In this paper we present a method based on a time-varying sinusoidal model for a robust and accurate estimation of amplitude and frequency modulations (AM-FM) in speech. The suggested approach has two main steps. Firs...
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ISBN:
(纸本)9781615676927
In this paper we present a method based on a time-varying sinusoidal model for a robust and accurate estimation of amplitude and frequency modulations (AM-FM) in speech. The suggested approach has two main steps. First, speech is modeled as a sinusoidal model with time-varying amplitudes. Specifically, the model makes use of a first order time polynomial with complex coefficients for capturing instantaneous amplitude and frequency (phase) components. Next, the model parameters are updated by using the previously estimated instantaneous phase information. Thus, an iterative scheme for AM-FM decomposition of speech is suggested which was validated on synthetic AM-FM signals and tested on reconstruction of voiced speech signals where the signal-to-error reconstruction ratio (SERR) was used as measure. Compared to the standard sinusoidal representation, the suggested approach found to improve the corresponding SERR by 47%, resulting in over 30 dB of SERR.
The speech signal is usually considered as stationary during short analysis time intervals. Though this assumption may be sufficient in some applications, it is not valid for high-resolution speech analysis and in app...
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ISBN:
(纸本)9781424423538
The speech signal is usually considered as stationary during short analysis time intervals. Though this assumption may be sufficient in some applications, it is not valid for high-resolution speech analysis and in applications such as speech transformation and objective voice function assessment for detection of voice disorders. In speech, there are non stationary components, for instance time-varying amplitudes and frequencies, which may change quickly over short time intervals. In this paper, a previously suggested time-varying quasi-harmonic model is extended in order or to estimate the chirp rate for each sinusoidal component, thus successfully tracking fast variations in frequency and amplitude. The parameters of the model are estimated through linear Least Squares and the model accuracy is evaluated on synthetic chirp signals. Experiments on speech signals indicate that the new model is able to efficiently estimate the signal component chirp rates, providing means to develop more accurate speech models for high-quality speech transformations.
Background: It has been apparent in the last few years that small non coding RNAs (ncRNA) play a very significant role in biological regulation. Among these microRNAs (miRNAs), 22-23 nucleotide small regulatory RNAs, ...
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Background: It has been apparent in the last few years that small non coding RNAs (ncRNA) play a very significant role in biological regulation. Among these microRNAs (miRNAs), 22-23 nucleotide small regulatory RNAs, have been a major object of study as these have been found to be involved in some basic biological processes. So far about 706 miRNAs have been identified in humans alone. However, it is expected that there may be many more miRNAs encoded in the human genome. In this report, a "context-sensitive" Hidden Markov Model (CSHMM) to represent miRNA structures has been proposed and tested extensively. We also demonstrate how this model can be used in conjunction with filters as an ab initio method for miRNA identification. Results: The probabilities of the CSHMM model were estimated using known human miRNA sequences. A classifier for miRNAs based on the likelihood score of this "trained" CSHMM was evaluated by: (a) cross-validation estimates using known human sequences, (b) predictions on a dataset of known miRNAs, and (c) prediction on a dataset of non coding RNAs. The CSHMM is compared with two recently developed methods, miPred and CID-miRNA. The results suggest that the CSHMM performs better than these methods. In addition, the CSHMM was used in a pipeline that includes filters that check for the presence of EST matches and the presence of Drosha cutting sites. This pipeline was used to scan and identify potential miRNAs from the human chromosome 19. It was also used to identify novel miRNAs from small RNA sequences of human normal leukocytes obtained by the Deep sequencing (Solexa) methodology. A total of 49 and 308 novel miRNAs were predicted from chromosome 19 and from the small RNA sequences respectively Conclusion: The results suggest that the CSHMM is likely to be a useful tool for miRNA discovery either for analysis of individual sequences or for genome scan. Our pipeline, consisting of a CSHMM and filters to reduce false positives shows promise as
This paper reports on the first steps taken in search of a solution that uses public video streams availab.e on the Internet to address the increasing need for monitoring transportation networks with the intent of ret...
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This paper identifies ways in which traditional approaches to argumentation can be modified to meet the needs of practical group decision support. A framework for outcome-driven decision rationale management is propos...
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The most common cause of traffic accidents is arguably the driver error due to lack of attention. And it is very unlikely this is going to change soon thanks to increasingly cell-phone usage, in-car entertainment syst...
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Personal Navigation Assistants (PNA) depend on updated digital maps for correctly positioning and route guiding users. As updating digital maps by the usual means is a highly expensive and time consuming task, GPS poi...
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State observer design procedure is proposed for nonlinear locally Lipschitz systems. Possible presence of disturbances is taken into account. The solution is based on logic-based control approach applicable to nonline...
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State observer design procedure is proposed for nonlinear locally Lipschitz systems. Possible presence of disturbances is taken into account. The solution is based on logic-based control approach applicable to nonline...
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State observer design procedure is proposed for nonlinear locally Lipschitz systems. Possible presence of disturbances is taken into account. The solution is based on logic-based control approach applicable to nonlinear systems with bounded solutions.
This paper introduces a new way to measure rhythmic similarity between two musical pieces using periodicity spectra. In order to detect similarity for pieces of different tempi, the linearity of the warping path betwe...
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ISBN:
(纸本)9781424414833
This paper introduces a new way to measure rhythmic similarity between two musical pieces using periodicity spectra. In order to detect similarity for pieces of different tempi, the linearity of the warping path between their spectra serves as a measure of their rhythmic similarity. Using a modified kNN classification approach on two datasets, the proposed measure provides comparable classification accuracy (82.1%) to the best of widely used measures (85.5%) for the first dataset;For the second dataset, which is characterized by a large variance of tempi, the proposed measure outperforms all reference measures, reaching an accuracy of 69.0%, while the best of the other measures reaches 53.8%. Moreover, the presented technique works fully automatically, and no information regarding tempo is needed.
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