diff --git a/examples/beamforming/mask_prediction/local/Build_MaskBFnet_CE.m b/examples/beamforming/mask_prediction/local/Build_MaskBFnet_CE.m
index d1b63d3..0b17862 100644
--- a/examples/beamforming/mask_prediction/local/Build_MaskBFnet_CE.m
+++ b/examples/beamforming/mask_prediction/local/Build_MaskBFnet_CE.m
@@ -11,7 +11,7 @@
 layer = genNetworkMaskBF_CE(para.topology);     % generate the network graph
 para.preprocessing{1} = {};                     % optional preprocessing for each data stream
 para.preprocessing{2} = {};
-if para.topology.MTL
+if isfield(para.topology, 'MTL') && para.topology.MTL
     para.cost_func.layer_idx = [ReturnLayerIdxByName(layer, 'cross_entropy') length(layer)];       % specify which layers are cost function layers
     para.cost_func.layer_weight = [1 para.topology.MTL];              % set the weights of each cost function layer
     para.preprocessing{3} = {};
diff --git a/utils/Reader_waveform.m b/utils/Reader_waveform.m
index 06c87dd..a73ed4f 100644
--- a/utils/Reader_waveform.m
+++ b/utils/Reader_waveform.m
@@ -22,14 +22,14 @@
         % read in multichannel waveforms
         if isfield(reader, 'multiArrayFiles') && reader.multiArrayFiles     % when the different channels are stored in different files
             for j=1:length(files{i})
-                [tmp_wav, fs] = Reader_waveform_core(files{i}{j}, reader.fs);
+                [tmp_wav, fs] = Reader_waveform_core(files{i}{j}, reader);
                 if j==1
                     wav = zeros(length(files{i}), length(tmp_wav));
                 end
                 wav(j,:) = tmp_wav;
             end
         else                                                                % when the different channels are stored in the same file
-            [wav, fs] = Reader_waveform_core(files{i}, reader.fs);
+            [wav, fs] = Reader_waveform_core(files{i}, reader);
         end
         % optional selection of channels
         if isfield(reader, 'useChannel')