Implement static methods

Static methods are methods that can be called without an existing instance of an object. In the implementation of processors, they are used to store all the hard-coded information. This can be for example the processor name, the type of signal it accepts as input, or the names and default values of its external parameters. A static method is implemented by defining it in a method block with the (Static) method attribute:

classdef myNewProcessor < Processor

  % ... Properties and other methods definition

  methods (Static)

    function out = myStaticMethod_1(in)

    function out = myStaticMethod_2(in)



Static methods share the same structure and names across processors, so they can easily be copy/pasted from an existing processor and then modified to reflect your new processor. The following three methods have to be implemented.

  • .getDependency(): Returns the type of input signal by its user request name
  • .getParameterInfo(): Returns names, default values, and descriptions of external parameters
  • .getProcessorInfo(): Returns information about the processor as a Matlab structure

As they are used to hard-code and return information, none of these methods accept input arguments.


This method returns the type of input signal your processor should accept:

function name = getDependency()
  name = 'requestNameOfInputSignal';

where 'requestNameOfInputSignal' is the request name of the signal that should be used as input. “Request name” corresponds to the request a user would place in order to obtain a particular signal. For example, the inner hair-cell envelope processor requires as input the output of e.g., a gammatone filterbank. The request name for this signal is 'filterbank' which should therefore be the output of the static method ihcProc.getDependency(). You can also check the list of currently valid request names by typing requestList in Matlab’s command window.

If you are unsure about which name should be used, consider which processor would come directly before your new processor in a processing chain (i.e., the processor your new processor depends on). Say it is named dependentProc. Then typing:


in Matlab’s command window will return the corresponding request name you should output in your getDependency method.


This method hard-codes all information regarding the (external) parameters used by your processor, i.e., lists of their names, default values, and description. These are used to populate the output of the helper script parameterHelper and to give a default value to parameters when your processor is instantiated.

The lists are returned as cell arrays of strings (or any other type for the default parameter values). They should follow the same order, such that the n-th member of each of the three lists relate to the same parameter.

Parameter names need not be the same as the parameter property name you defined earlier. This will become apparent in the next section. In fact, names should be changed to at least include a two or three letters prefix that is unique to your new processor. You can make sure it is not already in use by browsing through the output of the parameterHelper script.

The method should look something like this:

function [names,defValues,description] = getParameterInfo()

  names = {'xx_par1','xx_par2','xx_par3'};

  defValues = {0.5, ...
               [1 2 3 4], ...

  description = {'Tuning factor of dummy example (s)',...
                 'Vector of unused frequencies (Hz)',...
                 'Model name (''someStringValue'' or ''anotherValue'')'}


This dummy example illustrates the following important points:

  • Use a unique prefix in the name of the parameters (xx_ above) that abbreviates the name or task of the processor.
  • Find a short, but self-explanatory parameter name (not like parX above). If it makes sense, you can re-use the same name as a parameter involved in another processor. The prefix will make the name unique.
  • Default values can be of any type (e.g., float number, array, strings,…)
  • Descriptions should be as short as possible while still explanatory. Mention if applicable the units or the different alternatives.


This method stores the properties of the processor that are needed to integrate it in the framework. It outputs a structure with the following fields:

  • .name: A short, self-explanatory name for the processor
  • .label: A name for the processor that is used as a label. It can the same as .name if that is sufficient, or a bit longer if needed.
  • .requestName: The name tag of the request that a user should input when calling the .addProcessor method of the manager. This has to be a valid Matlab name (e.g., it cannot include spaces).
  • .requestLabel: A longer name for the signal this processor produces, used e.g., as plot labels.
  • outputType: The type of signal object (name of the class) this processor produces. If none of the existing signals in the framework are suitable, you will need to implement a new one.
  • isBinaural: Set to 0 if your processor operates on a single channel (e.g., an auditory filterbank) or to 1 if it needs a binaural input (e.g., the inter-aural level differences processor). If your processor can operate on both mono and stereo signals (such as the pre-processor preProc.m), set it to 2.

Your method should initialise the structure that will be returned as output and give a value to all of the above-mentioned fields:


function pInfo = getProcessorInfo

  pInfo = struct; = 'MyProcessor';
  pInfo.label = 'Processor doing things';
  % etc...