Speech coding is a very important area of research in digital signal processing. It is a fundamental element of digital communications and has progressed at a fast pace in parallel to the increase of demands in telecommunication services and capabilities. Most of the speech coders reported in the literature are based on linear prediction (LP) analysis. Code Excited Linear Predictive (CELP) coder is a typical and popular example of this class of coders. This coder performs LP analysis of speech for extracting LP coefficients and employs an analysis-by-synthesis procedure to search a stochastic codebook to compute the excitation signal. The method used for performing LP analysis plays an important role in the design of a CELP coder. The autocorrelation method is conventionally used for LP analysis. Though this works reasonably well for noise-free (clean) speech, its performance goes down when signal is corrupted by noise. Spectral analysis of speech signals in noisy environments is an aspect of speech coding that deserves more attention. This dissertation studies the application of recently proposed robust LP analysis methods for estimating the power spectrum envelope of speech signals. These methods are the moving average, moving maximum and average threshold methods. The proposed methods will be compared to the more commonly used methods of LP analysis, such as the conventional autocorrelation method and the Spectral Envelope Estimation Vocoder (SEEVOC) method. The Linear Predictive Coding (LPC) spectrum calculated from these proposed methods are shown to be more robust. These methods work as well as the conventional methods when the speech signal is clean or has high signal-to-noise ratio. Also, these robust methods give less quantisation distortion than the conventional methods. The application of these robust methods for speech compression using the CELP coder provides better speech quality when compared to the conventional LP analysis methods.

Over the past two decades there has been substantial growth in speech communications and new speech related applications. Bandwidth constraints led researchers to investigate ways of compressing speech signals whilst maintaining speech quality and intelligibility so as to increase the possible number of customers for the given bandwidth. Because of this a variety of speech coding techniques have been proposed over this period. At the heart of any proposed speech coding method is quantisation of the speech production model parameters that need to be transmitted to the decoder. Quantisation is a controlling factor for the targeted bit rates and for meeting quality requirements. The objectives of the research presented in this thesis are twofold. The first enabling the development of a very low bit rate speech coder which maintains quality and intelligibility. This includes increasing the robustness to various operating conditions as well as enhancing the estimation and improving the quantisation of speech model parameters. The second objective is to provide a method for enhancing the performance of an existing speech related application. The first objective is tackled with the aid of three techniques. Firstly, various novel estimation techniques are proposed which are such that the resultant estimated speech production model parameters have less redundant information and are highly correlated. This leads to easier quantisation (due to higher correlation) and therefore to bit saving. The second approach is to make use of the joint effect of the quantisation of spectral parameters (i.e. LSF and spectral amplitudes) for their big impact on the overall bit allocation required. Work towards the first objective also includes a third technique which enhances the estimation of a speech model parameter (i.e. the pitch) through a robust statistics-based post-processing (or tracking) method which operates in noise contaminated environments. Work towards the second objective focuses on an application where speech plays an important role, namely echo-canceller and noise-suppressor systems. A novel echo-canceller method is proposed which resolves most of the weaknesses present in existing echo-canceller systems and improves the system performance.

No description available.

This thesis focuses on the design and analysis of an optimal filter that is capable of making one-step-ahead prediction of a bandlimited signal while attenuating unwanted noise. First, the filter optimization based on the least mean-square-error criterion is presented. Then, an exact expression for the achievable minimum mean square error (MMSE) is derived with the aid of the Toeplitz form method and Szego theory. Based on this MMSE expression, the formulae for estimating the optimal filter¡¦s in-band prediction error and out-of-band noise attenuation are derived. Finally, the optimal filter is applied to sigma-delta modulation. It shows that the modulation performance and stability are intimately related to the filter performance and can be accurately estimated by the derived formulae.

This thesis presents a new robust filtering technique that suppresses impulsive noise in speech signals. The method makes use of Projection Statistics based on medians to detect segments of speech with impulses. The autoregressive model employed to smooth out the speech signal is identified by means of a robust nonlinear estimator known as the Schweppe-type Huber GM-estimator. Simulation results are presented that demonstrate the effectiveness of the filter. Another contribution of the work is the development of a robust version of the Kalman filter based on the Huber M-estimator. The performances of this filter are evaluated for a simple autoregressive process. / Master of Science

No description available.

No description available.

The application of the linear prediction Model for speech waveform analysis to context-independent automatic speaker recognition is explored, primarily in terns of the parametric sensitivity of the model. Feature vectors to characterize speakers are formed from linear prediction speech parameters computed as inverse filter coefficients, reflection coefficients or cepstral coefficients, and also power spectrum parameters via Fast Fourier Transform coefficients. The comparative performance of these parameters is investigated in speaker recognition experiments. The stability of the linear prediction parameters is tested over a range of model order from p=6 to p=30. Two independent speech databases are used to substantiate the experimental results. The quality of the automatic recognition technique is assessed in a novel experiment based on a direct performance comparison with the human skill of aural recognition. Correlation is sought between the performance of the aural and automatic recognition methods, for each of the four parameter sets. Although the recognition accuracy of the automatic system is superior to that of the direct aural technique, the error distributions are highly variable. The performance of the automatic system is shown to be empirically based and unlike the intuitive human process. An extended preamble to the description of the experiments reviews the current art of automatic speaker recognition, with a critical consideration of the performance of linear prediction techniques. As supported by our experimental results, it is concluded that success in the laboratory rests upon a rather fragile foundation. Application to problems beyond the controlled laboratory environment is seen, therefore, to be still more precarious.

Šiame darbe aš apibendrinau informaciją apie pasirinkimo tarp tiesinio prognozavimo mikro ir makro-modelių problemą. Agregavimas suprantamas kaip sektorinis agregavomas, o modeliai yra iš vienmatės tiesinės regresijos klasės. Aš išvedžiau kriterijų pasirinkimui tarp makro ir mikro-modelių ir idealaus agregavimo testą tiesinio agregavimo su fiksuotais ir atsitiktiniais svoriais atvejais. Paskutiniu atveju idealų agregavimą rekomenduoju tikrinti permutaciniu testu. Rezultatai iliustruoju ekonominiu pavyzdžiu. Modeliuoju Lietuvos vidutinį darbo užmokestį agreguotu modeliu ir atskirose ekonominės veiklos sektoriuose. Analizės rezultatas parodo, kad modeliai yra ekvivalentūs. / This paper focuses on the choice between macro and micro models. I suggest a hypothesis testing procedure for in-sample model selection for such variables as average wage. Empirical results show that Lithuanian average wage should be predict by using aggregate model.

Os codificadores preditivos de voz por analise-mediante-síntese vem sendo amplamente aplicados em telefonia móvel celular e em telecomunicações sigilosas. A predição linear do sinal de voz e as técnicas de análise-mediante-síntese são apresentadas de forma a relacionar algumas características perceptivas da audição humana as técnicas e parâmetros usados no processamento de sinais. Esta classe de codificadores e descrita no contexto do codificador preditivo excitado por códigos. Estruturas especiais do codificador tais como livros de códigos adaptativos, esparsos e definidos por base vetorial são abordadas bem como melhoramentos de processamento tais quais as buscas com ortogonalidade. Propõe-se um novo codificador, o codificador preditivo linear com excitação decomposta em vetores singulares, que complementa uma representação recentemente anunciada da excitação da voz com buscas em livros de códigos adaptativos. Os resultados de um estudo de codificadores principais desta classe são apresentados. A analise comparativa baseia-se em medidas objetivas temporais e espectrais. Um estudo suplementar de seleção espectral das características da excitação e de quantização do conjunto completo de parâmetros do codificador proposto revelou resultados interessantes sobre a representação espectral adaptativa e sobre a sensibilidade a quantização das características da excitação. / Analysis-by-synthesis linear predictive speech coders are widely applied in mobile and secure telecommunications. Linear prediction of speech signals and analysis-by-synthesis techniques are presented so that some perceptual features of human hearing may be related to signal processing techniques and parameters. The basic operation of this class of coders is described in the framework of the code-excited predictive coder. Special coder structures such as adaptive, sparse and vector-basis codebooks are introduced as well as processing enhancements such as orthogonal searches. A recently introduced representation of voice excitation is complemented by adaptive codebook searches to give rise to the new proposed coder, the singular-vector-decomposed excitation linear predictive coder. The sults of a study of some important coders in this class is present. The coders are compared on the basis of waveform and spectral objective distortion measures. A further study of spectral selection of excitation features, and quantization of the whole set of parameters is performed on the proposed coder. Some interesting results are described concerning the adaptive spectral representation and the sensitivity to quantization of the excitation features.