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Asilomar Conference on Signals, Systems & Computers

作者： Suat Yeldner M.R. Baraniecki Commun. Technol. Div. COMSAT Lab. Clarksburg MD USA Communications Technology Division Comsat Laboratories Clarksburg MD USA

The mixed harmonic excitation linear predictive speech coder (MHE-LPC) is based on a technique derived from the multi-band excitation (MBE) and multi-band linear predictive coding (MB-LPC) types of speech coding algorithms. The MHE-LPC coder has the potential of producing high quality speech at 4.8 kb/s and below. This coder employs a new pitch estimation and a mixed voicing technique that utilizes a number of bands within the 4 kHz speech bandwidth (6-8 bands), each band having a voicing probability that defines voiced and unvoiced portions of the excitation spectrum. In addition to these, a new DCT based LPC and residual amplitude quantization technique has been developed and is described in this paper. The 3.6 kb/s MHE-LPC coder with 14th order LPC filter was found to produce better speech quality than various other low rate speech coding standards. During an informal listening test, the 3.6 kb/s MHE-LPC vocoder produced better performance than the 3.6 kb/s INMARSAT Mini-M coder. The MHE-LPC algorithm can be easily extended to bit rates between 1.2 and 8 kb/s depending on the applications.

关键词： Speech coding linear predictive coding Power harmonic filters Bandwidth Discrete cosine transforms Quantization Code standards Testing Vocoders Bit rate

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Statistical method for sparse coding of speech including a linear predictive model

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2006年第367期367卷 231-251页

作者： Rufiner, Hugo L. Goddard, John Rocha, Luis F. Torres, Maria E. Univ Nac Entre Rios Fac Ingn RA-3100 Oro Verde Entre Rios Argentina Univ Autonoma Metropolitana Iztapalapa Dept Ing Elect Mexico City DF Mexico Univ Buenos Aires Fac Ingn RA-1053 Buenos Aires DF Argentina

Recently, different methods for obtaining sparse representations of a signal using dictionaries of waveforms have been studied. They are often motivated by the way the brain seems to process certain sensory signals. Algorithms have been developed using a specific criterion to choose the waveforms occurring in the representation. The waveforms are choosen from a fixed dictionary and some algorithms also construct them as a part of the method. In the case of speech signals, most approaches do not take into consideration the important temporal correlations that are exhibited. It is known that these correlations are well approximated by linear models. Incorporating this a priori knowledge of the signal can facilitate the search for a suitable representation solution and also can help with its interpretation. Lewicki proposed a method to solve the noisy and overcomplete independent component analysis problem. In the present paper we propose a modification of this statistical technique for obtaining a sparse representation using a generative parametric model. The representations obtained with the method proposed here and other techniques are applied to artificial data and real speech signals, and compared using different coding costs and sparsity measures. The results show that the proposed method achieves more efficient representations of these signals compared to the others. A qualitative analysis of these results is also presented, which suggests that the restriction imposed by the parametric model is helpful in discovering meaningful characteristics of the signals. (c) 2005 Elsevier B.V. All rights reserved.

关键词： speech analysis and representation sparse coding linear predictive coding basis pursuit matching pursuit independent component analysis

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IMAGE-coding USING 2-D SPACE-VARYING linear predictive FILTERS EXCITED BY A 2-D MULTIPULSE FIELD

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SIGNAL PROCESSING 1989年第1期16卷 13-19页

作者： BARLAUD, M BLANCFERAUD, L MENEZ, J MATHIEU, P Laboratoire Signaux et Systèmes 41 boulevard Napoléon III 06041 Nice Cedex France

This paper deals with information compression for digitized image encoding. Among the different techniques, we discuss the linear predictive coding (LPC) technique. Because of the non-homogeneous nature of images, a space-varying model would yield better results. Therefore, we propose a linear predictive Image coding technique using a space-varying 2-D AR filter. Compression is achieved by an approximation of the AR model input (prediction residual) with a limited number of pulses. The multipulse techniques of Atal and Depreterre are used for the input estimation. First, the space-varying AR 2-D filter is presented for image modeling. Using space basis functions, the LPC parameters are estimated. Then, the problem of the estimation of the synthetic input (location and amplitude of pulses) is stated. Two solutions are given to overcome the computational difficulties that arise when excitation estimation methods (Atal and Depreterre) are applied with a space-varying image model. The efficiency of the proposed solutions is shown by experimental results. Zusammenfassung Dieser Beitrag beschäftigt sich mit der Informationskompression für die Decodierung von digitalisierten Bildern. Von den verschiedenen Techniken besprechen wir die lineare Prädiktive Codierung (LPC). Wegen der nichthomogenen Eigenschaft von Bildern, würde ein ortsabhängiges Modell zu besseren Resultaten führen. Basierend auf dieser Feststellung, schlagen wir eine lineare Prädiktive Bildcodierungstechnik vor, die ein ortsvariantes 2-D AR Filter benutzt. Die Kompression wird durch eine Approximation des AR Modelleingangs (prediction residual) mit einer begrenzten Anzahl von Pulsen erreicht. Die Multipulse-Techniken von Atal und Depreterre werden zur Schätzung des Eingangs benutzt. Zunächst werden ortsvariante AR 2-D Filter für die Bildmodellierung vorgestellt. Unter der Benutzung von örtlichen Basisfunktionen werden die LPC Parameter geschätzt. In einem weiteren Schritt wird das Problem der Schätzung des s

关键词： Image coding linear predictive coding multipulse excitation field space-varying 2-D filter non-stationary AR model

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An Efficient Speech Synthesizer: A Hybrid Monotonic Architecture for Text-to-speech via VAE & LPC-Net with Independent Sentence Length

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2025年 1-25页

作者： Nallabala, Naveen Kumar Souprayen, Balamurugan Ramasamy, Maruthamuthu Penumarti, Seshu Kumar Navuluri, Nuthan Chandu Madanapalle Inst Technol & Sci Dept Comp Applicat Madanapalle Andhra Pradesh India JAIN Sch Comp Sci & Informat Technol Bengaluru Karnataka India Univ East London Dept Architecture Comp & Engn London England

This research delineates the development of a hybrid architecture for text-to-speech (TTS) synthesis, termed the Efficient Speech Synthesizer (ESS). ESS leverages an end-to-end training paradigm to holistically optimize all the parameters, thereby facilitating the synthesis of high-fidelity speech with remarkable efficiency, irrespective of sentence length. The architecture integrates state-of-the-art feed-forward methodologies, including variational autoencoders and linear predictive coding techniques, which impose monotonic constraints on sequence alignment while incurring negligible computational overhead. The primary objective of this study is to engineer a TTS model characterized by minimal complexity and latency, rendering it suitable for deployment across a broad spectrum of computational platforms, ranging from high-performance systems to resource-constrained IoT devices and smartphones.

关键词： Efficient Speech Synthesizer Non-autoregressive Autoregressive Monotonic alignment linear predictive coding Variational autoencoder

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A multi-level feature fusion artificial neural network for classification of acoustic emission signals

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ANNALS OF THE NEW YORK ACADEMY OF SCIENCES 2025年第1期1544卷 223-241页

作者： Huang, Jinliang Zhu, Zhaolin Chen, Zhihao Lu, Haotian Yang, Zijin Zhejiang Univ Hainan Inst Sanya Peoples R China Zhejiang Univ Ocean Coll Zhoushan Peoples R China

In this paper, we introduce FUSION-ANN, a novel artificial neural network (ANN) designed for acoustic emission (AE) signal classification. FUSION-ANN comprises four distinct ANN branches, each housing an independent multilayer perceptron. We extract denoised features of speech recognition such as linear predictive coding, Mel-frequency cepstral coefficient, and gammatone cepstral coefficient to represent AE signals. These features are concatenated to form a new feature called LMGC, which serves as input data for the four branches of FUSION-ANN. The network performs AE signal recognition and classification through forward propagation in each branch, utilizing multi-level feature fusion. We evaluate FUSION-ANN's performance on the ORION-AE benchmark dataset, which contains AE signals from various loading conditions simulating loosening phenomena in aeronautics, automotive, and civil engineering structures. Our results demonstrate an impressive average accuracy of 98% in AE signal classification. Additionally, FUSION-ANN boasts high training efficiency, robustness, and accuracy, making it suitable for reliable AE signal analysis. However, given the current limitations, we aim to conduct more comprehensive investigations in the future. Our plan includes further testing of the network's performance across various categories of AE signals to assess its generality. Additionally, we will select richer and more efficient feature sets to characterize these signals.

关键词： artificial neural network classification of AE signals gammatone cepstral coefficient linear predictive coding Mel-frequency cepstral coefficients multi-level feature fusion

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coding-Based Data Compression for Multichannel SAR

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Martone, Michele Gollin, Nicola Krieger, Gerhard Imbembo, Ernesto Rizzoli, Paola German Aerosp Ctr DLR Microwaves & Radar Inst D-82234 Wessling Germany European Space Agcy ESA NL-2201 AZ Noordwijk Binnen Netherlands

Multichannel synthetic aperture radar (MC-SAR) allows for high-resolution imaging of a wide swath (HRWS), at the cost of acquiring and downlinking a significantly larger amount of data, compared with conventional SAR systems. In this letter, we discuss the potential of efficient data volume reduction (DVR) for MC-SAR. Specifically, we focus on methods based on transform coding (TC) and linear predictive coding (LPC), which exploit the redundancy introduced in the raw data by the finer azimuth sampling peculiar to the MC system. The proposed approaches, in combination with a variable-bit quantization, allow for the optimization of the resulting performance and data rate. We consider three exemplary yet realistic MC-SAR systems, and we conduct simulations and analyses on synthetic SAR data considering different radar backscatter distributions, which demonstrate the effectiveness of the proposed methods.

关键词： Azimuth Synthetic aperture radar Doppler effect Quantization (signal) Backscatter Encoding Autocorrelation Transform coding Redundancy linear predictive coding Data volume reduction (DVR) multichannel (MC) synthetic aperture radar (SAR) raw data quantization SAR

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Perceptual linear predictive noise modelling for sinusoid-plus-noise audio coding

Perceptual linear predictive noise modelling for sinusoid-pl...

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

作者： R.C. Hendriks R. Heusdens J. Jensen Department of Mediamatics Delft University of Technnology Delft Netherlands

Sinusoidal coding of an audio subject to a bit-rate constraint, in general, results in a noise-like residual signal. This residual signal is of high perceptual importance; reconstruction of audio using the sinusoidal representation only typically results in an artificial sounding reconstruction. We present a new method, called perceptual linear predictive coding (PLPC), where the residual is encoded by applying LPC in the perceptual domain. This method minimizes a perceptual modelling error and therefore represents only residual components that are of perceptual relevance, while automatically discarding components masked by the sinusoidally coded part. Subjective listening tests show that PLPC performs significantly better than ordinary LPC as a sinusoidal residual coding technique. Furthermore, PLPC combined with a flexible segmentation and model order allocation algorithm leads to a significant gain in terms of R/D performance for fragments with fast changing characteristics.

关键词： predictive models Audio coding linear predictive coding Acoustic noise Encoding Testing Performance evaluation Performance gain Frequency Filter bank

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Efficient coding of speech spectral envelope using a non-linear two-dimensional predictive method in the index domain

Efficient coding of speech spectral envelope using a non-lin...

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IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM)

作者： H.R. Sadegh Mohammadi W.H. Holmes Electrical Engineering Research Center Jahad Daneshgahi IUST University Tehran Iran School of Electrical Engineering University of New South Wales Sydney Australia

Line spectral frequencies (LSFs) are the most popular parameters for spectrum quantization in speech coders using linear prediction. A new method for the quantization of the LSFs is proposed in this paper. This method is a scalar quantization scheme based on a nonlinear two-dimensional prediction in the index domain, and hereafter will be referred to as predictive delta adaptive scalar quantization (PDASQ). It is shown that it can be implemented efficiently with negligible computational overhead and memory requirements compared to the simple scalar quantization method. Although PDASQ needs lower bit rates, its quantization distortion is of the same order as that of the conventional scalar quantization. Satisfactory performance of the new method is verified through experimental tests using computer simulation.

关键词： Speech coding Quantization Frequency Costs Australia Bit rate Testing Computer simulation predictive models linear predictive coding

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predictive coding of Speech Using Microphone/Speaker Adaptation and Vector Quantization

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1994年第2期2卷 266-273页

作者： Aarskog, Alv I. Guren, Hans C. ABB Corp Res N-1361 Billingstad Norway

A problem with speech coders based on trained quantizers is their lack of robustness against variations in the microphone and input filter response. In this paper a simple backward adaptive prefiltering technique is proposed as a means of improving the robustness and quality of a speech coder at no cost in bit rate. The technique is particularly useful in conjunction with vector quantization (VQ) of the linear predictive coding (LPC) parameters. The performance of the prefilter, denoted a microphone and speaker adaptation (MSA) filter, has been evaluated in terms of prediction gain and spectral distortion, together with objective and subjective quality of a 7.5 kbit/s CELP speech coder. In this coder a 10-bit direct VQ of the LPC parameters using the residual energy distortion measure has been applied. This is consistent with the covariance method of LPC analysis. Simulation results illustrate that the MSA filter significantly improves the performance and robustness of the LPC VQ against changes in the input response. The 7.5 kbit/s CELP with a trained excitation codebook and MSA was found to be clearly better (subjectively and objectively) than the one without MSA. The coder with MSA also showed to be practically indistinguishable from the same CELP with unquantized LPC coefficients and a stochastic excitation codebook.

关键词： predictive coding Speech coding Microphones linear predictive coding Robustness Filters Costs Bit rate Vector quantization Performance gain

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Analysis of a hybrid fractal-predictive-coding compression scheme

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2003年第6期18卷 483-495页

作者： Tong, CS Pi, M Hong Kong Baptist Univ Dept Math Kowloon Tong Hong Kong Peoples R China Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada

There has been tremendous progress in fractal compression since the pioneer work of Barnsley and Jacquin in the late 1980s. As the encoding time complexity issues are gradually being solved, there is a steady growth of applications of fractals, especially in hybrid systems. However, such fractal hybrid systems tend to be rather difficult to analyze, and part of that difficulty lies in the quantization of the scaling and luminance offset parameters adopted in most fractal compression schemes. In this paper, we present theoretical and empirical justification for a well-known but underused alternative parametrization for the fractal affine transform. In particular, we shall present a detailed analysis of a hybrid fractal-LPC (linear predictive coding) compression scheme using the aforementioned alternative affine transform parameters. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： fractal image compression linear predictive coding fractal bit allocation

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