supdeq_dec

% SUpDEq - Spatial Upsampling by Directional Equalization

 function sparseHRTFdataset_dec = supdeq_dec(sparseHRTFdataset, eqDataset, targetDistance)

 This function performs a distance error compensation (dec) of a (sparse)
 HRTF set. It determines the onsets of the equalized datasets and 
 adjusts/shifts the (sparse) dataset baset on these onsets.

 Output:
 sparseHRTFdataset_dec - Sparse HRTF dataset with shifted initial delays

 Input:        
 sparseHRTFdataset     - Struct with sparse HRTF dataset
 eqDataset:            - Struct with equalization dataset
                         (supdeq_getEqDataset)
 targetDistance:       - Correct distance to target in m
                         Default: 2.00m

 Dependencies: AKTools

 Reference:
 C. Pörschmann and J. M. Arend, ?How positioning inaccuracies influence 
 the spatial upsampling of sparse head-related transfer function sets,? 
 in Proceedings of the International Conference on Spatial Audio - ICSA 2019
   
 (C) 2019 by CP,  Christoph Pörschmann
             JMA, Johannes M. Arend  
             TH Köln - University of Applied Sciences
             Institute of Communications Engineering
             Department of Acoustics and Audio Signal Processing

0001 %% SUpDEq - Spatial Upsampling by Directional Equalization
0002 %
0003 % function sparseHRTFdataset_dec = supdeq_dec(sparseHRTFdataset, eqDataset, targetDistance)
0004 %
0005 % This function performs a distance error compensation (dec) of a (sparse)
0006 % HRTF set. It determines the onsets of the equalized datasets and
0007 % adjusts/shifts the (sparse) dataset baset on these onsets.
0008 %
0009 % Output:
0010 % sparseHRTFdataset_dec - Sparse HRTF dataset with shifted initial delays
0011 %
0012 % Input:
0013 % sparseHRTFdataset     - Struct with sparse HRTF dataset
0014 % eqDataset:            - Struct with equalization dataset
0015 %                         (supdeq_getEqDataset)
0016 % targetDistance:       - Correct distance to target in m
0017 %                         Default: 2.00m
0018 %
0019 % Dependencies: AKTools
0020 %
0021 % Reference:
0022 % C. Pörschmann and J. M. Arend, ?How positioning inaccuracies influence
0023 % the spatial upsampling of sparse head-related transfer function sets,?
0024 % in Proceedings of the International Conference on Spatial Audio - ICSA 2019
0025 %
0026 % (C) 2019 by CP,  Christoph Pörschmann
0027 %             JMA, Johannes M. Arend
0028 %             TH Köln - University of Applied Sciences
0029 %             Institute of Communications Engineering
0030 %             Department of Acoustics and Audio Signal Processing
0031 
0032 function sparseHRTFdataset_dec = supdeq_dec(sparseHRTFdataset, eqDataset, targetDistance)
0033 
0034 if nargin < 3 || isempty(targetDistance)
0035     targetDistance = 2;
0036 end
0037 
0038 %Get data from eqDataset
0039 c = eqDataset.c;
0040 r = eqDataset.radius;
0041 
0042 %Get data from sparseHRTFdataset and perform equalization
0043 fs = sparseHRTFdataset.f(end)*2;
0044 sparseSamplingGrid = sparseHRTFdataset.samplingGrid;
0045 Nsparse = sparseHRTFdataset.Nmax;
0046 eqHRTFdataset = supdeq_eq(sparseHRTFdataset,eqDataset,Nsparse,sparseSamplingGrid,false);
0047 
0048 % Determine onsets
0049 for kk = 1:length(sparseHRTFdataset.samplingGrid(:,1)) 
0050     %Get HRIRs
0051     eqHRIR_L=ifft([eqHRTFdataset.HRTF_L(kk,:) zeros(1,length(eqHRTFdataset.HRTF_L(kk,:))-2)],'symmetric');
0052     eqHRIR_R=ifft([eqHRTFdataset.HRTF_R(kk,:) zeros(1,length(eqHRTFdataset.HRTF_R(kk,:))-2)],'symmetric');
0053     %Determine onsets with 10 times oversampling and write in sparseHRTFdataset struct
0054     sparseHRTFdataset.onset(kk,1) =  AKonsetDetect(eqHRIR_L',10,-1,'rel',[4000 fs]);
0055     sparseHRTFdataset.onset(kk,2) =  AKonsetDetect(eqHRIR_R',10,-1,'rel',[4000 fs]);
0056 end
0057 %Get mean onset
0058 meanOnset = mean(mean(sparseHRTFdataset.onset));
0059 
0060 %"Normalize" onsets by substracting mean and transform to seconds
0061 sparseHRTFdataset.onset(:,1)=(sparseHRTFdataset.onset(:,1)-meanOnset)/fs;
0062 sparseHRTFdataset.onset(:,2)=(sparseHRTFdataset.onset(:,2)-meanOnset)/fs;
0063 
0064 for kk = 1:length(sparseHRTFdataset.samplingGrid(:,1)) 
0065     %Get "startingDistance" by adding (potential) offset/distance error to
0066     %target distance
0067     startingDistance(kk,:) = targetDistance+sparseHRTFdataset.onset(kk,:)*c;
0068     %Get targetDirection from respective sampling grid
0069     targetDirection = sparseSamplingGrid(kk,:);
0070     %Shift distance / compensate for distance errors
0071     %Use mean of left/right ear starting distance as staring distance for shift
0072     [sparseHRTFdataset.HRTF_L(kk,:),sparseHRTFdataset.HRTF_R(kk,:)] = supdeq_shiftDistance(sparseHRTFdataset.HRTF_L(kk,:),sparseHRTFdataset.HRTF_R(kk,:),mean(startingDistance(kk,:)),targetDistance,targetDirection,r,fs,c);
0073 end
0074 
0075 %Save in new variable and add starting/target distance info
0076 sparseHRTFdataset_dec = sparseHRTFdataset;
0077 sparseHRTFdataset_dec.decStartingDistance = startingDistance;
0078 sparseHRTFdataset_dec.decTargetDistance = targetDistance;
0079 fprintf('Performed distance error compensation to target distance of %d m\n',targetDistance);
0080     
0081 end
0082