Noise measurement

About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.

Noise suppression of speech by signal processing including applying a transform to time domain input sequences of digital signals representing audio information

Abstract: A communications device, such as a cellular telephone handset (10), and a method of operating the same to suppress noise in audio information such as speech, is presented. The handset (10) includes a digital signal processor (DSP) (30) having program memory (31) for controlling the DSP (30) to apply a hierarchical lapped transform to the input digital sequence. The hierarchical lapped transform decomposes the input sequence into coefficients representative of plurality of sub-bands corresponding to critical bands of the human ear. Each coefficient is modified by a noise suppression filter operator, based upon a ratio of an estimate of the noise power to an estimate of the signal power in the corresponding sub-band; clamping of changes in the noise power estimate over time, and use of a decaying signal envelope estimate, eliminate distortion in the processed signal. Musical noise is eliminated by using a minimum gain value in each sub-band. Inverse transformation of the modified coefficients provides the filtered time-domain output signal. Improved noise suppression is provided, in a manner that may be readily and robustly performed by fixed-point digital signal processors.

Noise adaptive training using a vector taylor series approach for noise robust automatic speech recognition

Abstract: In traditional methods for noise robust automatic speech recognition, the acoustic models are typically trained using clean speech or using multi-condition data that is processed by the same feature enhancement algorithm expected to be used in decoding. In this paper, we propose a noise adaptive training (NAT) algorithm that can be applied to all training data that normalizes the environmental distortion as part of the model training. In contrast to the feature enhancement methods, NAT estimates the underlying “pseudo-clean” model parameters directly without relying on point estimates of the clean speech features as an intermediate step. The pseudo-clean model parameters learned with NAT are later used with vector Taylor series (VTS) model adaptation for decoding noisy utterances at test time. Experiments performed on the Aurora 2 and Aurora 3 tasks, demonstrate that the proposed NAT method obtain relative improvements of 18.83% and 32.02%, respectively, over VTS model adaptation.

A New Process Uncertainty Robust Student’s t Based Kalman Filter for SINS/GPS Integration

Abstract: Motivated by the problem that the Gaussian assumption of process noise may be violated and the statistics of process noise may be inaccurate when the carrier maneuvers severely, a new process uncertainty robust Student’s t-based Kalman filter is proposed to integrate the strap-down inertial navigation system (SINS) and global positioning system (GPS). To better address the heavy-tailed process noise induced by severe maneuvering, the one-step predicted probability density function is modeled as a Student’s t distribution, and the conjugate prior distributions of inaccurate mean vector, scale matrix, and degrees of freedom (dofs) parameter are, respectively, selected as Gaussian, inverse Wishart, and Gamma distributions, based on which a new Student’s t-based hierarchical Gaussian state-space model for SINS/GPS integration is constructed. The state vector, auxiliary random variable, mean vector, scale matrix, and dof parameter are jointly estimated based on the constructed hierarchical Gaussian state-space model using the variational Bayesian approach. Experimental results illustrate that the proposed method has significantly better robustness for the suppression of the process uncertainty but slightly higher computational complexity than the existing state-of-the-art methods.

Phase autocorrelation (PAC) derived robust speech features

Abstract: We introduce a new class of noise robust acoustic features derived from a new measure of autocorrelation, and explicitly exploiting the phase variation of the speech signal frame over time. This family of features, referred to as "phase autocorrelation" (PAC) features, include PAC spectrum and PAC MFCC (Mel-frequency cepstral coefficient), among others. In regular autocorrelation based features, the correlation between two signal segments (signal vectors), separated by a particular time interval k, is calculated as a dot product of these two vectors. In our proposed PAC approach, the angle between the two vectors is used as a measure of correlation. Since dot product is usually more affected by noise than the angle, PAC-features are expected to be more robust to noise. This is indeed significantly confirmed by the presented experimental results. The experiments were conducted on the Numbers 95 database, on which "stationary" (car) and "non -stationary" (factory) Noisex 92 noises were added with varying SNR. In most of the cases, without any specific tuning, PAC-MFCC features perform better.

On the generalized DFIIt structure and its state-space realization in digital filter implementation

Abstract: It is well known that for a digital filter of order p, the number of nontrivial parameters in the classical optimal state-space realizations is proportional to p/sup 2/, while the traditional shift operator z-based direct-form II transposed (zDFIIt) structure, though having poor numerical properties, is one of the most efficient structures, just possessing 3p+1 nontrivial parameters. In this paper, based on the concept of polynomial operators, a new structure is proposed for digital filter implementation, which is a generalization of the traditional zDFIIt and the prevailing /spl delta/DFIIt structures. This structure, denoted as /spl rho/DFIIt, possesses 3p+1 nontrivial parameters plus p parameters at choice. Expressions for evaluating the sensitivity measure and the roundoff noise gain are derived for the /spl rho/DFIIt structure and its equivalent state-space realization that has the same structure complexity. It is shown that the state-space realization always yields a smaller roundoff noise gain than the /spl rho/DFIIt structure. One of the nice properties of these two structures is that for a given digital filter, they can be optimized with the p free parameters. The optimal structure problems can be solved with exhaustive researching under practical considerations. Numerical examples are presented to illustrate the design procedure.