linear prediction based speech (LPC) analysis is known to be sensitive to the presence of additive noise. In this paper, we present a noise-compensated method for LPC analysis which ensures good spectral matching betw...
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linear prediction based speech (LPC) analysis is known to be sensitive to the presence of additive noise. In this paper, we present a noise-compensated method for LPC analysis which ensures good spectral matching between the original speech spectrum and the autoregressive (AR) model spectrum. In this method, the noise periodogram is obtained first by applying a simplified noise power spectral density (PSD) estimator on the calculated noisy periodogram. Then, the effect of noise on the spectral parameters is decreased by gradually subtracting values of the resulting noise autocorrelation coefficients from the coefficients derived from the noisy speech. By taking the absolute value of the estimated reflection coefficients as the decision criterion, we show that this iterative procedure ensures a significant decrease of the degrading effect of noise while the estimated autocorrelation matrix is guaranteed to be positive definite. The method was tested on real speech signals and yielded superior performance when compared to conventional LPC analysis, even in severe noisy conditions.
The design effort required in a project, not only impacts the final cost, but also the project lead-time. This paper presents a case study carried out with the collaboration of Pratt & Whitney Canada, a global lea...
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ISBN:
(纸本)9781424415281;1424415284
The design effort required in a project, not only impacts the final cost, but also the project lead-time. This paper presents a case study carried out with the collaboration of Pratt & Whitney Canada, a global leader in the design and manufacture of aircraft engines. The study uses a parametric model for the purpose of design effort estimation of an integrated blade-rotor low-pressure compressor (IBR LPC) fan. The model estimation is compared with the actual project performance, and results demonstrate good estimation of the design effort. The impact of various factors used for design effort estimation is also discussed. Finally, the usefulness of the model is demonstrated.
The problem of blind source separation (BSS) for multiple-input multiple-output (MIMO) autoregressive (AR) mixtures is addressed in this paper. A new time-domain method for system identification and BSS is proposed ba...
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The problem of blind source separation (BSS) for multiple-input multiple-output (MIMO) autoregressive (AR) mixtures is addressed in this paper. A new time-domain method for system identification and BSS is proposed based on the Gaussian mixture model (GMM) for sources distribution. The algorithm is based on the generalized expectation-maximization (GEM) method for joint estimation of the AR model parameters and the GMM parameters of the sources. The method is tested via simulations of synthetic and real audio signals. The results show that the proposed algorithm outperforms the well-known multidimensional linear predictive coding (LPC), and it achieves higher signal-to-interference ratio (SIR) in the BSS problem.
It is well known that linear predictive coding (LPC) performs well when the prediction coefficients are estimated from noise-free speech, and the system tends to degrade and perform poorly on noisy speech. This paper ...
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It is well known that linear predictive coding (LPC) performs well when the prediction coefficients are estimated from noise-free speech, and the system tends to degrade and perform poorly on noisy speech. This paper describes a method to minimize the degradation on the prediction coefficients in the presence of noise when an LPC analysis is used. In this method, a more accurate estimation of noise power is computed by using a simplified noise power spectral density (PSD) estimator. After an inverse discrete Fourier transform (DFT), the extracted noise autocorrelation coefficients are gradually subtracted from the coefficients derived from noisy speech according to an iterative processing scheme. The proposed processing scheme takes the absolute value of the estimated reflection coefficients as the decision criterion. It is shown that performing this iterative procedure on every autocorrelation lag ensures a substantial decrease in the degrading effects of noise, while the estimated autocorrelation matrix is guaranteed to be positive-definite. Experimental results indicate that the variance of the estimated prediction coefficients can be decreased significantly using the proposed method.
In this paper we study the identification of AR parameters in a biomedical signal corrupted by additive white gaussian noise. The identification of AR parameter is treated as a signal estimation problem, whose aim is ...
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In this paper we study the identification of AR parameters in a biomedical signal corrupted by additive white gaussian noise. The identification of AR parameter is treated as a signal estimation problem, whose aim is to obtain an estimate of the clean signal, given the noisy observations, and after that to obtain the noise free AR parameters. The novelty of our approach is the simultaneous estimation of AR parameter and the model order of the AR process. This is done adopting a Bayesian framework and using a special form for the prior of AR parameters. To obtain the solution we use the Variational Bayesian (VB) Framework. Simulation results have shown that the proposed approach correctly identifies the model order of AR process while at the same time produces an estimate for the AR parameters.
This paper describes continuous speech recognition experiments for Romanian language, based on statistical modelling by using hidden Markov models. These experiments are made in order to select the most appropriate fe...
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This paper describes continuous speech recognition experiments for Romanian language, based on statistical modelling by using hidden Markov models. These experiments are made in order to select the most appropriate features extraction method. The compared methods are cepstral and LPC analysis, in standard and perceptual versions. In our tests the cepstral coefficients perform in the most situations better versus the linear prediction ones, and the perceptual coefficients better than the standard ones.
Speech compression is of paramount importance in modern day communications where bandwidth conservation is necessary to accommodate the ever increasing number of communication channels. Historically, LPC (linear predi...
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Speech compression is of paramount importance in modern day communications where bandwidth conservation is necessary to accommodate the ever increasing number of communication channels. Historically, LPC (linear predictive coding) is used for speech analysis for better results, and subband coding is used in audio compression methods, such as MPEG standards. In this paper, we combine a RELP (residual excited linear prediction) technique with subband coding to yield compression rates of upto 85% with acceptable distortion levels
This paper presents a speaker verification system using a combination of vector quantization (VQ) and hidden Markov model (HMM) to improve the HMM performance. A Malay spoken digit database which contains 100 speakers...
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This paper presents a speaker verification system using a combination of vector quantization (VQ) and hidden Markov model (HMM) to improve the HMM performance. A Malay spoken digit database which contains 100 speakers is used for the testing and validation modules. It is shown that, by using the proposed combination technique, a total success rate (TSR) of 99.97% is achieved and it is an improvement of 11.24% in performance compared to HMM. For speaker verification, true speaker rejection rate, impostor acceptance rate and equal error rate (EER) are also improved significantly compared to HMM.
We present a study into all-pole spectral envelope estimation for the case of harmonic signals. We address the problem of the selection of the model order and propose to make use of the fact that the spectral envelope...
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We present a study into all-pole spectral envelope estimation for the case of harmonic signals. We address the problem of the selection of the model order and propose to make use of the fact that the spectral envelope is sampled by means of the harmonic structure to derive a reasonable choice for an appropriate model order. The experimental investigation uses synthetic ARMA featured signals with varying fundamental frequency and differing model structure to evaluate the performance of the selected all-pole models. The experimental results confirm the relation between optimal model order and the fundamental frequency.
The adaptive multi-rate (AMR) is the mandatory speech codec for GSM system, ranging from 12.2 kbps down to 4.75 kbps. To satisfy the requirement of need for both audio and video simultaneously, a kind of hardware and ...
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The adaptive multi-rate (AMR) is the mandatory speech codec for GSM system, ranging from 12.2 kbps down to 4.75 kbps. To satisfy the requirement of need for both audio and video simultaneously, a kind of hardware and software of AMR speech encoder based on high-performance chips TMS320C64X DSP is proposed. Considering the limited resource of DSP chips, the optimization scheme is referred to later. The result shows that the CPU-load is reduced from 38 percent to 4 percent. And the time of processing each frame is also reduced greatly after optimizing. It is verified with all the test vectors provided by 3 GPP, and the stable operation on the real-time testing board was also confirmed. So, more resource is reserved for video coding on DSP chips. This is helpful for implementing both speech coder and video coder on one DSP chip.
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