2,374 resources related to Cepstral analysis
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
2020 IEEE International Symposium on Circuits and Systems (ISCAS)
The International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems (CAS) Society and the world’s premier networking and exchange forum for researchers in the highly active fields of theory, design and implementation of circuits and systems. ISCAS2020 focuses on the deployment of CASS knowledge towards Society Grand Challenges and highlights the strong foundation in methodology and the integration of multidisciplinary approaches which are the distinctive features of CAS contributions. The worldwide CAS community is exploiting such CASS knowledge to change the way in which devices and circuits are understood, optimized, and leveraged in a variety of systems and applications.
The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.
IEEE Antennas and Wireless Propagation Letters (AWP Letters) will be devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation.
Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
The design and manufacture of consumer electronics products, components, and related activities, particularly those used for entertainment, leisure, and educational purposes
Measurements and instrumentation utilizing electrical and electronic techniques.
IEEE International Workshop on Intelligent Signal Processing, 2005., 2005
This paper presents a sub-bund approach of audio source indexing technique without any prior information about the sources. The empirical mode decomposition (EMD) scheme, capable of decomposing nonlinear and non- stationary signal into some bases, is employed to implement the proposed sub- band method. The feature vectors are derived from each of the selected sub- bands of the signal block. ...
1993 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1993
One of the problems with speaker-independent speech recognition is the huge amount of training data required, which implies a high cost. The performance of a discrete density hidden-Markov-model speaker-independent speech recognition system when using a small set of examples for training is investigated. By using LPC (linear prediction coding)-based analysis, an approximately 12% error rate was obtained on a highly ...
Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96, 1996
The paper addresses the environmental mismatch problem that arises from noise and channel variabilities. A new feature mapping technique based on an optimal affine transform of the cepstrum is proposed to solve the mismatch problem observed over the speaker recognition systems. It is designed based on the fact that both the channel and noise interferences basically cause the cepstrum space ...
IEEE Signal Processing Letters, 1999
A novel method for designing recursive allpass digital filters is presented. This method uses cepstral coefficients to design the allpass filter. For a stable allpass filter, the denominator polynomial must be of minimum phase. For a minimum phase polynomial the magnitude function and the group delay function are related through the cepstral coefficients. Hence, given the group delay function, the ...
Canadian Journal of Electrical and Computer Engineering, 2004
A novel transform-domain image watermark based on cepstrum analysis is proposed in this paper. A complex cepstrum-based scheme is developed to embed a gray-level image in the one-dimensional cepstrum components of the original image signal. Computer simulation results show that the proposed algorithm is imperceptible and is robust to most watermarking attacks, especially to image cropping, JPEG compression and multipliable ...
IMS 2011 Microapps - A Practical Approach to Verifying RFICs with Fast Mismatch Analysis
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IMS 2012 Microapps - Generation and Analysis Techniques for Cost-efficient SATCOM Measurements Richard Overdorf, Agilent
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Zohara Cohen AMA EMBS Individualized Health
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Network Analysis: RF Boot Camp
Micro-Apps 2013: Power Added Efficiency (PAE) Analysis with 8990B Peak Power Analyzer
Brooklyn 5G Summit 2014: Dr. Robert Heath on Coverage and Capacity Analysis of Dense Millimeter Wave Cellular System
Real-time Spectrogram Analysis of Continuous Optical Wavefields - José Azaña - Closing Ceremony, IPC 2018
MicroApps: New Technologies and Techniques for Wideband Analysis (Agilent Technologies)
Robotics History: Narratives and Networks Oral Histories: Barbara Hayes Roth
This paper presents a sub-bund approach of audio source indexing technique without any prior information about the sources. The empirical mode decomposition (EMD) scheme, capable of decomposing nonlinear and non- stationary signal into some bases, is employed to implement the proposed sub- band method. The feature vectors are derived from each of the selected sub- bands of the signal block. Linear predictive cepstral coefficient (LPCC) is used as the principal feature of this indexing system. The dominant features are selected at each sub-band with principal component analysis (PCA). A dominancy dependent weighted function is introduced to measure the similarity between the source model and the derived feature vectors. The higher order statistics (HOS) is employed to compute the sub-band LPCC features. The use of HOS makes LPCC less affected by Gaussian type noises. The experimental results show that the sub-band technique produces better indexing efficiency than that of the full-band technique.
One of the problems with speaker-independent speech recognition is the huge amount of training data required, which implies a high cost. The performance of a discrete density hidden-Markov-model speaker-independent speech recognition system when using a small set of examples for training is investigated. By using LPC (linear prediction coding)-based analysis, an approximately 12% error rate was obtained on a highly confusable telephone- quality vocabulary. Using RASTA PLP analysis, a 4% error rate can be achieved. The reason for this improvement is that the RASTA filter filters out the convolutional noise of the different telephone lines and PLP analysis suppresses speaker-dependent details. RASTA filtering was also tried out on the LPC cepstra and gives, with a higher model order, the same results as RASTA PLP.<<ETX>>
The paper addresses the environmental mismatch problem that arises from noise and channel variabilities. A new feature mapping technique based on an optimal affine transform of the cepstrum is proposed to solve the mismatch problem observed over the speaker recognition systems. It is designed based on the fact that both the channel and noise interferences basically cause the cepstrum space to undergo an affine transformation. By taking an inverse transformation, one can easily decouple from the speech the effects of the channel and noise. Alternatively, one can take a forward transform of the training data to simulate the operating conditions.
A novel method for designing recursive allpass digital filters is presented. This method uses cepstral coefficients to design the allpass filter. For a stable allpass filter, the denominator polynomial must be of minimum phase. For a minimum phase polynomial the magnitude function and the group delay function are related through the cepstral coefficients. Hence, given the group delay function, the cepstral coefficients corresponding to the denominator polynomial part are first determined. From the cepstral coefficients the minimum phase denominator polynomial coefficients are determined through a nonlinear recursive difference equation.
A novel transform-domain image watermark based on cepstrum analysis is proposed in this paper. A complex cepstrum-based scheme is developed to embed a gray-level image in the one-dimensional cepstrum components of the original image signal. Computer simulation results show that the proposed algorithm is imperceptible and is robust to most watermarking attacks, especially to image cropping, JPEG compression and multipliable noise.
It is demonstrated that using the proposed probabilistic vector mapping algorithm as a feature preprocessor results in robust performance levels across a wide range of signal-to-noise (SNR) levels. The authors evaluate the algorithm using an HMM word spotting system trained with clean cepstral features and tested with vector mapped noisy cepstra. In addition to robust behavior, it is shown that using the vector mapper results in performance that equals or exceeds that of using matched training and testing. For example, with 10-dB SNR testing speech, word spotting performance with the vector mapping preprocessor and clean training is 15% better than matching training with 10-dB SNR speech. A mapping algorithm based on the method of radial basis functions (RBFs) for mapping noisy speech features into the space of clean features is presented. Performance using this RBF mapper is shown to be comparable to that of the vector mapper.<<ETX>>
There are some fundamental frequency detection methods for speech processing such as cepstrum analysis. However, the traditional methods need a lot of computing time and arithmetic processing. Moreover, a speech signal is converted into frequency domain by using an analysis section. In this paper, we propose a fast direct transformation (FDT). FDT extracts an amplitude feature of a signal by a simple computation. We perform the fundamental frequency detection of speech signal by using FDT. We compare the FDT algorithm with the conventional fundamental frequency detection methods by using teacher data (fundamental frequency) detected by the inspection. We perform an improvement as compared with an autocorrelation method by using FDT algorithm.
The variability of Lombard speech under different noise conditions and an adaptation method for the different Lombard speech are discussed. For this purpose, various kinds of Lombard speech are recorded under different conditions of noise injected into a earphone with controlled feedback of voice. First, DTW word recognition experiments using clean speech as a reference are performed to show that the higher the noise level becomes the more seriously the utterance is affected. The second linear transformation of the cepstral feature vector is tested to show that when given enough (more than 100 words) training data, the transformation matrix can be correctly learned for each of the noise conditions. Interpolation of the transfer matrix is then proposed in order to reduce the adaptation parameter and number of training samples. The authors show, finally, that five words are enough for the learning interpolated transformation matrix for unknown noise conditions.
In many problems of medical diagnosis as well as therapy and rehabilitation, evaluation of the deformed speech signal quality is required. In problems of distorted speech diagnosis the regular methods of speech signal preprocessing and classification, used in speech or voice recognition, totally fail. Also the standard speech signal parametrization techniques (e.g. LPC or cepstral coefficients) cannot satisfactorily describe pathological speech because of its dissimilar phonetic and acoustic structure compared with correct speech, and also because the aim of the recognition process is totally different. In the paper a new method for processing and classification of pathologically deformed speech, based on neural networks techniques, is presented and discussed.
A weighted Viterbi algorithm (HMM) is proposed and applied in combination with spectral subtraction and cepstral mean normalization to cancel both additive and convolutional noise in speech recognition. The weighted Viterbi approach is compared and used in combination with state duration modelling. The results presented show that a proper weight on the information provided by static parameters can substantially reduce the error rate, and that the weighting procedure improves better the robustness of the Viterbi algorithm than the introduction of temporal constraints with a low computational load. Finally, it is shown that the weighted Viterbi algorithm in combination with temporal constraints leads to a high recognition accuracy at moderate SNRs without the need of an accurate noise model.
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