Application of the saber method for improved spectral analysis of noisy speech

technical reportA stand alone noise suppression algorithm is described for reducing the spectral effects of acoustically added noise in speech. A fundamental result is developed which shows that the spectral magnitude of speech plus noise can be effectively approximated as the sum of magnitudes of speech and noise. Using this simple phase independent additive model, the noise bias present in the short time spectrum is reduced by subtracting off the expected noise spectrum calculated during nonspeech activity. After bias removal, the time waveform is recalculated from the modified magnitude and saved phase. This Spectral Averaging for Bias Estimation and Removal, or SABER method requires only one FFT per time window for analysis and synthesis

Selected methods for improving synthesis speech quality using linear predictive coding: system description, coefficient smoothing and streak

technical reportThis report develops two generalizations of the standard Linear Predictive Coding (LPC) implementation of a narrow band speech compression system. The purpose of each method is to improve the speech quality that is available from a standard LPC system

Suppression of acoustic noise in speech using spectral subtraction

technical reportA stand alone noise suppression algorithm is presented for reducing the spectral effects of acoustically added noise in speech. Effective performance of digital speech processors operating in practical environments may require suppression of noise from the digital waveform. Spectral subtraction offers a computationally efficient, processor independent, approach to effective digital speech analysis. The method, requiring about the same computation as high-speed convolution, suppresses stationary noise for speech by subtracting the spectral noise bias calculated during non-speech activity. Secondary procedures and then applied to attenuate the residual noise left after subtraction. Since the algorithm resynthesizes a speech waveform, it can be used as a preprocessor to narrow band voice communications systems, speech recognition systems or speaker authentication systems

Improving linear prediction analysis of noisy speech by predictive noise cancellation

technical reportThe analysis of speech using Linear Prediction is reformulated to account for the presence of acoustically added noise and a technique is presented for reducing its effect on parameter estimation. The method, called Predictive Noise Cancellation (PNC), modifies the noisy speech autocorrelations using an estimate of present background noise which is adaptively updated from an average all-pole noise spectrum. The all-pole noise spectrum is calculated by averaging autocorrelations during non-speech activity. The method uses procedures which are already available to the LPC analyzer, and thus is well suited for real time analysis of noisy speech. Preliminary results show signal to noise improvements on the order of 10 to 20 db

Suppression of acoustic noise in speech using two microphone adaptive noise cancellation

technical reportAcoustic noise with energy greater or equal to the speech is suppressed by filtering a separately recorded correlated noise signal and subtracting it from the speech waveform. This approach was investigated to determine the degree of noise suppression possible using an external correlated input. The second reference noise signal is adaptively filtered using the least mean squares, LMS and the lattice gradient algorithms. These two approaches are developed and compared in terms of degree of noise power reduction, algorithm convergence time, and degree of speech enhancement. Both methods were shown to reduce ambient noise power by at least 20dB with minimal speech distortion and thus to be potentially powerful as noise suppression preprocessors for voice communication in severe noise environments

Noise suppression methods for robust speech processing (1 Oct. 1976 - 31 March 1977)

technical reportTo develop robust speech processes, based upon the integration of digital noise suppression methods and narrow band speech analysis-synthesis methods, capable of realizing practical, real time methods for effectively processing speech recorded in practical operating environments

Noise Suppression Methods for Robust Speech Processing

technical reportRobust speech processing in practical operating environments requires effective environmental and processor noise suppression. This report describes the technical findings and accomplishments to develop real time, compressed speech analysis-synthesis algorithms whose performance is invariant under signal contamination. Fulfillment of this requirement is necessary to insure reliable secure compressed speech transmission within realistic military command and control environments. Overall contributions resulting from this research program include the understanding of how environmental noise degrades narrow band, coded speech, development of appropriate real time noise suppression algorithms, and development of speech parameter identification methods that consider signal contamination as a fundamental element in the estimation process. This report describes results in the areas of noise suppression using the spectral subtraction algorithm, dual input adaptive noise cancellation using the LMS algorithm, pole-zero parameter estimation, nonparametric-rank order statistics with applications to Robust Speech Activity detection, spectral analysis and synthesis using the constant-Q transform, and pitch and rate changes to speech using the constant-Q transform. Sponsored in part by DARPA

Noise suppression methods for robust speech processing (1 Oct. 1979- 31 Mar. 1980)

technical reportRobust speech processing in practical operating environments requires effective environmental and processor noise suppression. This report describes the technical findings and accomplishments during this reporting period for the research program funded to develop real time, compressed speech analysis-synthesis algorithms whose performance is invariant under signal contamination. Fulfillment of this requirement is necessary to insure reliable secure compressed speech transmission within realistic military command and control environments. Overall contributions resulting from this research program include the understanding of how environmental noise degrades narrow band, coded speech, development of appropriate real time noise suppression algorithms, and development of speech parameter identification methods that consider signal contamination as a fundamental element in the estimation process. This report describes the current research and results in the areas of noise suppression using the dual input adaptive noise cancellation using the Short-time Fourier Transform algorithms, articulation rate change techniques, and a description of an experiment: which demonstrated that the spectral subtraction noise suppression algorithm can improve the intelligibility of 400 bps, LPC-10 coded, helicopter speech by 10.6 points

Noise suppression methods for robust speech processing (1 Oct. 1978- 31 Mar. 1979)

technical reportRobust speech processing in practical operating environments requires effective environmental and processor noise suppression. This report describes the technical findings and accomplishments during this reporting period for the research program funded to develop real time, compressed speech analysis-synthesis algorithms whose performance is invariant under signal contamination. Fulfillment of this requirement is necessary to insure reliable secure compressed speech transmission within realistic military command and control environments. Overall contributions resulting from this research program include the understanding of how environmental from this research program include the understanding of how environmental noise degrades narrow band, coded speech, development of appropriate real time noise suppression algorithms, and development of speech parameter identification methods that consider signal contamination as a fundamental element in the estimation process. This report describes the current research and results in the areas of noise suppression using the spectral subtraction algorithm, dual input adaptive noise cancellation using the LMS algorithm, pole-zero parameter estimation, nonparametric-rank order statistics with applications to Robust Speech activity detection, spectral analysis and synthesis using the constant-Q transform, and pitch and rate changes to speech using the constant-Q transform

Noise suppression methods for robust speech processing (1 April 1979- 30 Sept. 1979)

technical reportRobust speech processing in practical operating environments requires effective environmental and processor noise suppression. This report describes the technical findings and accomplishments during this reporting period for the research program funded to develop real-time, compressed speech analysis-synthesis algorithms whose performance is invariant under signal contamination. Fulfillment of this requirement is necessary to insure reliable secure compressed speech transmission within realistic military command and control environments. Overall contributions resulting from this research program include the understanding of how environmental noise degrades narrow band, coded speech, development of appropriate real time noise suppression algorithms, and development of speech parameter identification methods that consider signal contamination as a fundamental element in the estimation process. This report describes the current research and results in the areas of noise suppression using the dual input adaptive noise cancellation using the LMS and gradient lattice algorithms, to spectral analysis and synthesis using the constant -Q trans form, articulation rate change techniques, analytic continuation of band limited signals or signal extrapolation, and two-dimensional automatic gain control