppler radar were gathered from managed beehives on a farm. Recordings were split into 0.4 s windows, and Log Area Ratios (LARs) were computed from the data. Support vector machine models were trained to recognize flight behavior from the LARs, using visual confirmation recorded by a camera. Spectrogram deep learning was also investigated using the same data. Once complete, this process would allow for removing the camera and accurately counting the events by radar-based machine learning alone. Challenging signals from more complex bee flights hindered progress. System accuracy of 70% was achieved, but clutter impacted the overall results requiring intelligent filtering to remove environmental effects from the data.

关键词： Apis mellifera honeybee radar machine learning support vector machine linear predictive coding log area ratios

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RoIP Compression Method in Burst Noise Upstream Environment for CATV Network

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IEEE TRANSACTIONS ON BROADCASTING 2021年第1期67卷 351-355页

作者： Cui, Chengyu Park, Sungkwon Hanyang Univ Dept Elect & Comp Engn Seoul 04763 South Korea

A hybrid fiber coaxial (HFC) network uses both optical fibers and coaxial cables for cable TV networks. Coaxial cables are mainly used for access networks. Recently, fiber to the home (FTTH) is being widely deployed. For cable TV companies to utilize readily deployed FTTH, legacy coax network-based customer premise equipment (CPE) such as cable modems and set top boxes may have to be replaced with fiber network based CPE. In order to reduce the expenditure incurred in this process, radio over Internet Protocol (RoIP) technology has been proposed. In the RoIP system, RF signal for coax generated by legacy CPE toward the headend is first sampled, and digitized, and then encapsulated in IP packets, which are then sent to the headend using FTTH access technologies such as passive optical network (PON). In the process of digitizing RF signal, a huge amount of data much more than the base band data is generated. So, a data compression method is required to lower the upstream bit rate maintaining the signal quality. Most compression methods are significantly degraded due to the interference caused by burst noise in the upstream link. Therefore, this paper proposes a new compression method which combines linear predictive coding (LPC), block scaling and non-linear quantization for CATV upstream links. The proposed method is demonstrated to greatly reduce the impact of burst noise while maintaining the compression performance.

关键词： Quantization (signal) RF signals Uplink Cable TV linear predictive coding IP networks Hybrid fiber coaxial cables Hybrid fiber coaxial radio over IP compression method linear predictive coding block scaling

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Self-supervised Prosody Learning at Phoneme-level with Momentum Contrast for Speech Synthesis

Self-supervised Prosody Learning at Phoneme-level with Momen...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhao-Ci Liu Ya-Jun Hu Liping Chen Zhen-Hua Ling National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei P.R.China iFLYTEK Research iFLYTEK Co. Ltd. China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper investigates leveraging large-scale speech data to enhance prosodic modeling in speech synthesis, and introduces a model named SP2MC which achieves self-supervised prosody learning at phoneme-level with momentum contrast. This model incorporates dual convolutional encoders for speech and linear predictive coding (LPC) residual inputs to generate phoneme-level embeddings, which are masked and processed by a Transformer model to produce prosody representations. Two supervision modules are employed to generate phoneme-level supervision from speech waveforms and residuals. Momentum contrast is utilized to manage negative sample selection in contrastive learning. Finally, the SP2MC representations are integrated into a Fastspeech2-based acoustic model for speech synthesis. Experimental results indicate that the naturalness of speech synthesized by the proposed method is significantly better than that of baselines.

关键词： Convolutional codes Speech coding Convolution predictive models Speech enhancement Transformers Acoustics Data models linear predictive coding Text to speech

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