In high dimensional feature space with finite samples, severe bias can be introduced in the nearest neighbor algorithm. In this paper, we propose a new classification method, which performs classification task based o...
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ISBN:
(纸本)0769525210
In high dimensional feature space with finite samples, severe bias can be introduced in the nearest neighbor algorithm. In this paper, we propose a new classification method, which performs classification task based on local probability center of each class. Moreover, this prototype-based method classifies the query sample by using two measures, one is the distance between query and local probability centers, the other is the posterior probability of query. Although both measures are effect, the experiments show the second one is the better. The investigation results prove that this method improves the classification performance of nearest neighbor algorithm substantially
Voice biometrics is an economic method of person authentication with the help of machines because of low cost and high power computers. In this paper, we investigate the problem of spectral resolution in female speech...
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Voice biometrics is an economic method of person authentication with the help of machines because of low cost and high power computers. In this paper, we investigate the problem of spectral resolution in female speech for speaker identification. Finally, a speaker recognition system is presented to compare the relative performance of different LP-based features such as (LPC and LPCC) and filterbank-based features such as Mel-frequency cepstral coefficients (MFCC) for identification of female speakers. The results are shown for database collected from 15 female speakers in Bengali.
This paper is about the reduction of the computational complexity of a speech codec. A linear predictive coding procedure is developed to allow its implementation with number theoretic transforms. The use of fermat nu...
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This paper is about the reduction of the computational complexity of a speech codec. A linear predictive coding procedure is developed to allow its implementation with number theoretic transforms. The use of fermat number transform can reduce, in a significant way, the cost of linearpredictive algorithm implantation on digital signal processor.
As wireless systems evolve toward supporting a wide array of services, including traditional voice service, using packet-switched transport, it becomes increasingly important to assess the impact of packet-switched tr...
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As wireless systems evolve toward supporting a wide array of services, including traditional voice service, using packet-switched transport, it becomes increasingly important to assess the impact of packet-switched transport protocols on voice quality. In this article we present a tutorial on voice quality evaluation for wireless packet-switched systems. We introduce an evaluation methodology that combines elementary objective voice quality metrics with a frame synchronization mechanism. ne methodology allows networking researchers to conduct effective and accurate quality evaluation of packet voice. To illustrate the use of the described evaluation methodology and interpretation of the results, we conduct a case study of the impact of robust header compression (ROHC) on the voice quality achieved with real-time transmission of GSM encoded voice over a wireless link.
This paper brings light on the digital signal processing (DSP) roots of a modern concept, voice over IP (VoIP). An example is also provided in which developments in DSP - speech coding, in particular - had a profound ...
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This paper brings light on the digital signal processing (DSP) roots of a modern concept, voice over IP (VoIP). An example is also provided in which developments in DSP - speech coding, in particular - had a profound impact on the early development of the ARPANET, the ancestor of the Internet. The author shows how packet speech, recently rediscovered and made popular as VoIP, was first successfully demonstrated in 1974 on the ARPANET and how the Internet protocol (IP) emerged largely as a result of that effort.
This paper describes a novel algorithm for transforming linear prediction coefficients (LPCs) to line spectral frequencies (LSFs) and line spectral pairs (LSPs) used by most of the speech processing applications. The ...
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This paper describes a novel algorithm for transforming linear prediction coefficients (LPCs) to line spectral frequencies (LSFs) and line spectral pairs (LSPs) used by most of the speech processing applications. The symmetric and antisymmetric polynomials (SAPS) for LSP/LSFs, corresponding to the LPC polynomial, are first multiplexed into a single real sequence. The required samples of SAPS correspond to the DFT of the obtained real sequence. The proposed algorithm is referred as PMLS as it is based on the Plus Minus (PM) algorithm an FFT which efficiently computes the DFT of a real sequence for the positive frequency interval only. The samples of the SAPS are efficiently utilized for the computation of a single parameter which is used for computation of LSF and LSP independently with some interpolation principles. This interpolation exploits the available samples of SAPS and does not require their samples at finer resolution. The efficiency of the PMLS is illustrated with the help of some examples. Some guidelines for an optimal implementation of PMLS on fixed point digital signal processors (DSPs) are also presented.
We present a new low-complexity algorithm for hyperspectral image compression that uses linear prediction in the spectral domain. We introduce a simple heuristic to estimate the performance of the linear predictor fro...
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We present a new low-complexity algorithm for hyperspectral image compression that uses linear prediction in the spectral domain. We introduce a simple heuristic to estimate the performance of the linear predictor from a pixel spatial context and a context modeling mechanism with one-band look-ahead capability, which improves the overall compression with marginal usage of additional memory. The proposed method is suitable to spacecraft on-board implementation, where limited hardware and low power consumption are key requirements. Finally, we present a least-squares optimized linear prediction technique that achieves better compression on data cubes acquired by the NASA JPL Airborne Visible/Infrared Imaging Spectrometer (AVIRIS).
This paper investigates a normalized cross-correlation-based doubletalk detector for acoustic echo cancellers. A novel low-complexity version is presented suitable for implementation on IP-enabled telephones employing...
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ISBN:
(纸本)0780391543
This paper investigates a normalized cross-correlation-based doubletalk detector for acoustic echo cancellers. A novel low-complexity version is presented suitable for implementation on IP-enabled telephones employing LPC-based speech coders. In particular. the algorithm obtains a decorrelated input signal and decorrelation filter coefficients directly from the speech decoder. The proposed algorithm is implemented using ITU G.729. and simulation results are collected for a typical room. Calibration data are obtained and shown to be independent of the speech input signal. It is also shown that the proposed algorithm has the same performance as a full-complexity version employing the same decorrelation filter order.
In this paper, we propose a new blind speech authentication method to verify the authenticity and integrity of speech. In this method, linear predictive coding (LPC) and least significant bit (LSB) steganography are c...
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In this paper, we propose a new blind speech authentication method to verify the authenticity and integrity of speech. In this method, linear predictive coding (LPC) and least significant bit (LSB) steganography are combined, using LPC prediction error (LPCPE) as invariable features of speech and inserting these features into least significant bits of speech samples. When verifying the speech, we extract the inserted features, and compare them with recalculated LPCPE. Experimental results show that this method can detect not only the tampering in speech but also the position and region of the tampering
The problem of using neural networks for military vehicle classification on the basis of ground vibration is presented in this paper. One of the main elements of the system is unit called geophone. This unit allows to...
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The problem of using neural networks for military vehicle classification on the basis of ground vibration is presented in this paper. One of the main elements of the system is unit called geophone. This unit allows to measure ground vibrations in each direction for certain period of time. The value of amplitude is used to fix LPC values of each vehicle. Because the multilayer perceptron is used, the learning set has to be prepared. Please find attached the results of using neural network such as: example of learning, validation and test sets, structure of the networks and learning algorithm, learning and testing results.
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