Cepstrum

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

Comparing MFCC and MPEG-7 audio features for feature extraction, maximum likelihood HMM and entropic prior HMM for sports audio classification

Abstract: We present a comparison of 6 methods for classification of sports audio. For feature extraction, we have two choices: MPEG-7 audio features and Mel-scale frequency cepstrum coefficients (MFCC). For classification, we also have two choices: maximum likelihood hidden Markov models (ML-HMM) and entropic prior HMMs (EP-HMM). EP-HMMs, in turn, have two variations: with and without trimming of the model parameters. We thus have 6 possible methods, each of which corresponds to a combination. Our results show that all the combinations achieve classification accuracy of around 90% with the best and the second best being, respectively, MPEG-7 features with EP-HMM and MFCC with ML-HMM.

Improvement of Force-Sensor-Based Heart Rate Estimation Using Multichannel Data Fusion

Abstract: The aim of this paper is to present and evaluate algorithms for heartbeat interval estimation from multiple spatially distributed force sensors integrated into a bed. Moreover, the benefit of using multichannel systems as opposed to a single sensor is investigated. While it might seem intuitive that multiple channels are superior to a single channel, the main challenge lies in finding suitable methods to actually leverage this potential. To this end, two algorithms for heart rate estimation from multichannel vibration signals are presented and compared against a single-channel sensing solution. The first method operates by analyzing the cepstrum computed from the average spectra of the individual channels, while the second method applies Bayesian fusion to three interval estimators, such as the autocorrelation, which are applied to each channel. This evaluation is based on 28 night-long sleep lab recordings during which an eight-channel polyvinylidene fluoride-based sensor array was used to acquire cardiac vibration signals. The recruited patients suffered from different sleep disorders of varying severity. From the sensor array data, a virtual single-channel signal was also derived for comparison by averaging the channels. The single-channel results achieved a beat-to-beat interval error of 2.2% with a coverage (i.e., percentage of the recording which could be analyzed) of 68.7%. In comparison, the best multichannel results attained a mean error and coverage of 1.0% and 81.0%, respectively. These results present statistically significant improvements of both metrics over the single-channel results ( $p ).

Smoothed nonparametric spectral estimation via cepsturm thresholding - Introduction of a method for smoothed nonparametric spectral estimation

Abstract: Cepstrum thresholding is shown to be an effective, automatic way of obtaining a smoothed nonparametric estimate of the spectrum of a stationary signal. In the process of introducing the cepstrum thresholding-based spectral estimator, we discuss a number of results on the cepstrum of a stationary signal, which might also be of interest to researchers in spectral analysis and allied topics, such as speech processing

Repetitive-PN1023-sequence echo-cancellation reference signal for single-carrier digital television broadcast systems

Abstract: DTV signals transmitted over the air with a symbol rate of around 10.76 million samples per second include echo-cancellation reference (ECR) signals each of which includes or essentially consists of a repetitive-PN1023 sequence with baud-rate symbols, which repetitive-PN1023 sequence incorporates a number of consecutive data-segment synchronization signals. Receivers for these DTV signals respond to these ECR signals to generate initial weighting coefficients for adaptive filters used for channel equalization and echo suppression. The initial weighting coefficients are calculated from a cepstrum extracted from the repetitive-PN1023 sequence ECR signal by DFT methods or with a PN1023 auto-correlation match filter.

The Old and the New in Seismic Deconvolution and Wavelet ESTIMATION

Abstract: Various seismic deconvolution operators can be determined by estimating a seismic wavelet and subsequently designing an appropriate inverse filter which converts the wavelet to a spike. Seismic wavelets and deconvolution operators must be estimated in a time adaptive sense due to the nonstationarity of the seismic trace. The wavelet estimation methods considered either use the assumption of a minimum phase wavelet and a random impulse response, or the assumption that the wavelet cepstrum is readily separable from the cepstrum of the seismic trace. The former assumption is required in using the Hilbert transform and Wiener-Levinson wavelet estimations, while the latter assumption is used in homomorphic deconvolution. These wavelet estimates can be used in the design of multichannel Wiener and Kalman deconvolution operators. Multichannel usage of homomorphic deconvolution can also be implemented through various types of cepstral stacking. The discussion of deconvolution filter design focuses on the problems of filter length degree of prewhitening and nonstationarity. In designing time adaptive deconvolution filters, the autocorrelation function can be used to monitor the nonstationarity of the seismic trace. The autocorrelation function, which is used in the computation of least squares inverse filters, can be estimated in an optimum fashion by using the maximummore » entropy method. Differences between minimum phase Wiener deconvolution and maximum entropy deconvolution become more pronounced for shorter data gates. As a result the maximum entropy approach is preferred for time adaptive deconvolution. The performance of various wavelet estimators and inverse filters is discussed using real and synthetic seismic data. Discussions of homomorphic deconvolution and maximum entropy prediction error filtering are merged with descriptions of conventional approaches to deconvolution.« less