Topic
Cepstrum
About: Cepstrum is a research topic. Over the lifetime, 3346 publications have been published within this topic receiving 55742 citations.
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Papers
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04 Jul 2001TL;DR: New structures are proposed for an effective realization of cepstral vocal tract models that model both formants and antiformants.
Abstract: Speech is an analog sound signal produced by exciting the human vocal tract. The magnitude response of the vocal tract exhibits both peaks (formants) and valleys (antiformants). Vocal tract models are differentiated according to whether they model the formants alone (LPC models) or also antiformants (ARMA and cepstral models). New structures are proposed for an effective realization of cepstral vocal tract models that model both formants and antiformants.
17 citations
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TL;DR: Several distance measures (Euclidean distances between complex autoregressive coefficients or complex partial correlation coefficients, log-likelihood distance, and complex power cepstrum distance) between planar shapes are presented on the basis of a complex Autoregressive model and are suitable for classification, identification, or clustering of planar shaped applications, like unsupervised applications.
17 citations
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TL;DR: In this paper, the authors proposed the Minimum Variance Cepstrum (MVC) estimator to estimate the time difference of arrival (TDOA) of sound waves between microphones.
17 citations
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01 Oct 2018TL;DR: Using LSTM for recognize Indonesian speech digit, the MFCC feature extraction gets better accuracy result of 96.58% compared to the LPC feature extraction which amounts to 93.79 %.
Abstract: This paper presents Indonesian speech digit of decimal number (0–9) recognition using Deep Learning Long-Short Term Memory (LSTM). The LPC (Linear Predictive Coding) and MFCC (Mel-Frequency Cepstrum) feature extraction was used as an input on the LSTM model and the level of recognition accuracy was compared. The LPC feature extract speech feature based on a pitch or fundamental frequency, while MFCC extract speech feature based on the sound spectrum. We used 7990 speech digits consisted of 12 LPC coefficients and 12 MFCC coefficients as training data, while 790 data was used to classify on LSTM that had been trained. The results show that using LSTM for recognize Indonesian speech digit, the MFCC feature extraction gets better accuracy result of 96.58% compared to the LPC feature extraction which amounts to 93.79 %.
17 citations
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TL;DR: A new framework to identify and assess progressive structural damage is developed and a new damage feature outperforms the conventional principle component analysis–based feature, and the comprehensive test framework including extensive progressive damage cases validates the proposed technique.
Abstract: This article aims at developing a new framework to identify and assess progressive structural damage. The method relies solely on output measurements to establish the frequency response functions o...
17 citations