The Experts below are selected from a list of 1314 Experts worldwide ranked by ideXlab platform

John F Culling - One of the best experts on this subject based on the ideXlab platform.

  • Signal Processing Software for teaching and research in psychoacoustics under unix and x windows
    Behavior Research Methods Instruments & Computers, 1996
    Co-Authors: John F Culling
    Abstract:

    A package of Software is described that generates, analyzes, stores, and displays sampled waveforms. The package is designed for use under UNIX and includes C source code, UNIX manual pages, and tutorial documents. The programs interact via UNIX pipes using an ASCII-text data format, which enables the user to view the data in numerical form as well as through the use of plotting programs. Among many other functions, the programs can do the following: efficiently generate linearphase FIR filters with arbitrary transfer functions; generate impulse responses for rectangular rooms of specified dimensions; convolve waveforms with each other; perform Fourier transformation and inverse Fourier transformation; filter waveforms in the Fourier domain; filter waveforms according to the peripheral frequency selectivity of the human auditory system; cross-correlate waveforms; autocorrelate waveforms; synthesize complex waveforms, including vowel sounds and white noise. The Software can read and write a variety of commonly used waveform file formats. The data can be plotted on an X-Window display using thegnuplot Software, which has been included in the package. The complete Software package is available via anonymous ftp from ftp.ihr.mrc.ac.uk in ~ftp/pub/johncu/wave.tar.Z

Shannon Rankin - One of the best experts on this subject based on the ideXlab platform.

  • Integration of real‐time odontocete call classification algorithm into PAMGUARD Signal Processing Software.
    Journal of the Acoustical Society of America, 2011
    Co-Authors: Michael Oswald, Shannon Rankin, Marc O Lammers, Julie N Oswald, Whitlow W. L. Au
    Abstract:

    Real‐time odontocete call classification algorithm (ROCCA) is a tool for real‐time acoustic species identification of delphinid whistles. Introduced in 2006 as MATLAB‐based Software, ROCCA is currently being incorporated into PAMGUARD, a freely‐available, open source Software package. ROCCA provides automated extraction of whistle contours from a spectrogram. It measures 54 whistle contour features including frequencies, slopes, duration, and variables related to the positions of inflection points and steps. ROCCA currently classifies whistles of seven species and one genus: Globicephala macrorhynchus, Pseudorca crassidens, Steno bredanensis, Stenella attenuata, S. coeruleoalba, S. longirostris, Tursiops truncatus, and Delphinus species. The classifier is a Random Forest trained on 2231 whistles collected over six cruises and 7 years in the eastern tropical Pacific Ocean. The original ROCCA classifier used a combination of discriminant function analysis and CART algorithms on 13 whistle contour features f...

  • Integration of real‐time odontocete call classification algorithm into PAMGUARD Signal Processing Software.
    The Journal of the Acoustical Society of America, 2011
    Co-Authors: Michael Oswald, Marc O Lammers, Julie N Oswald, Shannon Rankin
    Abstract:

    Real‐time odontocete call classification algorithm (ROCCA) is a tool for real‐time acoustic species identification of delphinid whistles. Introduced in 2006 as MATLAB‐based Software, ROCCA is currently being incorporated into PAMGUARD, a freely‐available, open source Software package. ROCCA provides automated extraction of whistle contours from a spectrogram. It measures 54 whistle contour features including frequencies, slopes, duration, and variables related to the positions of inflection points and steps. ROCCA currently classifies whistles of seven species and one genus: Globicephala macrorhynchus, Pseudorca crassidens, Steno bredanensis, Stenella attenuata, S. coeruleoalba, S. longirostris, Tursiops truncatus, and Delphinus species. The classifier is a Random Forest trained on 2231 whistles collected over six cruises and 7 years in the eastern tropical Pacific Ocean. The original ROCCA classifier used a combination of discriminant function analysis and CART algorithms on 13 whistle contour features for an overall correct classification score of 35%, which was significantly greater than random (12%). The current Random Forest scheme, trained on 54 whistle contour features, yields an overall correct classification score of 62%. Feedback from at‐sea beta testing has been incorporated into the latest version of ROCCA. Additional species, automated detection, and alternate classification schemes are being explored to extend ROCCA into different geographic areas with greater accuracy.

Michael Oswald - One of the best experts on this subject based on the ideXlab platform.

  • Integration of real‐time odontocete call classification algorithm into PAMGUARD Signal Processing Software.
    Journal of the Acoustical Society of America, 2011
    Co-Authors: Michael Oswald, Shannon Rankin, Marc O Lammers, Julie N Oswald, Whitlow W. L. Au
    Abstract:

    Real‐time odontocete call classification algorithm (ROCCA) is a tool for real‐time acoustic species identification of delphinid whistles. Introduced in 2006 as MATLAB‐based Software, ROCCA is currently being incorporated into PAMGUARD, a freely‐available, open source Software package. ROCCA provides automated extraction of whistle contours from a spectrogram. It measures 54 whistle contour features including frequencies, slopes, duration, and variables related to the positions of inflection points and steps. ROCCA currently classifies whistles of seven species and one genus: Globicephala macrorhynchus, Pseudorca crassidens, Steno bredanensis, Stenella attenuata, S. coeruleoalba, S. longirostris, Tursiops truncatus, and Delphinus species. The classifier is a Random Forest trained on 2231 whistles collected over six cruises and 7 years in the eastern tropical Pacific Ocean. The original ROCCA classifier used a combination of discriminant function analysis and CART algorithms on 13 whistle contour features f...

  • Integration of real‐time odontocete call classification algorithm into PAMGUARD Signal Processing Software.
    The Journal of the Acoustical Society of America, 2011
    Co-Authors: Michael Oswald, Marc O Lammers, Julie N Oswald, Shannon Rankin
    Abstract:

    Real‐time odontocete call classification algorithm (ROCCA) is a tool for real‐time acoustic species identification of delphinid whistles. Introduced in 2006 as MATLAB‐based Software, ROCCA is currently being incorporated into PAMGUARD, a freely‐available, open source Software package. ROCCA provides automated extraction of whistle contours from a spectrogram. It measures 54 whistle contour features including frequencies, slopes, duration, and variables related to the positions of inflection points and steps. ROCCA currently classifies whistles of seven species and one genus: Globicephala macrorhynchus, Pseudorca crassidens, Steno bredanensis, Stenella attenuata, S. coeruleoalba, S. longirostris, Tursiops truncatus, and Delphinus species. The classifier is a Random Forest trained on 2231 whistles collected over six cruises and 7 years in the eastern tropical Pacific Ocean. The original ROCCA classifier used a combination of discriminant function analysis and CART algorithms on 13 whistle contour features for an overall correct classification score of 35%, which was significantly greater than random (12%). The current Random Forest scheme, trained on 54 whistle contour features, yields an overall correct classification score of 62%. Feedback from at‐sea beta testing has been incorporated into the latest version of ROCCA. Additional species, automated detection, and alternate classification schemes are being explored to extend ROCCA into different geographic areas with greater accuracy.

Mohammad Fathi Al-sa'd - One of the best experts on this subject based on the ideXlab platform.

  • Multisensor Time-Frequency Signal Processing Software Matlab Package: An analysis tool for multichannel non-stationary data
    SoftwareX, 2018
    Co-Authors: Boualem Boashash, Abdeldjalil Aissa El Bey, Mohammad Fathi Al-sa'd
    Abstract:

    The Multisensor Time-Frequency Signal Processing (MTFSP) Matlab package is an analysis tool for multichannel non-stationary Signals collected from an array of sensors. By combining array Signal Processing for non-stationary Signals and multichannel high resolution time-frequency methods, MTFSP enables applications such as cross-channel causality relationships, automated component separation and direction of arrival estimation, using multisensor time-frequency distributions (MTFDs). MTFSP can address old and new applications such as: abnormality detection in biomedical Signals, source localization in wireless communications or condition monitoring and fault detection in industrial plants. It allows e.g. the reproduction of the results presented in [3].

Whitlow W. L. Au - One of the best experts on this subject based on the ideXlab platform.

  • Integration of real‐time odontocete call classification algorithm into PAMGUARD Signal Processing Software.
    Journal of the Acoustical Society of America, 2011
    Co-Authors: Michael Oswald, Shannon Rankin, Marc O Lammers, Julie N Oswald, Whitlow W. L. Au
    Abstract:

    Real‐time odontocete call classification algorithm (ROCCA) is a tool for real‐time acoustic species identification of delphinid whistles. Introduced in 2006 as MATLAB‐based Software, ROCCA is currently being incorporated into PAMGUARD, a freely‐available, open source Software package. ROCCA provides automated extraction of whistle contours from a spectrogram. It measures 54 whistle contour features including frequencies, slopes, duration, and variables related to the positions of inflection points and steps. ROCCA currently classifies whistles of seven species and one genus: Globicephala macrorhynchus, Pseudorca crassidens, Steno bredanensis, Stenella attenuata, S. coeruleoalba, S. longirostris, Tursiops truncatus, and Delphinus species. The classifier is a Random Forest trained on 2231 whistles collected over six cruises and 7 years in the eastern tropical Pacific Ocean. The original ROCCA classifier used a combination of discriminant function analysis and CART algorithms on 13 whistle contour features f...