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  • Two!Ears documentation
  • First steps
    • Installation guide
    • Modules of the Two!Ears Auditory Model
    • Setting up an acoustic scene
      • Binaural renderer
      • Binaural room scanning renderer
    • Setting up an auditory model
    • Working with the database
    • Use a robotic platform
  • Binaural simulator
    • Usage
      • Configuration
        • Configuration using a Matlab script
        • Configuration using XML Scene Description
      • Simulate Ear-Signals
    • Examples
      • Simulate two dry sources
      • Simulate a moving source
      • Simulate rooms using the Image Source Model
      • Simulate rooms using Binaural Room Impulse Responses
    • Advanced installation
      • Linux/Mac
        • Prerequisites
        • Compile MEX Binaries
      • Windows 7 64bit
        • Prerequisites
        • Compile MEX Binaries
    • Credits
  • Robotic platform
    • Getting started
      • Introduction
        • Component-based software architectures in robotics
        • ROS, a software platform for robotics
        • GenoM3, a tool to develop robotic components
      • Installation of the robotic tools
        • Install ROS
        • Install the GenoM3 tools through robotpkg
        • Install a GenoM3 component from the sources
    • Audio streaming
      • BASS, an audio streaming server component
        • BASS terminology
        • Services
        • Output port
        • Example of use
      • Writing a client of BASS
        • An algorithm for clients of BASS
        • Sample implementation in a GenoM3 component
  • Auditory front-end
    • Overview
      • Framework functionality
      • Getting started
      • Computation of an auditory representation
        • Using default parameters
        • Input/output signals dimensions
        • Change parameters used for computation
        • Compute multiple auditory representations
        • How to plot the result
      • Chunk-based processing
      • Feedback inclusion
        • Placing a new request
        • Modifying a processor parameter
        • Deleting a processor
      • List of commands
        • Signal objects sObj
      • Acknowledgement
    • Technical description
      • Introduction
      • Data handling
        • Circular buffer
        • Signal objects
        • Data objects
      • Processors
        • General considerations
        • processChunk method and chunk-based compatibility
      • Manager
        • Processors and signals instantiation
        • Carrying out the processing
    • Available processors
      • Pre-processing (preProc.m)
        • DC removal filter
        • Pre-emphasis
        • RMS normalisation
        • Level reference and scaling
        • Middle ear filtering
      • Auditory filter bank
        • Gammatone (gammatoneProc.m)
        • Dual-resonance non-linear filter bank (drnlProc.m)
      • Inner hair-cell (ihcProc.m)
      • Adaptation (adaptationProc.m)
      • Auto-correlation (autocorrelationProc.m)
      • Rate-map (ratemapProc.m)
      • Spectral features (spectralFeaturesProc.m)
      • Onset strength (onsetProc.m)
      • Offset strength (offsetProc.m)
      • Binary onset and offset maps (transientMapProc.m)
      • Pitch (pitchProc.m)
      • Medial Olivo-Cochlear (MOC) feedback (mocProc.m)
      • Amplitude modulation spectrogram (modulationProc.m)
      • Spectro-temporal modulation spectrogram
      • Cross-correlation (crosscorrelationProc.m)
      • Interaural time differences (itdProc.m)
      • Interaural level differences (ildProc.m)
      • Interaural coherence (icProc.m)
      • Precedence effect (precedenceProc.m)
    • Add your own processors
      • Check-list for adding a new processor
      • Getting started and setting up processor properties
        • External parameters controllable by the user
        • Internal parameters
      • Implementing static methods
        • getDependency
        • getParameterInfo
        • getProcessorInfo
      • Implementing parameters “getter” methods
      • Implement the processor constructor
      • Preliminary testing
        • Default instantiation
        • Is it a valid processor?
        • Are parameters correctly described?
      • Implementing the core processing method
        • Input and output arguments
        • Chunk-based and signal-based processing
        • Reset method
      • Override parent methods
        • Initialisation methods
        • Input/output routing methods
        • Processing method
      • Allowing alternative processing options
      • Implement a new signal type
      • Recommendations for final testing
    • Credits
  • Blackboard system
    • Introduction
    • Usage
      • Setting up the blackboard
      • Running the blackboard
      • Further examples
    • Blackboard architecture
      • Blackboard system architectural considerations
        • Building a flexible system
        • Building a dynamic system
      • Dynamic system construction
      • Dynamic blackboard memory
      • Dynamic blackboard interactions
      • Dynamic blackboard scheduler
    • Knowledge sources
      • Abstract knowledge source
      • Auditory front-end knowledge source: AuditoryFrontEndKS
      • Auditory signal dependent knowledge source superclass: AuditoryFrontEndDepKS
      • Localisation knowledge sources
        • Location knowledge source: DnnLocationKS
        • Location knowledge source: GmmLocationKS
        • Confusion detection knowledge source: ConfusionKS
        • Confusion solving knowledge source: ConfusionSolvingKS
        • Head rotation knowledge source: RotationKS
      • Identification knowledge sources
        • Identity knowledge source: IdentityKS
        • Identity decision knowledge source: IdDecisionKS
        • Identity Live Debugging knowledge source: IdTruthPlotKS
        • Segment Identity knowledge source: SegmentIdentityKS
      • Sound quality related knowledge sources
        • Coloration knowledge source: ColorationKS
        • Location knowledge source: ItdLocationKS
      • Segmentation knowledge sources
        • Segmentation knowledge source: SegmentationKS
      • Obsolete knowledge sources
        • Acoustic cues knowledge source: AcousticCuesKS
      • Upcoming knowledge sources
        • Number of sources knowledge source: SourceNumberKS
    • Add your own knowledge sources
      • Example of adding a new knowledge source
  • Database
    • Usage
    • Experiment results
      • Human label file format
      • Coloration
        • 2013-05-01: Coloration of a point source in Wave Field Synthesis
        • 2015-10-01: Coloration of a point source in Wave Field Synthesis revisited
      • Localisation
        • 2012-03-01: Localisation of a real vs. binaural simulated point source
        • 2013-11-01: Localisation of different source types in sound field synthesis
      • Quality ratings
        • 2014-04-01: Scene related sound quality
        • 2015-11-01: Listening preference of popular music presented by WFS, surround, and stereo
    • Impulse responses
      • Anechoic measurements (HRTFs)
        • Anechoic HRTFs from the KEMAR manikin with different distances
        • Spherical far-field HRTF compilation of the Neumann KU100
        • MIT HRTF measurements of a KEMAR dummy head
        • Near-field HRTFs from SCUT database of the KEMAR
      • Reverberant measurements (BRIRs)
        • Two!Ears, CNRS Toulouse, Adream-building
        • TU Berlin, Telefunken-building, room Auditorium 3
        • TU Berlin, Telefunken-building, room Spirit
    • Sound databases
      • Speech databases
        • GRID corpus
      • Acoustic scenes and events
        • IEEE AASP Challenge on Detection and Classification
    • Stimuli
  • Examples
    • Localisation with and without head rotations
    • Localisation - looking at the results in detail
    • DNN-based localisation under reverberant conditions
    • GMM-based localisation under reverberant conditions
    • Train sound type identification models
      • Example step-through
        • Start-up
        • Feature and model creators
        • Training and testing sets
        • Scene configuration
        • Running the pipeline
    • Identification of sound types
      • Example step-through
        • Specifying the identification models
        • Starting Two!Ears
        • Creating a test scene
        • Initialising the Binaural Simulator
        • Building the example Blackboard System
        • Running the simulation
        • Evaluating the simulation
    • Segmentation with and without priming
    • (Re)train the segmentation stage
    • Stream binaural signals from BASS to Matlab
      • Preliminary steps
      • Control BASS to start an acquisition
        • Connect to genomix and load BASS
        • Get the name of your sound interface
        • Start an acquisition
      • Get audio data in Matlab
      • End the session
    • Prediction of coloration in spatial audio systems
      • Getting listening test data
      • Setting up the Binaural Simulator
      • Estimating the coloration with the Blackboard
      • Verify the results
    • Prediction of localisation in spatial audio systems
      • Getting the listening test data
      • Setting up the Binaural Simulator
      • Estimating the localisation with the Blackboard
      • Verify the results
  • Development of Two!Ears
    • Installation of the development version
      • Getting the code
        • Working on the whole Two!Ears model
        • Working on a single module
      • Setting up dependencies on particular branches
      • Adding your changes
    • Development using git
      • Git for beginners
        • Getting a remote repository to your computer
        • Adding/changing files
        • Staying up to date with the remote repository
        • Getting further help
        • Developing and branching
        • Remote branches
      • Git advanced commands
        • Storing credentials
        • Working together with a svn repository
        • Removing commits with large files
        • Split repository
      • Git under Windows
      • Git with large binary files
    • Matlab coding style guide
      • Introduction
      • Documentation and comments
        • Class headers
        • Function headers
        • Comments
        • License
        • Author
        • Versioning
      • Naming Conventions
        • General
        • Variables
        • Constants
        • Functions
        • Classes/Objects
      • Layout
        • Code Indention
        • White Spaces
        • Line Width
        • Line Breaks
      • Credits
    • Writing documentation
      • Getting the raw documentation
      • Getting started with Sphinx
      • Converting existing documentation
      • Commonly used terms
      • Guidelines for reStructuredText files
        • Add a figure
        • Add a table
        • Dealing with referencing throughout the document
        • Using acronyms
      • Documenting new features
 
The Two!Ears Auditory Model
  • Docs »
  • Examples
  • Edit on GitHub

ExamplesΒΆ

In this part you find a collection of several examples using the complete Two!Ears Auditory Model for common tasks. At the moment the following examples are available:

  • Localisation with and without head rotations
  • Localisation - looking at the results in detail
  • DNN-based localisation under reverberant conditions
  • GMM-based localisation under reverberant conditions
  • Train sound type identification models
  • Identification of sound types
  • Segmentation with and without priming
  • (Re)train the segmentation stage
  • Stream binaural signals from BASS to Matlab
  • Prediction of coloration in spatial audio systems
  • Prediction of localisation in spatial audio systems

This section will expand with every new major feature that will be added to the model.

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© Copyright 2015-2016, Two!Ears Team. Revision 6f3cd8c3.