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USA: predictive coding and the Changing Legal Marketplace

LEGAL ETHICS 2013年第2期16卷 380-383页

作者： Cooper, Benjamin P. Univ Mississippi Sch Law Law University MS 38677 USA

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关键词： linear predictive coding SPEECH processing systems LEGAL professions COST effectiveness COMPUTERS in law enforcement

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Nonlinear Long-Term Prediction of Speech Based on Truncated Volterra Series

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2012年第3期20卷 1069-1073页

作者： Despotovic, Vladimir Goertz, Norbert Peric, Zoran Univ Belgrade Tech Fac Bor Bor 19210 Serbia Vienna Univ Technol Inst Telecommun A-1040 Vienna Austria Univ Nis Fac Elect Engn Nish 18000 Serbia

Previous studies of nonlinear prediction of speech have been mostly focused on short-term prediction. This paper presents long-term nonlinear prediction based on second-order Volterra filters. It will be shown that the presented predictor can outperform conventional linear prediction techniques in terms of prediction gain and "whiter" residuals.

关键词： linear predictive coding long-term prediction nonlinear filters volterra series

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An alarm method for a Loose Parts Monitoring System

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SHOCK AND VIBRATION 2012年第4期19卷 753-761页

作者： Cao, Yanlong He, Yuanfeng Zheng, Huawen Yang, Jiangxin Zhejiang Univ Inst Mfg Engn Hangzhou 310027 Zhejiang Peoples R China

In order to reduce the false alarm rate and missed detection rate of a Loose Parts Monitoring System (LPMS) for Nuclear Power Plants, a new hybrid method combining linear predictive coding (LPC) and Support Vector Machine (SVM) together to discriminate the loose part signal is proposed. The alarm process is divided into two stages. The first stage is to detect the weak burst signal for reducing the missed detection rate. Signal is whitened to improve the SNR, and then the weak burst signal can be detected by checking the short-term Root Mean Square (RMS) of the whitened signal. The second stage is to identify the detected burst signal for reducing the false alarm rate. Taking the signal's LPC coefficients as its characteristics, SVM is then utilized to determine whether the signal is generated by the impact of a loose part. The experiment shows that whitening the signal in the first stage can detect a loose part burst signal even at very low SNR and thusly can significantly reduce the rate of missed detection. In the second alarm stage, the loose parts' burst signal can be distinguished from pulse disturbance by using SVM. Even when the SNR is -15 dB, the system can still achieve a 100% recognition rate.

关键词： Alarm signal Loose Parts Monitoring System Support Vector Machine linear predictive coding

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DWT and LPC based feature extraction methods for isolated word recognition

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EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2012年第1期2012卷 1-7页

作者： Nehe, Navnath S. Holambe, Raghunath S. Pravara Rural Engn Coll Dept Instrumentat Engn Loni 413736 Maharashtra India SGGS Inst Engn & Technol Nanded Maharashtra India

In this article, new feature extraction methods, which utilize wavelet decomposition and reduced order linear predictive coding (LPC) coefficients, have been proposed for speech recognition. The coefficients have been derived from the speech frames decomposed using discrete wavelet transform. LPC coefficients derived from subband decomposition (abbreviated as WLPC) of speech frame provide better representation than modeling the frame directly. The WLPC coefficients have been further normalized in cepstrum domain to get new set of features denoted as wavelet subband cepstral mean normalized features. The proposed approaches provide effective (better recognition rate), efficient (reduced feature vector dimension), and noise robust features. The performance of these techniques have been evaluated on the TI-46 isolated word database and own created Marathi digits database in a white noise environment using the continuous density hidden Markov model. The experimental results also show the superiority of the proposed techniques over the conventional methods like linear predictive cepstral coefficients, Mel-frequency cepstral coefficients, spectral subtraction, and cepstral mean normalization in presence of additive white Gaussian noise.

关键词： feature extraction linear predictive coding discrete wavelet transform cepstral mean normalization hidden Markov model

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Low-complexity PARCOR coder designed for entropy coding of prediction residuals

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ACOUSTICAL SCIENCE AND TECHNOLOGY 2012年第4期33卷 262-265页

作者： Kamamoto, Yutaka Moriya, Takehiro Harada, Noboru NTT Corp NTT Commun Sci Labs 3-1 Morinosato Wakamiya Atsugi Kanagawa 2430198 Japan

We described the low-complexity PARCOR coder de- signed for entropy coding of prediction residual signals. The quantization of PARCOR coefficients is based on a criterion that minimizes the entropy of the prediction residual signals. The devised NLBS algorithm is included in the G.711.0 standard because this tool shows efficient performance in terms of bit reduction and complexity. It will be widely used in the near future because G.711.0 with the described low- complexity tool can compress the data rate of G.711, the prevailing speech coding technology.

关键词： Speech coding Lossless compression linear predictive coding Standardization ITU-T G.711.0

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Data reconstruction for missing electrocardiogram using linear predictive coding

Data reconstruction for missing electrocardiogram using line...

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IEEE International Conference on Mechatronics and Automation

作者： Nipon Theera-Umpon Panyaphon Phiphatkhunarnon Sansanee Auephanwiriyakul Department of Electrical Engineering Faculty of Engineering and the Biomedical Engineering Center Chiang Mai University Thailand Department of Computer Engineering Faculty of Engineering Chiang Mai University Thailand Department of Computer Engineering Faculty of Engineering and the Biomedical Engineering Center Chiang Mai University Thailand

An electrocardiogram (ECG) reconstruction method based on a linear prediction technique is proposed in this paper. The method can reconstruct a rather long missing parts of ECG signals. Each missing data segment may cover 1 to 8 beats. The data used in the experiments are from the MIT-BIH normal sinus rhythm database. The experimental results show that our method can perform very well. The reconstructed signals are visually very close to the ground truths. The numerical evaluation also shows that the proposed method yields good results on the heart rate variability (HRV) measure derivation. It gives the time-domain HRV measures that are very close to the ground truths. Its performance is also better than the method commonly used by experts in which the abnormal beats are removed before calculating the HRV measures.

关键词： linear predictive coding Electrocardiography Heart rate variability Image reconstruction Reconstruction algorithms Parameter estimation Time measurement Time domain analysis Strips Signal processing

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Speech Enhancement and Recognition of Compressed Speech Signal in Noisy Reverberant Conditions

Speech Enhancement and Recognition of Compressed Speech Sign...

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1st International Conference on Information Systems Design and Intelligent Applications (INDIA 2012)

作者： Suman, Maloji Khan, Habibulla Latha, M. Madhavi Kumari, Devarakonda Aruna CSI Guntur Andhra Pradesh India KLU Guntur Andhra Pradesh India JNTU Coll Engn Hyderabad Andhra Pradesh India

ISBN: (纸本)9783642274428

Speech compression, enhancement and recognition in noisy, reverberant conditions is a challenging task. In this paper a new approach to this problem, which is developed in the framework of probabilistic random modeling, speech coding techniques are commonly used in low bit rate analysis and synthesis. coding algorithms seek to minimize the bit rate in the digital representation of a signal without an objectionable loss of signal quality in the process. Speech enhancement aims to improve speech quality by using various algorithms This paper deals with multistage vector quantization technique used for coding of narrow band speech signals. The parameter used for coding of speech signals are the line spectral frequencies, so as to ensure filter stability after quantization. A new approach incorporates the information about statistical random nature of uncompressed speech signal using LBG algorithm. The code books used for quantization are generated by using Linde, Buzo and Gray(LBG) algorithm. Speech model is characterized by LPC coefficients and parameterized by the coefficients of the reverberation filters The results of the multistage vector quantizer are compared with unconstrained vector quantization Technique. The performance of quantization is measured in terms of spectral distortion measured in dB, Computational complexity measured in KFlops and Memory Requirements measured in Floats. From the results it can be proved that multistage vector quantization is having better spectral distortion performance, less computational complexity and memory requirements when compared to unconstrained vector quantization. The proposed approach yields significantly estimating the parameters from the data, better performance in both signal to noise ratio and subjective filter methods

关键词： linear predictive coding Multi stage vector quantization Line Spectral Frequencies (LSF)

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Low-Complexity Vector Precoding for Multi-user Systems

Low-Complexity Vector Precoding for Multi-user Systems

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46th Asilomar Conference on Signals, Systems and Computers

作者： Barrenechea, Maitane Burg, Andreas Mendicute, Mikel Univ Mondragon Dept Elect & Comp Sci Arrasate Mondragon 20500 Spain Ecole Polytech Fed Lausanne Inst Elect Engn CH-1015 Lausanne Switzerland

ISBN: (纸本)9781467350518

Vector precoding enables non-cooperative signal acquisition in the multi-user broadcast channel. The performance advantage with respect to the more straightforward linear precoding algorithms comes as a consequence of an added perturbation vector, which enhances the properties of the precoded signal. Nevertheless, the computation of the perturbation signal entails a search for the closest point in an infinite lattice, which is known to belong in the class of non-deterministic polynomial-time hard (NP-hard) problems. This contribution presents a novel tree search scheme that achieves an error-rate performance that is close to the optimum given by the sphere encoder, but with a significantly simpler tree-search structure that only considers the most promising nodes for expansion. With the aim of better showcasing the low-complexity and simple datapath of the proposed tree-search technique, its hardware implementation on a 65 nm ASIC target device has been performed.

关键词： computational complexity linear predictive coding polynomials signal detection

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Classification of Speaker Accent using Hybrid DWT-LPC Features and K-Nearest Neighbors in Ethnically Diverse Malaysian English

Classification of Speaker Accent using Hybrid DWT-LPC Featur...

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IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE)

作者： Yusnita, M. A. Paulraj, M. P. Yaacob, Sazali Shahriman, A. B. Univ Teknol MARA Fac Elect Engn Permatang Pauh 13500 P Pinang Malaysia Univ Malaysia Perlis Sch Mechatron Engn Ulu Pauh 02600 Malaysia

ISBN: (纸本)9781467330336;9781467323024

Accent is a major cause of variability in automatic speaker-independent speech recognition systems. Under certain circumstances, this event introduces unsatisfactory performance of the systems. In order to circumvent this deficiency, accent analyzer in preceding stage could be a smart solution. This paper proposes a rather new approach of hybrid way to optimize the extraction of accent from speech utterances over other facets using linear predictive coefficients (LPC) derived from discrete wavelet transform (DWT). The constructed features were used to model an accent recognizer, implemented based on K-nearest neighbors. Experimental results showed that the hybrid dyadic-X DWT-LPC features were highly correlated to the Malay, Chinese and Indian accents of Malaysian English speakers through an increase of classification rate of 9.28% over the conventional LPC method.

关键词： Discrete Wavelet Transform linear predictive coding Accent Classification K-nearest Neighbors Malaysian English

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Energy first search scheme for spectral envelope vector quantisation of linear predictive coding residual

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IET SIGNAL PROCESSING 2007年第4期1卷 190-196页

作者： Chen, F. -K. Tsai, P. -Y. So Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan

linear predictive coding (LPC) residual is an important component for analysis-by-synthesis speech coders. Using spectral envelope vector quantisation (SEVQ) for LPC residual signals, MPEG-4 harmonic vector excitation coding (HVXC) speech coder is able to change the tone and speed for decoding bitstreams. A modified version is proposed to the simplified hearing-based SEVQ (SSEVQ) approach proposed by Wang and Yang. In addition, the energy first search scheme for SEVQ (EFSEVQ) is proposed in order to reduce the computational complexity of SEVQ for MPEG-4 HVXC speech coder. The proposed EFSEVQ scheme also agrees with human hearing properties. Simulation results reveal that the proposed EFSEVQ search scheme not only reduces the computational complexity but also preserves the quality of encoded speech.

关键词： vector quantisation analysis-by-synthesis speech coders speech coding linear predictive coding computational complexity LPC residual signals hearing-based SEVQ Speech and audio coding spectral envelope vector quantisation energy first search scheme MPEG-4 HVXC speech coder MPEG-4 harmonic vector excitation coding Codecs, coders and decoders vocoders linear predictive coding residual

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