The Experts below are selected from a list of 270 Experts worldwide ranked by ideXlab platform
Paavo Alku - One of the best experts on this subject based on the ideXlab platform.
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Frequency-warped time-weighted Linear Prediction for glottal vocoding
2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017Co-Authors: Manu Airaksinen, Jouni Pohjalainen, Bajibabu Bollepalli, Paavo AlkuAbstract:Auto-regressive modeling is a prevalent source-filter separation method of speech. Conventional Linear Prediction (LP) and its derivatives such as weighted Linear Prediction (WeLP) produce parametric spectral models within a Linear frequency scale, whereas frequency-warped Linear Prediction (WaLP) can be used to take into account the frequency sensitivity of the human auditory system. From the perspective of glottal vocoding, the principles behind WeLP have been found to be beneficial for an accurate separation of the glottal source signal and the vocal tract transfer function, but this approach can not utilize the auditory benefits of frequency warping. On the other hand, the WaLP approach suffers from less accurate source-filter separation properties. In this study, a generalized frequency-warped time-weighted Linear Prediction (WWLP) analysis is proposed. Experiments with WWLP are performed within the context of glottal vocoding. The subjective listening test results show that WWLP-based spectral envelope modeling is able to increase quality over previously developed methods in some of the test cases.
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Gaussian mixture Linear Prediction
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Jouni Pohjalainen, Paavo AlkuAbstract:This work introduces an approach to Linear predictive signal analysis utilizing a Gaussian mixture autoregressive model. By initializing different autoregressive states of the model to approximately correspond to the target signal and the expected type of undesired signal components, such as background noise, the iterative parameter estimation converges towards a focused Linear Prediction model of the target signal. Differently initialized and trained variants of mixture Linear Prediction are evaluated using objective spectrum distortion measures as well as in feature extraction for speech detection in the presence of ambient noise. In these evaluations, the novel analysis methods perform better than the Fourier transform and conventional Linear Prediction.
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Wideband Parametric Speech Synthesis Using Warped Linear Prediction
13Th Annual Conference of the International Speech Communication Association 2012 (Interspeech 2012) Vols 1-3, 2012Co-Authors: Tuomo Raitio, Antti Suni, Martti Vainio, Paavo AlkuAbstract:This paper studies the use of warped Linear Prediction (WLP) for\nwideband parametric speech synthesis. As the sampling frequency is\nincreased from the usual 16 kHz, Linear frequency resolution of\nconventional Linear Prediction (LP) cannot efficiently model the speech\nspectrum. By using frequency warping that weights perceptually the most\nimportant formant information, spectral models with better accuracy and\nlower model orders can be utilized. In this work, WLP is embedded in a\nparametric speech synthesizer to efficiently create wideband synthetic\nspeech. Experiments show that WLP-based wideband synthetic speech is\nrated better compared to narrowband speech and wideband LP-based speech.
Marc Moonen - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Real-time implementations of sparse Linear Prediction for speech processing
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Tobias Lindstrøm Jensen, Daniele Giacobello, Mads Groesboll Christensen, Søren Holdt Jensen, Marc MoonenAbstract:Employing sparsity criteria in Linear Prediction of speech has been proven successful for several analysis and coding purposes. However, sparse Linear Prediction comes at the expenses of a much higher computational burden and numerical sensitivity compared to the traditional minimum variance approach. This makes sparse Linear Prediction difficult to deploy in real-time systems. In this paper, we present a step towards real-time implementation of the sparse Linear Prediction problem using hand-tailored interior-point methods. Using compiled implementations the sparse Linear Prediction problems corresponding to a frame size of 20ms can be solved on a standard PC in approximately 2ms and orders faster than with general purpose software.
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sparse Linear Prediction and its applications to speech processing
IEEE Transactions on Audio Speech and Language Processing, 2012Co-Authors: Daniele Giacobello, Manohar N. Murthi, Søren Holdt Jensen, Mads Graesboll Christensen, Marc MoonenAbstract:The aim of this paper is to provide an overview of Sparse Linear Prediction, a set of speech processing tools created by introducing sparsity constraints into the Linear Prediction framework. These tools have shown to be effective in several issues related to modeling and coding of speech signals. For speech analysis, we provide predictors that are accurate in modeling the speech production process and overcome problems related to traditional Linear Prediction. In particular, the predictors obtained offer a more effective decoupling of the vocal tract transfer function and its underlying excitation, making it a very efficient method for the analysis of voiced speech. For speech coding, we provide predictors that shape the residual according to the characteristics of the sparse encoding techniques resulting in more straightforward coding strategies. Furthermore, encouraged by the promising application of compressed sensing in signal compression, we investigate its formulation and application to sparse Linear predictive coding. The proposed estimators are all solutions to convex optimization problems, which can be solved efficiently and reliably using, e.g., interior-point methods. Extensive experimental results are provided to support the effectiveness of the proposed methods, showing the improvements over traditional Linear Prediction in both speech analysis and coding.
Daniele Giacobello - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Real-time implementations of sparse Linear Prediction for speech processing
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Tobias Lindstrøm Jensen, Daniele Giacobello, Mads Groesboll Christensen, Søren Holdt Jensen, Marc MoonenAbstract:Employing sparsity criteria in Linear Prediction of speech has been proven successful for several analysis and coding purposes. However, sparse Linear Prediction comes at the expenses of a much higher computational burden and numerical sensitivity compared to the traditional minimum variance approach. This makes sparse Linear Prediction difficult to deploy in real-time systems. In this paper, we present a step towards real-time implementation of the sparse Linear Prediction problem using hand-tailored interior-point methods. Using compiled implementations the sparse Linear Prediction problems corresponding to a frame size of 20ms can be solved on a standard PC in approximately 2ms and orders faster than with general purpose software.
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sparse Linear Prediction and its applications to speech processing
IEEE Transactions on Audio Speech and Language Processing, 2012Co-Authors: Daniele Giacobello, Manohar N. Murthi, Søren Holdt Jensen, Mads Graesboll Christensen, Marc MoonenAbstract:The aim of this paper is to provide an overview of Sparse Linear Prediction, a set of speech processing tools created by introducing sparsity constraints into the Linear Prediction framework. These tools have shown to be effective in several issues related to modeling and coding of speech signals. For speech analysis, we provide predictors that are accurate in modeling the speech production process and overcome problems related to traditional Linear Prediction. In particular, the predictors obtained offer a more effective decoupling of the vocal tract transfer function and its underlying excitation, making it a very efficient method for the analysis of voiced speech. For speech coding, we provide predictors that shape the residual according to the characteristics of the sparse encoding techniques resulting in more straightforward coding strategies. Furthermore, encouraged by the promising application of compressed sensing in signal compression, we investigate its formulation and application to sparse Linear predictive coding. The proposed estimators are all solutions to convex optimization problems, which can be solved efficiently and reliably using, e.g., interior-point methods. Extensive experimental results are provided to support the effectiveness of the proposed methods, showing the improvements over traditional Linear Prediction in both speech analysis and coding.
Peter Händel - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Graph Linear Prediction results in smaller error than standard Linear Prediction
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Aran Venkitaraman, Saikat Chatterjee, Peter HändelAbstract:Linear Prediction is a popular strategy employed in the analysis and representation of signals. In this paper, we propose a new Linear Prediction approach by considering the standard Linear Prediction in the context of graph signal processing, which has gained significant attention recently. We view the signal to be defined on the nodes of a graph with an adjacency matrix constructed using the coefficients of the standard Linear predictor (SLP). We prove theoretically that the graph based Linear Prediction approach results in an equal or better performance compared with the SLP in terms of the Prediction gain. We illustrate the proposed concepts by application to real speech signals.
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Graph Linear Prediction results in smaller error than standard Linear Prediction
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Aran Venkitaraman, Saikat Chatterjee, Peter HändelAbstract:Linear Prediction is a popular strategy employed in the analysis and representation of signals. In this paper, we propose a new Linear Prediction approach by considering the standard Linear Prediction in the context of graph signal processing, which has gained significant attention recently. We view the signal to be defined on the nodes of a graph with an adjacency matrix constructed using the coefficients of the standard Linear predictor (SLP). We prove theoretically that the graph based Linear Prediction approach results in an equal or better performance compared with the SLP in terms of the Prediction gain. We illustrate the proposed concepts by application to real speech signals.
Evgeny A. Verbitskiy - One of the best experts on this subject based on the ideXlab platform.
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Similarities and Differences Between Warped Linear Prediction and Laguerre Linear Prediction
IEEE Transactions on Audio Speech and Language Processing, 2011Co-Authors: Albertus Den C. Brinker, Harish Krishnamoorthi, Evgeny A. VerbitskiyAbstract:Linear Prediction has been successfully applied in many speech and audio processing systems. This paper presents the similarities and differences between two classes of Linear Prediction schemes, namely, Warped Linear Prediction (WLP) and Laguerre Linear Prediction (LLP). It is shown that both systems are closely related. In particular, we show that the LLP is in fact a WLP system where the optimization procedure is adapted such that the whitening property is automatically incorporated. The adaptation consists of a new Linear constraint on the parameters. Furthermore, we show that an optimized WLP scheme where whitening is achieved by prefiltering before estimating the optimal coefficients results in a filter having all except the last reflection coefficient equal to those of the optimal LLP filter.
