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

The White Horse Patrol

Men will always love beauty and a beautiful horse will always arouse the fancy and admiration of horsemen and laymen alike. To them nothing is more awe-inspiring than matched and intelligent horses. Horses skilled in acting are usually found only in the circus, but occasionally an individual will delight in owning and training a trick horse. When a number of such men are brought together by this mutual interest, the arrangement that results may well lead to something unique in that it deals with living creatures by nature of different conformation, temperament, and capability

Blind Normalization of Speech From Different Channels

We show how to construct a channel-independent representation of speech that has propagated through a noisy reverberant channel. This is done by blindly rescaling the cepstral time series by a non-linear function, with the form of this scale function being determined by previously encountered cepstra from that channel. The rescaled form of the time series is an invariant property of it in the following sense: it is unaffected if the time series is transformed by any time-independent invertible distortion. Because a linear channel with stationary noise and impulse response transforms cepstra in this way, the new technique can be used to remove the channel dependence of a cepstral time series. In experiments, the method achieved greater channel-independence than cepstral mean normalization, and it was comparable to the combination of cepstral mean normalization and spectral subtraction, despite the fact that no measurements of channel noise or reverberations were required (unlike spectral subtraction).Comment: 25 pages, 7 figure