Cepstrum

About: Cepstrum is a research topic. Over the lifetime, 3346 publications have been published within this topic receiving 55742 citations.

Spoken-word recognition using dynamic features analysed by two-dimensional cepstrum

Abstract: In the paper, two-dimensional cepstrum (TDC) analysis and its application to word and monosyllable recognition are described. The TDC can simultaneously represent several different kinds of information contained in the speech waveform: static and dynamic features, as well as global and fine frequency structure. Noise reduction and speech enhancement can be easily performed using the TDC. Using word and monosyllable recognition experiments based on dynamic programming (DP) matching of a time sequence of the TDC, it is confirmed that the global static features (spectral envelope) and global dynamic features are both effective for speech recognition. A speaker-independent (noisy) word recognition algorithm is also proposed which recognises the words based on the similarity of dynamic features. The algorithm employs linear matching instead of DP nonlinear matching, requires a small amount of memory, and shows high speed and high accuracy in recognition. At present, the recognition rate is 89.0% at ∞ dB and 70.0% at 0 dB signal-to-noise ratio.

Rocket engine gear defect monitoring method

Abstract: Vibration and tachometer measurements are used to assess the health of rotating equipment to compute and store two sided cepstrum parameters used to compare the engine performance to a class of engines for determining out-of-family performance indicating the healthy or defective nature of the engine under test. The cepstrum parameter can be viewed after static test firing of a rocket engine and analyzed for changes in the cepstrum parameter further indicating defect growth during static test firing. Engine-to-engine comparisons of vibration-related parameters can be used to provide information on abnormal gear behavior. The cepstrum is defined as the inverse discrete Fourier transform of the logarithm of two-sided autospectral density. The test method is an effective screen for determining defective rocket engine components during preflight static testing.

Noise estimation in voice signals using short-term cepstral analysis

Abstract: Cepstral-based estimation is used to provide a baseline estimate of the noise level in the logarithmic spectrum for voiced speech. A theoretical description of cepstral processing of voiced speech containing aspiration noise, together with supporting empirical data, is provided in order to illustrate the nature of the noise baseline estimation process. Taking the Fourier transform of the liftered (filtered in the cepstral domain) cepstrum produces a noise baseline estimate. It is shown that Fourier transforming the low-pass liftered cepstrum is comparable to applying a moving average (MA) filter to the logarithmic spectrum and hence the baseline receives contributions from the glottal source excited vocal tract and the noise excited vocal tract. Because the estimation process resembles the action of a MA filter, the resulting noise baseline is determined by the harmonic resolution (as determined by the temporal analysis window length) and the glottal source spectral tilt. On selecting an appropriate temporal analysis window length the estimated baseline is shown to lie halfway between the glottal excited vocal tract and the noise excited vocal tract. This information is employed in a new harmonics-to-noise (HNR) estimation technique, which is shown to provide accurate HNR estimates when tested on synthetically generated voice signals.

Data-driven filtering of cepstral time trajectories for robust speech recognition

Abstract: A method and apparatus for speech processing in a distributed speech recognition system having a front-end and a back-end. The speech processing steps in the front-end are as follows: extracting speech features from a speech signal and normalizing the speech features in order to alter the power of the noise component in the modulation spectrum in relation to the power of the signal component, especially with frequencies above 10 Hz. A low-pass filter is then used to filter the normalized modulation spectrum in order to improve the signal-to-noise ratio (SNR) in the speech signal. The combination of feature vector normalization and low-pass filtering is effective in noise removal, especially in a low SNR environment.