The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Andreas Spanias - One of the best experts on this subject based on the ideXlab platform.
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Sinusoidal Component selection based on partial loudness criteria
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Harish Krishnamoorthi, Andreas SpaniasAbstract:Sinusoidal models are widely used in parametric speech and audio coding schemes. A common requirement in these applications is to select only a subset of Components that provide the greatest perceptual benefit particularly at low bitrates. Usually, perceptual Sinusoidal Component selection algorithms make use of greedy algorithms that are computationally expensive. In this paper, we present a new algorithm that selects Sinusoidal Components based on the partial loudness model proposed by Moore & Glasberg. We compare the performance of the proposed algorithm in terms of perceptual benefit and computational complexity to other existing Sinusoidal selection algorithms.
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ICASSP - Sinusoidal Component selection based on partial loudness criteria
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Harish Krishnamoorthi, Andreas SpaniasAbstract:Sinusoidal models are widely used in parametric speech and audio coding schemes. A common requirement in these applications is to select only a subset of Components that provide the greatest perceptual benefit particularly at low bitrates. Usually, perceptual Sinusoidal Component selection algorithms make use of greedy algorithms that are computationally expensive. In this paper, we present a new algorithm that selects Sinusoidal Components based on the partial loudness model proposed by Moore & Glasberg. We compare the performance of the proposed algorithm in terms of perceptual benefit and computational complexity to other existing Sinusoidal selection algorithms.
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low complexity Sinusoidal Component selection using loudness patterns
International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Harish Krishnamoorthi, Andreas Spanias, Visar Berisha, Homin KwonAbstract:Sinusoidal modeling of audio at low-bit rates involves selecting a limited number of parameters according to a quantitative or perceptual criterion. Most perceptual Sinusoidal Component selection strategies are computationally intensive and not suitable for real-time applications. In this paper, a computationally efficient Sinusoidal selection algorithm based on a novel hybrid loudness estimation scheme is presented. The hybrid scheme first estimates efficiently the loudness of a multi-tone signal from the loudness patterns of its constituent Sinusoidal Components. Then it refines this estimate by performing a full evaluation of loudness but only in select critical bands. Experimental results show that the proposed technique maintains a low perceptual Sinusoidal synthesis error at a much lower computational complexity.
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ICASSP - Low-complexity Sinusoidal Component selection using loudness patterns
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Harish Krishnamoorthi, Andreas Spanias, Visar Berisha, Homin KwonAbstract:Sinusoidal modeling of audio at low-bit rates involves selecting a limited number of parameters according to a quantitative or perceptual criterion. Most perceptual Sinusoidal Component selection strategies are computationally intensive and not suitable for real-time applications. In this paper, a computationally efficient Sinusoidal selection algorithm based on a novel hybrid loudness estimation scheme is presented. The hybrid scheme first estimates efficiently the loudness of a multi-tone signal from the loudness patterns of its constituent Sinusoidal Components. Then it refines this estimate by performing a full evaluation of loudness but only in select critical bands. Experimental results show that the proposed technique maintains a low perceptual Sinusoidal synthesis error at a much lower computational complexity.
Harish Krishnamoorthi - One of the best experts on this subject based on the ideXlab platform.
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Sinusoidal Component selection based on partial loudness criteria
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Harish Krishnamoorthi, Andreas SpaniasAbstract:Sinusoidal models are widely used in parametric speech and audio coding schemes. A common requirement in these applications is to select only a subset of Components that provide the greatest perceptual benefit particularly at low bitrates. Usually, perceptual Sinusoidal Component selection algorithms make use of greedy algorithms that are computationally expensive. In this paper, we present a new algorithm that selects Sinusoidal Components based on the partial loudness model proposed by Moore & Glasberg. We compare the performance of the proposed algorithm in terms of perceptual benefit and computational complexity to other existing Sinusoidal selection algorithms.
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ICASSP - Sinusoidal Component selection based on partial loudness criteria
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Harish Krishnamoorthi, Andreas SpaniasAbstract:Sinusoidal models are widely used in parametric speech and audio coding schemes. A common requirement in these applications is to select only a subset of Components that provide the greatest perceptual benefit particularly at low bitrates. Usually, perceptual Sinusoidal Component selection algorithms make use of greedy algorithms that are computationally expensive. In this paper, we present a new algorithm that selects Sinusoidal Components based on the partial loudness model proposed by Moore & Glasberg. We compare the performance of the proposed algorithm in terms of perceptual benefit and computational complexity to other existing Sinusoidal selection algorithms.
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low complexity Sinusoidal Component selection using loudness patterns
International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Harish Krishnamoorthi, Andreas Spanias, Visar Berisha, Homin KwonAbstract:Sinusoidal modeling of audio at low-bit rates involves selecting a limited number of parameters according to a quantitative or perceptual criterion. Most perceptual Sinusoidal Component selection strategies are computationally intensive and not suitable for real-time applications. In this paper, a computationally efficient Sinusoidal selection algorithm based on a novel hybrid loudness estimation scheme is presented. The hybrid scheme first estimates efficiently the loudness of a multi-tone signal from the loudness patterns of its constituent Sinusoidal Components. Then it refines this estimate by performing a full evaluation of loudness but only in select critical bands. Experimental results show that the proposed technique maintains a low perceptual Sinusoidal synthesis error at a much lower computational complexity.
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ICASSP - Low-complexity Sinusoidal Component selection using loudness patterns
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Harish Krishnamoorthi, Andreas Spanias, Visar Berisha, Homin KwonAbstract:Sinusoidal modeling of audio at low-bit rates involves selecting a limited number of parameters according to a quantitative or perceptual criterion. Most perceptual Sinusoidal Component selection strategies are computationally intensive and not suitable for real-time applications. In this paper, a computationally efficient Sinusoidal selection algorithm based on a novel hybrid loudness estimation scheme is presented. The hybrid scheme first estimates efficiently the loudness of a multi-tone signal from the loudness patterns of its constituent Sinusoidal Components. Then it refines this estimate by performing a full evaluation of loudness but only in select critical bands. Experimental results show that the proposed technique maintains a low perceptual Sinusoidal synthesis error at a much lower computational complexity.
Marc Bodson - One of the best experts on this subject based on the ideXlab platform.
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Analysis and Implementation of an Adaptive Algorithm for the Rejection of Multiple Sinusoidal Disturbances
IEEE Transactions on Control Systems Technology, 2009Co-Authors: X. Guo, Marc BodsonAbstract:A discrete-time adaptive algorithm is proposed to reject periodic disturbances in the case where the frequencies are unknown and a reference sensor is not available. The stability of the algorithm is analyzed using averaging theory, and the design of the parameters is based on the linearized averaged system. While the algorithm is first designed for rejecting periodic disturbances with one Sinusoidal Component, it is also extended to deal with cases where the disturbance has multiple Sinusoidal Components. A frequency separation method is proposed to prevent the frequency estimates from converging to the same value. The effectiveness of the adaptive scheme is validated in simulations and in experiments on an active noise control testbed.
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Theory and implementation of adaptive algorithms for rejecting disturbances having multiple Sinusoidal Components
2006Co-Authors: Marc Bodson, X. GuoAbstract:This dissertation is concerned about adaptive algorithms for rejecting disturbances having multiple Sinusoidal Components. A discrete-time adaptive algorithm is proposed to reject such disturbances in the case where the frequencies are unknown and possibly time-varying. A reference sensor is not assumed to be available. The stability of the algorithm is analyzed using averaging theory, and the design of the parameters is based on the linearized averaged system. While the algorithm is first designed for rejecting periodic disturbances with one Sinusoidal Component, it is also extended to deal with cases where the disturbance has multiple Sinusoidal Components. A frequency separation method is proposed to prevent the frequency estimates from converging to the same value. The dissertation also considers the problem of rejecting disturbances with two Sinusoidal Components in the case where the frequencies are unknown and closely spaced. A natural approach consists in canceling the Components using two separate adaptive algorithms combined in a single scheme. However, experiments in active noise control applications have shown that convergence using such an approach could be very slow. The alternative approach of this dissertation consists in representing the disturbance signal as a single sinusoid with time-varying magnitude and phase. The theoretical basis and the limitations of such a representation are first discussed. Then, an adaptive disturbance rejection algorithm is proposed and the resulting nonlinear system is analyzed using some approximations. Active noise control experiments demonstrate that the proposed algorithm has better convergence properties than an algorithm designed to cancel the two frequency Components separately. In some cases, however, the cost is a small residual error on the output signal. The last problem considered in this dissertation is the rejection of Sinusoidal disturbances with known but rapidly varying frequencies. The dissertation shows that a large class of adaptive algorithms for disturbance cancellation yields control systems that are equivalent to linear compensators implementing the internal model principle. The fact had been known to be true for periodic disturbances with fixed frequency. However, the dissertation shows that the result can be extended to disturbances of time-varying frequency (i.e., frequency-modulated signals), regardless of the rate of variation of the frequency. In particular, several adaptive controllers are shown to be equivalent to linear time-varying compensators, with a pseudo-gradient algorithm being equivalent to a polytopic linear parameter varying compensator. The equivalence provides an opportunity to apply knowledge gained either in adaptive control or in robust linear control to the other field.
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Rejection of disturbances with a large Sinusoidal Component of unknown frequency
Smart Structures and Materials 1996: Mathematics and Control in Smart Structures, 1996Co-Authors: Marc Bodson, Scott C. DouglasAbstract:An important application of smart materials and structures is the control of periodic disturbances or vibration in environments such as aircrafts and helicopters. In these cases, the source of the noise is a rotating machine, so that a large Component of the disturbance is periodic. While it is often possible to take measurements on the machine that is the source of the periodic disturbance, concerns of reliability and maintainability sometimes make such measurements undesirable, if not impossible. Then, the problem is to attenuate a periodic disturbance whose frequency is unknown. An adaptive algorithm is presented in this paper for periodic disturbance attenuation, using the concept of a phase-locked loop. For simplicity, the disturbance is assumed to be Sinusoidal. An approximate analysis is performed and the results are found useful to select the design parameters. Simulations are presented that demonstrate the ability of the algorithm to reject Sinusoidal disturbances with unknown frequency, and to follow signals with slowly varying magnitude and frequency. The effect of measurement noise and of additional disturbances is also analyzed. The results provide numerical measures of the parameter variations and of the loss of performance in the presence of noise.
Richard Heusdens - One of the best experts on this subject based on the ideXlab platform.
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Schemes for optimal frequency-differential encoding of Sinusoidal model parameters
Signal Processing, 2003Co-Authors: Jesper Jensen, Richard HeusdensAbstract:Sinusoidal coding plays an important role in low bit-rate audio coding. This paper considers frequency-differential encoding of the Sinusoidal model parameters as an alternative to time-differential encoding. For a given signal frame, the parameters of each Sinusoidal Component may be encoded either differentially relative to other Components in the same frame, or directly, i.e., without differential encoding. Using basic tools from graph theory, we derive several algorithms for finding bit-rate optimal combinations of direct and differential encoding of the Sinusoidal parameters. In simulation experiments with audio signals, the algorithms showed bit-rate reductions of up to 28% relative to direct encoding. Furthermore, when compared to what can be considered a traditional FD encoding scheme (as used in MPEG-4 audio), the proposed algorithms achieve bit-rate reductions of up to 6%.
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Schemes for optimal frequency-differential encoding of Sinusoidal model parameters
Signal Processing, 2003Co-Authors: Jesper Jensen, Richard HeusdensAbstract:Sinusoidal coding plays an important role in low bit-rate audio coding. This paper considers frequency-differential encoding of the Sinusoidal model parameters as an alternative to time-differential encoding. For a given signal frame, the parameters of each Sinusoidal Component may be encoded either differentially relative to other Components in the same frame, or directly, i.e., without differential encoding. Using basic tools from graph theory, we derive several algorithms for finding bit-rate optimal combinations of direct and differential encoding of the Sinusoidal parameters. In simulation experiments with audio signals, the algorithms showed bit-rate reductions of up to 28% relative to direct encoding. Furthermore, when compared to what can be considered a traditional FD encoding scheme (as used in MPEG-4 audio), the proposed algorithms achieve bit-rate reductions of up to 6%.
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ICASSP - Optimal frequency-differential encoding of Sinusoidal model parameters
IEEE International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Jesper Jensen, Richard HeusdensAbstract:Sinusoidal coding has proven to be efficient for low bit-rate audio coding. In this paper we consider schemes for frequency-differential (FD) encoding of the Sinusoidal model parameters. For a given signal frame, the parameters of a Sinusoidal Component may be encoded either differentially relative to other Components in the same frame, or directly, i.e., without differential encoding. Using basic tools from graph theory, two algorithms are derived for finding bit rate optimal combinations of direct and differential encoding of the Sinusoidal parameters. In simulation experiments with audio signals, the algorithms showed bit-rate reductions of up to 27% relative to direct encoding. Furthermore, when compared to a commonly used FD encoding scheme, the proposed algorithms achieved bit rate reductions of up to 7%.
Toshio Irino - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Higher order waveform symmetry measure and its application to periodicity detectors for speech and singing with fine temporal resolution
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Hideki Kawahara, Masanori Morise, Ryuichi Nisimura, Toshio IrinoAbstract:Another simple and high-speed F0 extractor with high temporal resolution based on our previous proposal has been developed by adding a higher-order symmetry measure. This extension made the proposed method significantly more robust than the previous one. The proposed method is a detector of the lowest prominent Sinusoidal Component. It can use several F0 refinement procedures when the signal is the sum of harmonic Sinusoidal Components. The refinement procedure presented here is based on a stable representation of instantaneous frequency of periodic signals. The whole procedure implemented by Matlab runs faster than realtime on usual PCs for 44,100 Hz sampled sounds. Application of the proposed algorithm revealed that rapid temporal modulations in both F0 trajectory and spectral envelope exist typically in expressive voices such as those those used in lively singing performance.
