Commit 30b72820 authored by Adam Mills's avatar Adam Mills
Browse files

Updating the HRTF loader so that it can be used by older version of matlab

parent bbda05b2

scripts/binauralRenderer_interface/matlab_hrir_generation_scripts/generate_HOA_HRIRs_MOD_lens.m

+7 −4
Original line number Diff line number Diff line
@@ -25,7 +25,7 @@ 
%    the United Nations Convention on Contracts on the International Sales of Goods.

% 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function IR_data = generate_HOA_HRIRs_MOD_lens(order, python_path, sofa_path, sofa_file_name, ir_len)

function IR_data = generate_HOA_HRIRs_MOD_lens(order, python_path, sofa_path, sofa_file_name, ir_len, write_out_sofa)

    % HRIR convertor - Takes sphere sampled HRIRs and converts them to

    % HOA HRIRs.

    %
@@ -37,13 +37,14 @@ function IR_data = generate_HOA_HRIRs_MOD_lens(order, python_path, sofa_path, so 
    % converted.

    % sofa_file - file name of the HRTFs to be converted

    % ir_len - length of the IRs to be used.

    % write_out_sofa - boolean to enable/disable writing out of sofa

    %

    % Typical usage:

    %   generate_HOA_HRIRs_MOD_lens(1, "~/.pyenv/versions/3.8.16/bin/python", '~/git/ivas-pc-testfiles/sofa-files/', 'HRIR_128_48000.sofa', 128)

    %



% Pointing to the correct python binary

pyenv(Version=python_path);

pyenv('Version', python_path);



% Load in the support coefs

load('hrtf_support_coefs.mat', 'hrtf_support_coefs');
@@ -120,8 +121,10 @@ IR = permute(IR_HOA, [2, 1, 3]); 
HOAformat_str = ['HOA',num2str(order),'S'];

save(fullfile(erase(sofa_file_name, ".sofa") + "_converted_" +  HOAformat_str + ".mat"), "IR")



if (write_out_sofa == 1)

    H.updateSOFA(IR);

    H.writeSOFA(fullfile(erase(sofa_file_name, ".sofa") + "_converted_" +  HOAformat_str + ".sofa"));

end



IR_data = IR;

scripts/binauralRenderer_interface/matlab_hrir_generation_scripts/hrtf_library_loader.m

+42 −3
Original line number Diff line number Diff line
@@ -80,6 +80,7 @@ classdef hrtf_library_loader < handle 
    end

  

    methods



        function obj = hrtf_library_loader()

        end
@@ -91,7 +92,7 @@ classdef hrtf_library_loader < handle 
            % This looks like a library built from a bunch of discrete

            % locations in free field

            % Let's figure out the source locations used in the HRTF library:

            Pos = double(sofa_file.SourcePosition)';

            Pos = hrtf_library_loader.convert_numpy_2darray(sofa_file.SourcePosition)';

            Units = strtrim(strsplit(string(sofa_file.SourcePosition_Units), ','));

            

            assert( any(strcmpi(Units{1}, {'degree','radian'})), 'Unknown units');
@@ -109,10 +110,10 @@ classdef hrtf_library_loader < handle 
            

            % Now, get the impulse repsonses:

            assert( isprop(sofa_file, 'GLOBAL_DataType'), 'Expected field: GLOBAL_DataType');

            Data.IR = double(sofa_file.Data_IR);

            Data.IR = hrtf_library_loader.convert_numpy_3darray(sofa_file.Data_IR);

            Data.SamplingRate = double(sofa_file.Data_SamplingRate);

            Data.SamplingRate_Units = string(sofa_file.Data_SamplingRate_Units);

            Data.Delay = double(sofa_file.Data_Delay);

            Data.Delay = hrtf_library_loader.convert_numpy_2darray(sofa_file.Data_Delay);

            switch lower(string(sofa_file.GLOBAL_DataType))

                case 'fir'

                    assert( size(Data.IR,2)>=2, 'Expecting 2 receivers (ears)');
@@ -394,6 +395,44 @@ classdef hrtf_library_loader < handle 
    

    methods(Static=true)



        function mat = convert_numpy_2darray(np_array)

            py_list = np_array.tolist();

            % List to cell

            matCell = cell(py_list)';

            shape = cell(np_array.shape);

            shape_mat = zeros(1, length(shape));

            for i=1:length(shape)

                shape_mat(i) = double(shape{i});

            end

            mat = zeros(shape_mat);

            for i = 1:length(matCell)

                if isa(matCell{i}, 'py.list')

                    mat(i,:) = cell2mat(cell(matCell{i}));

                end

            end

        end



        function mat = convert_numpy_3darray(np_array)

            py_list = np_array.tolist();

            % List to cell

            matCell = cell(py_list)';

            shape = cell(np_array.shape);

            shape_mat = zeros(1, length(shape));

            for i=1:length(shape)

                shape_mat(i) = double(shape{i});

            end

            mat = zeros(shape_mat);

            for i = 1:length(matCell)

                if isa(matCell{i}, 'py.list')

                    for j = 1:length(matCell{i})

                        if isa(matCell{i}, 'py.list')

                            mat(i,j,:) = cell2mat(cell(matCell{i}{j}));

                        end

                    end

                end

            end

        end

    

        function [Band2FR,FCs]=MakeBand2FR(FFTLen,FSample)

            % Compute the standard band filters for freq domain representations

            %