Готові списки джерел за темами / Multi-channel linear prediction

Добірка наукової літератури з теми "Multi-channel linear prediction"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Multi-channel linear prediction"

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Jukic, Ante, Toon van Waterschoot, Timo Gerkmann, and Simon Doclo. "Multi-Channel Linear Prediction-Based Speech Dereverberation With Sparse Priors." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 9 (September 2015): 1509–20. http://dx.doi.org/10.1109/taslp.2015.2438549.

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Mousavi, Leila, Farbod Razzazi, and Afrooz Haghbin. "Blind speech dereverberation using sparse decomposition and multi-channel linear prediction." International Journal of Speech Technology 22, no. 3 (July 15, 2019): 729–38. http://dx.doi.org/10.1007/s10772-019-09620-x.

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3

DELCROIX, M., T. HIKICHI, and M. MIYOSHI. "On a Blind Speech Dereverberation Algorithm Using Multi-Channel Linear Prediction." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E89-A, no. 10 (October 1, 2006): 2837–46. http://dx.doi.org/10.1093/ietfec/e89-a.10.2837.

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4

Park, Sangwoo, and Osvaldo Simeone. "Speeding Up Training of Linear Predictors for Multi-Antenna Frequency-Selective Channels via Meta-Learning." Entropy 24, no. 10 (September 26, 2022): 1363. http://dx.doi.org/10.3390/e24101363.

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An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols. This paper proposes novel channel-prediction algorithms that address this goal by integrating transfer and meta-learning with a reduced-rank parametrization of the channel. The proposed methods optimize linear predictors by utilizing data from previous frames, which are generally characterized by distinct propagation characteristics, in order to enable fast training on the time slots of the current frame. The proposed predictors rely on a novel long short-term decomposition (LSTD) of the linear prediction model that leverages the disaggregation of the channel into long-term space-time signatures and fading amplitudes. We first develop predictors for single-antenna frequency-flat channels based on transfer/meta-learned quadratic regularization. Then, we introduce transfer and meta-learning algorithms for LSTD-based prediction models that build on equilibrium propagation (EP) and alternating least squares (ALS). Numerical results under the 3GPP 5G standard channel model demonstrate the impact of transfer and meta-learning on reducing the number of pilots for channel prediction, as well as the merits of the proposed LSTD parametrization.
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5

Delcroix, Marc, Takafumi Hikichi, and Masato Miyoshi. "Blind dereverberation algorithm for speech signals based on multi-channel linear prediction." Acoustical Science and Technology 26, no. 5 (2005): 432–39. http://dx.doi.org/10.1250/ast.26.432.

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6

Wang, Ning, and Jia-Yang Li. "Efficient Multi-Channel Thermal Monitoring and Temperature Prediction Based on Improved Linear Regression." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–9. http://dx.doi.org/10.1109/tim.2021.3139659.

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7

Yoshioka, Takuya, and Tomohiro Nakatani. "Generalization of Multi-Channel Linear Prediction Methods for Blind MIMO Impulse Response Shortening." IEEE Transactions on Audio, Speech, and Language Processing 20, no. 10 (December 2012): 2707–20. http://dx.doi.org/10.1109/tasl.2012.2210879.

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8

Jin, Xin, Xin Liu, Jinyun Guo, and Yi Shen. "Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction." Sensors 21, no. 4 (February 17, 2021): 1403. http://dx.doi.org/10.3390/s21041403.

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Анотація:
Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.
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Yuxuan Zhou, Yuxuan Zhou, Wanzhong Chen Yuxuan Zhou, Linlin Li Wanzhong Chen, Linlin Gong Linlin Li, and Chang Liu Linlin Gong. "The Energy-Efficient Resource Allocation of Multi-Modal Perception for Affective Brain-Computer Interactions Based on Non-Linear Iterative Prediction Scheme." 網際網路技術學刊 24, no. 3 (May 2023): 641–50. http://dx.doi.org/10.53106/160792642023052403009.

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Анотація:
<p>For the whole environmental settings in this research, the conventional affective brain-computer interactions can not build a good performance on energy-efficient resource of network&rsquo;s forwarding ports and routing paths due to its poor allocation function of cognitive radio networks, based on the novel interactive networking architecture, the model of non-linear iterative prediction scheme in interaction was successively proposed. This research proposes a modified LSTM algorithm with a structure of non-linear iterative in complexity prediction, joins the multiple k modes selection and multi-agent systems, maximizes EERA of forwarding and routing while maintaining the communication quality. Firstly, considering whether this affective brain-computer interactions need the networking communication in system. Secondly, adjusting the forwarding and routing factors of energy-efficient resource allocation by selecting the best optimal energy-efficient resource for the links through the non-linear iterative prediction in a multi-modal perception. The simulation results show that compared with the other models and algorithms, the proposed scheme for affective brain-computer interactions, which has a nice performance on a higher EERA and channel utilization of a networking architecture of brain-computer interactions.</p> <p>&nbsp;</p>
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10

Lv, X., C. Jing, Y. Wang, and S. Jin. "A DEEP NEURAL NETWORK FOR SPATIOTEMPORAL PREDICTION OF THEFT CRIMES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-3/W2-2022 (October 27, 2022): 35–41. http://dx.doi.org/10.5194/isprs-archives-xlviii-3-w2-2022-35-2022.

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Abstract. Accurate crime prediction plays an important role in public safety, providing technical guidance and decision support for the police and government departments. Due to the dynamics and imbalance of crime distribution, it is difficult to build predictive models for it. Specifically, the fine-grained and non-linear spatiotemporal dependencies of crime data cannot be captured accurately. In this paper, a neural network model ST-ACLCrime based on ConvLSTM and SE block was proposed to predict the number of theft crimes in hotspot areas. By overlaying ConvLSTM layers, fine-grained spatiotemporal dependencies are captured while preserving spatial location information. To further enhance the global channel feature representation, SE block is used to recalibrate the channel features and enhance the channel inter-dependencies. In addition, the closeness and the period components are set to dynamically capture the dependence of different time trends. We choose the city of Chicago as the study case, and use a multi-level spatial grid to divide the whole city area. The experimental results show that the proposed model exceeds all baseline model, such as HA, CNN, LSTM, CNN-LSTM and ConvLSTM. It was effectively capturing spatiotemporal dependence and improving prediction accuracy.
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Дисертації з теми "Multi-channel linear prediction"

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Jukić, Ante [Verfasser]. "Sparse Multi-Channel Linear Prediction for Blind Speech Dereverberation / Ante Jukić." München : Verlag Dr. Hut, 2017. http://d-nb.info/1149580399/34.

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Chetupalli, Srikanth Raj. "Spatial Analysis and Reconstruction of Reverberant Speech." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4632.

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Speech signal includes the spoken message and a lot more information such as speaker emotion, identity, language, speaking location characteristic etc., which makes the human interaction lively, desirable and more useful. In the present day speech tele-communication, source related attributes are nearly preserved but the spatial attributes of the speech source such as the position, room reverberation, and ambient noise are meant to be ignored. Spatial perception of a person's speech in a reverberant environment comprises three components: direction, range and ``spaciousness'' as perceived by a local listener. A linear system based reverberant signal model has two components, directional and diffuse, which are responsible for the perception of source direction and spaciousness. The range perception of a source is commonly attributed to the relative levels of directional, and diffuse components. An analysis of the signal recorded by the multiple microphones, in terms of the source position, direction and diffuse components would enable a perceptually sensitive spatial speech reconstruction. In this thesis, we consider communication of speech along with its spatial attributes in an enclosure and its reconstruction at a receiver location to achieve a spatial speech communication solution. In the present approach, spatial speech communication involves estimation of the directional and diffuse components of a recorded source signal and the relative source position at the transmitter enclosure. We consider arbitrarily placed multi-microphone recording for spatial signal acquisition. We then extend a delayed \highlight{multi-channel} linear prediction (MCLP) based formulation to estimate the directional and diffuse signal components. In MCLP, the diffuse signal component is modeled using linear prediction in the short-time Fourier transform (STFT) domain, and the prediction residual is taken as the directional component. We develop three different methods for the estimation of prediction filter. In the first method, we consider the heavy-tail distribution nature of the directional signal STFT and propose a Student's t-distribution based Bayesian estimation. The model also includes an independent Gaussian prior for the prediction coefficients to account for the unknown prediction order of the MCLP. In the second formulation, the knowledge of clean speech signal is used explicitly as a constraint for the filter estimation, through an auto encoder neural network constrained power spectral density (PSD) estimation. It is shown to be more effective for the iterative MCLP optimum solution. In the third method, we consider a directional sound propagation model using the relative acoustic transfer function (RTF), and a distortion-less response spatial filter estimation. This method combines the benefits of MCLP dereverberation as well as spatial filtering, which makes it better suited for cases with noise or other directional interferers. Further, the source direction is also estimated as a latent parameter in this method. We then extend the formulation to a dynamic moving source scenario catering to source position changes. Using the linear dynamical system approach and the online spatial filtering, we develop a scheme to obtain both dereverberation and source tracking. We recognize that the directional and diffuse components of the reverberant source signal also contain cues about the source and microphone positions. Thus, we develop a method to compute the geometry of the multi-microphone placement using the diffuse component of the microphone signals, and then the directional component is used for source position estimation with respect to the microphones. The reverberant signal analysis and source position estimation is shown to be useful for spatial speech communication using the multiple spatially distributed microphone arrays, for signal acquisition at the transmitter and a multi-loudspeaker reconstruction at the receiver. We estimate the reverberant signal components and the source direction separately at each microphone array of the transmitter and the source position is estimated via fusion of individual direction estimates. The direct and diffuse components of the reverberant signal of the microphone array nearest to the estimated source position are considered for reconstruction at the receiver. A perceptually effective and simple reconstruction is considered at the receiver, in which the directional component of the reverberant signal is assumed to be coming from a point source and the reverberation component is diffuse all around the listener. We simulate the virtual transmitter source location using a four loudspeaker setup at the receiver: the vector base amplitude panning scheme is considered for the directional component and the diffuse component is used to recreate the spaciousness. The spaciousness is achieved through diffuse sound playback equally from all the loudspeakers after decorrelation and gain normalization. We show that the formulation is amenable to spatial scene modification, such as source direction and distance modification using the decomposed signal components and the source parameters.
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Частини книг з теми "Multi-channel linear prediction"

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Wang, Jing, Yi Zhao, Wenzhi Li, Fei Wang, Zesong Fei, and Xiang Xie. "Prediction Model of Multi-channel Audio Quality Based on Multiple Linear Regression." In Lecture Notes in Computer Science, 688–98. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24075-6_66.

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Тези доповідей конференцій з теми "Multi-channel linear prediction"

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Jukic, Ante, Zichao Wang, Toon van Waterschoot, Timo Gerkmann, and Simon Doclo. "Constrained multi-channel linear prediction for adaptive speech dereverberation." In 2016 IEEE International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2016. http://dx.doi.org/10.1109/iwaenc.2016.7602922.

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2

Koep, Niklas, Magnus Schafer, and Peter Vary. "Noise-shaping for closed-loop Multi-Channel Linear Prediction." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7177998.

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3

Liu, Hong, Xiuling Wang, Miao Sun, and Cheng Pang. "Multi-Channel Linear Prediction Based on Binaural Coherence for Speech Dereverberation." In Interspeech 2016. ISCA, 2016. http://dx.doi.org/10.21437/interspeech.2016-729.

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4

Se-young Kim, Hyung-jun Ju, Jeong-woo Han, Suk-youb Kang, Ki-man Kim, Ji-won Jung, and Young Yun. "Multi-channel speech dereverberation system based on modified linear prediction residual." In 2010 IEEE International Conference on Consumer Electronics (ICCE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icce.2010.5418769.

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Li, Guanjun, Shan Liang, Shuai Nie, and Wenju Liu. "Adaptive Dereverberation Using Multi-channel Linear Prediction with Deficient Length Filter." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682349.

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6

Pasha, Shahab, Christian Ritz, and Yue Xian Zou. "Spatial multi-channel linear prediction for dereverberation of ad-hoc microphones." In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2017. http://dx.doi.org/10.1109/apsipa.2017.8282306.

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Sun, Xuguang, Yi Zhou, and Xiaofeng Shu. "Multi-Channel Linear Prediction Speech Dereverberation Algorithm Based on QR-RLS Adaptive Filter." In the 3rd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3220162.3220176.

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8

Shiota, Satoshi, Longbiao Wang, Kyohei Odani, Atsuhiko Kai, and Weifeng Li. "Distant-talking speech recognition using multi-channel LMS and multiple-step linear prediction." In 2014 9th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2014. http://dx.doi.org/10.1109/iscslp.2014.6936619.

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Dietzen, Thomas, Simon Doclo, Ann Spriet, Wouter Tirry, Marc Moonen, and Toon van Waterschoot. "Low-Complexity Kalman filter for multi-channel linear-prediction-based blind speech dereverberation." In 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2017. http://dx.doi.org/10.1109/waspaa.2017.8170040.

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10

Jukic, Ante, Nasser Mohammadiha, Toon van Waterschoot, Timo Gerkmann, and Simon Doclo. "Multi-channel linear prediction-based speech dereverberation with low-rank power spectrogram approximation." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7177939.

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Звіти організацій з теми "Multi-channel linear prediction"

1

Bell, Gary, David Abraham, Nathan Clifton, and Lamkin Kenneth. Wabash and Ohio River confluence hydraulic and sediment transport model investigation : a report for US Army Corps of Engineers, Louisville District. Engineer Research and Development Center (U.S.), March 2022. http://dx.doi.org/10.21079/11681/43441.

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Анотація:
Avulsions of the Wabash River in 2008 through 2011 at its confluence with the Ohio River resulted in significant shoaling in the Ohio River. This caused a re-alignment of the navigation channel and the need for frequent dredging. A two-dimensional numerical hydrodynamic model, Adaptive Hydraulics (AdH), was developed to simulate base (existing) conditions and then altered to simulate multiple alternative scenarios to address these sediment issues. The study was conducted in two phases, Phase 1 in 2013 – 2015 and Phase 2 in 2018 – 2020. Field data were collected and consisted of multi-beam bathymetric elevations, bed sediment samples, suspended sediment samples, and discharge and velocity measurements. The model hydrodynamic and sediment transport computations adequately replicated the water surface slope, flow splits, bed sediment gradations, and suspended sediment concentrations when compared with field data. Thus, it was shown to be dependable as a predictive tool. The alternative that produced the most desirable results included a combination of three level-crested emergent dikes on Wabash Island and four submerged dikes on the Illinois shore with a level crest from the bank to the tip of the dike. The selected alternative produced an improved sailing line while maintaining authorized channel depths.
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