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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TL;DR: This paper proposes a simple yet effective technique for fingerprint identification that is image-based in which feature vectors of a fingerprint are extracted after sectorization of the cepstrum of a fingerprints and matched with those stored in the database.
Abstract: Using biometrics to verify a person’s identity has several advantages over the present practices of personal identification numbers (PINs) and passwords. Minutiae-based automated fingerprint identification systems are more popular, but they are more computationally complex and time consuming. In this paper we propose a simple yet effective technique for fingerprint identification. This method is image-based in which feature vectors of a fingerprint are extracted after sectorization of the cepstrum of a fingerprint. They are matched with those stored in the database. The experimental results show that this algorithm could correctly identify fingerprints with accuracy more than 96% in case of larger number of sectors.
11 citations
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13 Nov 2014
TL;DR: Experimental results depict that the RMFCC and low-variance spectrum-estimators-based robust feature extractors outperformed the MFCC, PNCC (power normalized cepstral coefficients), and ETSI-AFE features both in clean and multi-condition training conditions.
Abstract: This paper presents robust feature extractors for a continuous speech recognition task in matched and mismatched environments. The mismatched conditions may occur due to additive noise, different channel, and acoustic reverberation. In the conventional Mel-frequency cepstral coefficient (MFCC) feature extraction framework, a subband spectrum enhancement technique is incorporated to improve its robustness. We denote this front-end as robust MFCCs (RMFCC). Based on the gammatone and compressive gammachirp filter-banks, robust gammatone filterbank cepstral coefficients (RGFCC) and robust compressive gammachirp filterbank cepstral coefficients (RCGCC) are also presented for comparison. We also employ low-variance spectrum estimators such as multitaper, regularized minimum-variance distortionless response (RMVDR), instead of a discrete Fourier transform-based direct spectrum estimator for improving robustness against mismatched environments. Speech recognition performances of the robust feature extractors are evaluated in clean as well as multi-style training conditions of the AURORA-4 continuous speech recognition task. Experimental results depict that the RMFCC and low-variance spectrum-estimators-based robust feature extractors outperformed the MFCC, PNCC (power normalized cepstral coefficients), and ETSI-AFE features both in clean and multi-condition training conditions.
11 citations
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01 Sep 1996
TL;DR: It is shown here that it is possible to obtain noise resistant cepstral coefficients, for speaker independent connected word recognition, and that these results can be obtained with smaller databases.
Abstract: The goal is to improve recognition rate by optimisation of Mel Frequency Cepstral Coefficients (MFCCs): modifications concern the time-frequency representation used to estimate these coefficients. There are many ways to obtain a spectrum out of a signal which differ in the method itself (Fourier, Wavelets,…), and in the normalisation. We show here that we can obtain noise resistant cepstral coefficients, for speaker independent connected word recognition. The recognition system is based on a continuous whole word hidden Markov model. An error reduction rate of approximately 50% is achieved. Moreover evaluation tests demonstrate that these results can be obtained with smaller databases: halving the training database have small effects on recognition rates (which is not the case with traditional MFCCs).
11 citations
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09 May 1995TL;DR: The non-supervised self organizing map of Kohonen, the supervised learning vector quantization algorithm (LVQ3), and a method based on second-order statistical measures (SOSM) were adapted, evaluated and compared for speaker verification on 57 speakers of a POLYPHONE-like data base.
Abstract: The non-supervised self organizing map of Kohonen (SOM), the supervised learning vector quantization algorithm (LVQ3), and a method based on second-order statistical measures (SOSM) were adapted, evaluated and compared for speaker verification on 57 speakers of a POLYPHONE-like data base. The SOM and LVQ3 were trained by codebooks with 32 and 256 codes and two statistical measures; one without weighting (SOSM1) and another with weighting (SOSM2) were implemented. As the decision criterion, the equal error rate (EER) and best match decision rule (BMDR) were employed and evaluated. The weighted linear predictive cepstrum coefficients (LPCC) and the /spl Delta/LPCC were used jointly as two kinds of spectral speech representations in a single vector as distinctive features. The LVQ3 demonstrates a performance advantage over SOM. This is due to the fact that the LVQ3 allows the long-term fine-tuning of an interested target codebook using speech data from a client and other speakers, whereas the SOM only uses data from the client. The SOSM performs better than the SOM and the LVQ3 for long test utterances, while for short test utterances the LVQ is the best method among the methods studied.
11 citations
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16 Apr 1996
TL;DR: In this paper, a two-dimensional cepstrum-based matching technique accessing rotations and translations is proposed, where the logarithmic polar mapping of the power spectra of both images to be registered is used for the decoupling of rotation and translations (similar to the Fourier-Mellin transform).
Abstract: Spatial registration is a major problem arising whenever several images of similar contents are to be compared. Considering translations only, two-dimensional cepstral techniques have been proven to be exact and robust against noise or intensity variations. Furthermore, the cepstral filtering is numerically more efficient than most common approaches to image registration based on cross-correlation or template matching. In a previous paper, we proposed a two- dimensional cepstrum based matching technique accessing rotations and translations. The logarithmic polar mapping of the power spectra of both images to be registered is used for the decoupling of rotations and translations (similar to the Fourier-Mellin transform). Rotations are detected first matching the mapped spectra by two-dimensional cepstrum analysis. After rotating back one image, the relative shift is determined using the same cepstrum technique. In clinical practice, the rotation detection step was discovered as the weakness of this registration technique. Based on 855 pairs of dental radiographs acquired in known positions, three different approaches of matching the mapped spectra are compared: the cepstrum technique, the cross-correlation, and the entropy of the one-dimensional histogram distribution function of the substraction image of the mapped spectra. The combination of the log-polar mapped power spectra of both x rays with the entropy-measure allows the best detection of rotations. The union with common cepstrum methods correcting translations results in a robust rotation- extended cepstrum technique.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
11 citations