Multiple signal classification

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MUltiple SIgnal Classification (MUSIC) is an algorithm used for frequency estimation and emitter location. (Wikipedia.org)






Conferences related to Multiple signal classification

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2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

The 23rd MLSP workshop in the series organized by the Signal Processing Society MLSP Technical Committee will present the most recent and exciting advances in data analysis for signal processing problems. The MLSP workshop is to bring together the researchers from all over the world to exchange ideas and share the most recent research achievements in signal processing developments for machine learning and cognition systems through plenary talks, tutorials as well as special and regular single-track sessions.


2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

The 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA’13) will be held at the Mohonk Mountain House in New Paltz, New York, and is sponsored by the Audio and Acoustic Signal Processing committee of the IEEE Signal Processing Society. The objective of this workshop is to provide an informal environment for the discussion of problems in audio and acoustics and signal processing techniques leading to novel solutions.


2013 International Conference onWireless Communications and Signal Processing (WCSP)

WCSP aims to gather International researchers from academia and industry to meet and exchange ideas and recent research works across the broad field of wireless technologies. Potential topics are solicited in, but not limited to the following areas: 1. Communication Theory 2. Wireless Communications 3. Wireless Networking 4. Signal Processing

  • 2012 International Conference on Wireless Communications & Signal Processing (WCSP 2012)

    * Communications Theory * Wireless Communications * Wireless Networking * Signal Processing

  • 2011 International Conference on Wireless Communications & Signal Processing (WCSP 2011)

    The Conference is the premier forum for the presentation of new advances and research results in the fields of theoretical, experimental, and applied Wireless Communications and Signal Processing. The conference aims to bring together leading researchers, scientists, engineers and scholar students in the domain of interest from around the world to exchange and share their experiences, new ideas and results in challenges encountered and solutions adopted.

  • 2010 International Conference on Wireless Communications & Signal Processing (WCSP 2010)

    WCSP 2010 is the premier forum for the presentation of new advances and research results in the fields of theoretical, experimental, and applied Wireless Communications and Signal Processing aims to exchange and share the experiences, new ideas and results in challenges encountered and solutions adopted.

  • 2009 International Conference on Wireless Communications & Signal Processing (WCSP 2009)

    WCSP 2009 is the first international conference on Wireless Communications and Signal Processing. It is planning to be held in Nanjing, China. Nanjing played an important role in these areas. There are a lot of IEEE members, wireless and mobile clients, services providers and producers in Nanjing.


2012 20th Signal Processing and Communications Applications Conference (SIU)

Conference will discuss state of the art solutions and research results on existing and future DSP and telecommunication systems, applications, and related standardization activities. Conference will also include invited lectures, tutorials and special sessions.

  • 2011 19th Signal Processing and Communications Applications Conference (SIU)

    Conference will bring together academia and industry professionals as well as students and researchers to present and discuss state of the art solutions and research results on existing and future DSP and telecommunication systems, applications, and related standardization activities. The Conference will also include invited lectures, tutorials and special sessions.

  • 2010 IEEE 18th Signal Processing and Communications Applications Conference (SIU)

    S1.Theory of Signal-Processing S2.Statistical Signal-Processing S3.Multimedia Signal-Processing S4.Biomedical Signal-Processing S5.Sensor Networks S6.Multirate Signal-Processing S7.Pattern Recognition S8.Computer Vision S9.Adaptive Filters S10.Image/Video/Speech Browsing, Retrieval S11.Speech/Audio Coding S12.Speech Processing S13.Human-Machine Interfaces S14.Surveillance Signal Processing S15.Bioinformatics S16.Self-Learning S17.Signal-Processing Education S18.Signal-Processing Systems S1

  • 2009 IEEE 17th Signal Processing and Communications Applications Conference (SIU)

    The scope of the conference is to cover recent topics in theory and applications of Signal Processing and Communications.

  • 2008 IEEE 16th Signal Processing and Communications Applications Conference (SIU)

    Signal Processing, Image Processing, Speech Processing, Pattern Recognition, Human Computer Interaction, Communication, Video and Speech indexing, Computer Vision, Biomedical Signal Processing

  • 2007 IEEE 15th Signal Processing and Communications Applications (SIU)


2011 IEEE 3rd International Conference on Signal Processing Systems (ICSPS)

ICSPS is one of the leading international conferences for presenting novel and fundamental advances in the fields of Signal Processing Systems. It also serves to foster communication among researchers and practitioners working in a wide variety of scientific areas with a common interest in improving Signal Processing Systems related techniques.


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Periodicals related to Multiple signal classification

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Antennas and Wireless Propagation Letters, IEEE

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.


Biomedical Engineering, IEEE Transactions on

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.


Signal Processing, IEEE Transactions on

The technology of transmission, recording, reproduction, processing, and measurement of speech; other audio-frequency waves and other signals by digital, electronic, electrical, acoustic, mechanical, and optical means; the components and systems to accomplish these and related aims; and the environmental, psychological, and physiological factors of thesetechnologies.




Xplore Articles related to Multiple signal classification

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Maximum likelihood velocity estimation of multiple seismic wavefronts

Liu, H.; Fu Li Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on, 1994

Presents a new approach to simultaneously estimate stacking velocity and zero- offset time of seismic wave propagation, which is specially designed for multiple seismic wavefronts while the traditional semblance approach and the subspace approach are not. Simulations show a good performance of the new approach


Glrt-Based Outlier Prediction and Cure in Under-Sampled Training Conditions using a Singular Likelihood Ratio

Johnson, B.A.; Abramovich, Y.I. Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, 2007

For cases where the number of training samples T does not exceed the number of antenna elements M, we consider a detection-estimation problem for Gaussian sources occupying a low-rank m-dimensioned signal subspace within the associated covariance matrix (m < T < M). We derive a likelihood ratio that for the null hypothesis is described by a probability function that does ...


The signal subspace decomposition method for extracting harmonic signal

Dengwei Wang; Yinghua Lu Information Theory Workshop, 2006. ITW '06 Punta del Este. IEEE, 2006

The Subspace Decomposition Method for extracting harmonic character is discussed. The theory of the crosscorrelation matrix based MUSIC method in Gaussian white noise is analysised. A method of subspace decomposition is proposed for the purpose of suppressing noise interference and increasing the ability of recognizing target and multiple signal classification, an algorithm of harmonic retrieval, was introduced to extract harmonic ...


Beamspace Root-MUSIC for minimum redundancy linear arrays

Zoltowski, M.D.; Silverstein, S.D.; Mathews, Cherian P. Signal Processing, IEEE Transactions on, 1993

Beamspace Root-MUSIC, a computationally efficient beamspace implementation of Root-MUSIC developed recently for use in conjunction with a uniformly spaced linear array (ULA), is discussed. Computationally efficient methods for using beamspace Root-MUSIC in conjunction with a minimum redundancy linear array (MRLA) for both the narrowband and wideband cases are developed. The MRLA is attractive in that it offers enhanced detection performance ...


Comparing textural features for music genre classification

Costa, Y.M.G.; Oliveira, L.; Koerich, A.L.; Gouyon, F. Neural Networks (IJCNN), The 2012 International Joint Conference on, 2012

In this paper we compare two different textural feature sets for automatic music genre classification. The idea is to convert the audio signal into spectrograms and then extract features from this visual representation. Two textural descriptors are explored in this work: the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Patterns (LBP). Besides, two different strategies of extracting features are ...


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Educational Resources on Multiple signal classification

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Maximum likelihood velocity estimation of multiple seismic wavefronts

Liu, H.; Fu Li Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on, 1994

Presents a new approach to simultaneously estimate stacking velocity and zero- offset time of seismic wave propagation, which is specially designed for multiple seismic wavefronts while the traditional semblance approach and the subspace approach are not. Simulations show a good performance of the new approach


Glrt-Based Outlier Prediction and Cure in Under-Sampled Training Conditions using a Singular Likelihood Ratio

Johnson, B.A.; Abramovich, Y.I. Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, 2007

For cases where the number of training samples T does not exceed the number of antenna elements M, we consider a detection-estimation problem for Gaussian sources occupying a low-rank m-dimensioned signal subspace within the associated covariance matrix (m < T < M). We derive a likelihood ratio that for the null hypothesis is described by a probability function that does ...


The signal subspace decomposition method for extracting harmonic signal

Dengwei Wang; Yinghua Lu Information Theory Workshop, 2006. ITW '06 Punta del Este. IEEE, 2006

The Subspace Decomposition Method for extracting harmonic character is discussed. The theory of the crosscorrelation matrix based MUSIC method in Gaussian white noise is analysised. A method of subspace decomposition is proposed for the purpose of suppressing noise interference and increasing the ability of recognizing target and multiple signal classification, an algorithm of harmonic retrieval, was introduced to extract harmonic ...


Beamspace Root-MUSIC for minimum redundancy linear arrays

Zoltowski, M.D.; Silverstein, S.D.; Mathews, Cherian P. Signal Processing, IEEE Transactions on, 1993

Beamspace Root-MUSIC, a computationally efficient beamspace implementation of Root-MUSIC developed recently for use in conjunction with a uniformly spaced linear array (ULA), is discussed. Computationally efficient methods for using beamspace Root-MUSIC in conjunction with a minimum redundancy linear array (MRLA) for both the narrowband and wideband cases are developed. The MRLA is attractive in that it offers enhanced detection performance ...


Comparing textural features for music genre classification

Costa, Y.M.G.; Oliveira, L.; Koerich, A.L.; Gouyon, F. Neural Networks (IJCNN), The 2012 International Joint Conference on, 2012

In this paper we compare two different textural feature sets for automatic music genre classification. The idea is to convert the audio signal into spectrograms and then extract features from this visual representation. Two textural descriptors are explored in this work: the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Patterns (LBP). Besides, two different strategies of extracting features are ...


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