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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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Journal ArticleDOI
TL;DR: A new algorithm for spike detection has been developed: this applies a cepstrum of bispectrum (CoB) estimated inverse filter to provide blind equalization to find a sequence of event times or delta sequence.
Abstract: Signals from extracellular electrodes in neural systems record voltages resulting from activity in many neurons. Detecting action potentials (spikes) in a small number of specific (target) neurons is difficult because many neurons, both near and more distant, contribute to the signal at the electrode. We consider some nearby neurons as target neurons (providing a signal) and all the other contributions to the signal as noise. A new algorithm for spike detection has been developed: this applies a cepstrum of bispectrum (CoB) estimated inverse filter to provide blind equalization. This technique is based on higher order statistics, and seeks to find a sequence of event times or delta sequence. We show that the CoB-based technique can achieve a 98% hit rate on an extracellular signal containing three spike trains at up to 0 dB SNR. Threshold setting for this technique is discussed, and we show the application of the technique to some real signals. We compare performance with four established techniques and report that the CoB-based algorithm performs best.

53 citations

Journal ArticleDOI
TL;DR: SDF-based feature extraction is compared with that of two commonly used feature extractors, namely Cepstrum and principal component analysis (PCA), for target detection and classification and shows consistently superior performance in terms of successful detection, false alarm, and misclassification rates.

53 citations

Journal ArticleDOI
TL;DR: Speech processing has vast applications in voice dialing, telephone communication, call routing, domestic appliances control, Speech to Text conversion, Text to Speech conversion, lip synchronization, automation systems etc.
Abstract: The automatic recognition of speech means enabling a natural and easy mode of communication between human and machine. Speech processing has vast applications in voice dialing, telephone communication, call routing, domestic appliances control, Speech to Text conversion, Text to Speech conversion, lip synchronization, automation systems etc. Here we have discussed some mostly used feature extraction techniques like Mel frequency Cepstral Co-efficient (MFCC), Linear Predictive Coding (LPC) Analysis, Dynamic Time Wrapping (DTW), Relative Spectra Processing (RASTA) and Zero Crossings with Peak Amplitudes (ZCPA).Some parameters like RASTA and MFCC considers the nature of speech while it extracts the features, while LPC predicts the future features based on previous features.

53 citations

Journal ArticleDOI
TL;DR: This paper describes the use of artificial neural networks for acoustic to articulatory parameter mapping, and shows that a single feed‐forward neural net is unable to perform this mapping sufficiently well when trained on a large data set.
Abstract: A long‐standing problem in the analysis and synthesis of speech by articulatory description is the estimation of the vocal tract shape parameters from natural input speech. Methods to relate spectral parameters to articulatory positions are feasible if a sufficiently large amount of data is available. This, however, results in a high computational load and large memory requirements. Further, one needs to accommodate ambiguities in this mapping due to the nonuniqueness problem (i.e., several vocal tract shapes can result in identical spectral envelopes). This paper describes the use of artificial neural networks for acoustic to articulatory parameter mapping. Experimental results show that a single feed‐forward neural net is unable to perform this mapping sufficiently well when trained on a large data set. An alternative procedure is proposed, based on an assembly of neural networks. Each network is designated to a specific region in the articulatory space, and performs a mapping from cepstral values into tract areas. The training of this assembly is executed in two stages: In the first stage, a codebook of suitably normalized articulatory parameters is used, and in the second stage, real speech data are used to further improve the mapping. During synthesis, neural networks are selected by dynamic programming using a criterion that ensures smoothly varying vocal tract shapes while maintaining a good spectral match. The method is able to accommodate nonuniqueness in acoustic‐to‐articulatory mapping and can be bootstrapped efficiently from natural speech. Results on the performance of this procedure compared to other mapping procedures, including codebook look‐up and a single multilayered network, are presented.

53 citations

Journal ArticleDOI
TL;DR: A new bearing fault classification method based on convolutional neural networks (CNNs) is presented, demonstrated to have strong ability of classification under the interference of factory noise and the Gaussian noise.
Abstract: Bearing fault diagnosis is an important technique in industrial production as bearings are one of the key components in rotating machines. In bearing fault diagnosis, complex environmental noises will lead to inaccurate results. To address the problem, bearing fault classification methods should be capable of noise resistance and be more robust. In previous studies, researchers mainly focus on noise-free condition, measured signal and signal with simulated noise, many effective approaches have been proposed. But in real-world working condition, strong and complex noises are often leads to inaccurate results. According to the situation, this work focuses on bearing fault classification under the influence of factory noise and the white Gaussian noise. In order to eliminate the noise interference and take the possible connection between signal frames into consideration, this paper presents a new bearing fault classification method based on convolutional neural networks (CNNs). By using the sensitivity to impulse of spectral kurtosis (SK), noises are repressed by the proposed filtering approach based on the SK. Mel-frequency cepstral coefficients (MFCC) and delta cepstrum are extracted as the feature by the reason of satisfactory performance in sound recognition. And in consideration of the connection between frames, a feature arrangement method is presented to transfer feature vectors to feature images, so the advantages of the CNNs in the fields of image processing can be exploited in the proposed method. The proposed method is demonstrated to have strong ability of classification under the interference of factory noise and the Gaussian noise by experiments.

53 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202386
2022206
202160
202096
2019135
2018130