reformatting

README.md

-### The intrusive binaural audio quality model BAM-Q (Fleßner et al., 2017) requires the clean 
-### and the distorted signal as input to estimate perceived binaural quality differences.
-### Stereo signals ([left channel right channel]) are required as model input. A stimuli level of 
-### 0 dB FS corresponds to a sound pressure level of 115 dB SPL.
-### Clean and distorted signals have to be the same length and temporal aligned. The input signals are required to
-### have a duration of at least 0.4 seconds.
+The intrusive binaural audio quality model BAM-Q (Fleßner et al., 2017) requires the clean 
+and the distorted signal as input to estimate perceived binaural quality differences.
+Stereo signals ([left channel right channel]) are required as model input. A stimuli level of 
+0 dB FS corresponds to a sound pressure level of 115 dB SPL.
+Clean and distorted signals have to be the same length and temporal aligned. The input signals are required to
+have a duration of at least 0.4 seconds.
 
 
-### 'example_BAMQ.m' gives a minimal example how to use BAM-Q in Matlab. 
+'example_BAMQ.m' gives a minimal example how to use BAM-Q in Matlab. 
 
-### The BAM-Q output provides four submeasures:
-### binQ:          binaural quality measure; based on a combination of of the submeasures that represent differences 
-###                between the reference and the test signal for interaural level differences (ILDdiff), 
-###                interaural time/phase differences (ITDdiff) and the interaural vector strength ('IVSdiff').
-###                100 ... no difference
-###                0   ... large difference
-###                -X  ... even larger difference
-### ILDdiff         ... intermediate ILD measure
-### ITDdiff         ... intermediate ITD measure (can be 0 if ITDs are not evaluable)
-### IVSdiff         ... intermediate IVS measure
+The BAM-Q output provides four submeasures:
+binQ:          binaural quality measure; based on a combination of of the submeasures that represent differences 
+                between the reference and the test signal for interaural level differences (ILDdiff), 
+                interaural time/phase differences (ITDdiff) and the interaural vector strength ('IVSdiff').
+                100 ... no difference
+               0   ... large difference
+                -X  ... even larger difference
+ILDdiff         ... intermediate ILD measure
+ITDdiff         ... intermediate ITD measure (can be 0 if ITDs are not evaluable)
+IVSdiff         ... intermediate IVS measure
 
 
-## A more detailed description of BAM-Q is given in:
+A more detailed description of BAM-Q is given in:
 
-### J.-H. Fleßner, R. Huber, and S. D. Ewert, "Assessment and Prediction of Binaural Aspects of Audio Quality",
-### Journal of the Audio Engineering Society, vol. 65, no.11, PP.929-942. 2017. https://doi.org/10.17743/jaes.2017.0037
+J.-H. Fleßner, R. Huber, and S. D. Ewert, "Assessment and Prediction of Binaural Aspects of Audio Quality",
+Journal of the Audio Engineering Society, vol. 65, no.11, PP.929-942. 2017. https://doi.org/10.17743/jaes.2017.0037
 
-## Abstract:
-### Binaural or spatial presentation of audio signals has become increasingly important in
-### consumer sound reproduction, but also for hearing assistive devices like hearing aids, where
-### signals in both ears might undergo heavy signal processing. Such processing might introduce
-### distortions to the interaural signal properties that affect perception. Here, an approach for
-### intrusive binaural auditory-model-based quality prediction (BAM-Q) is introduced. BAM-Q
-### uses a binaural auditory model as front-end to extract the three binaural features interaural
-### level difference, interaural time difference, and a measure of interaural coherence. The current
-### approach focuses on the general applicability (with respect to binaural signal differences) of
-### the binaural quality model to arbitrary binaural audio signals. Thus, two listening experiments
-### were conducted to subjectively measure the influence of these binaural features and their
-### combinations on binaural quality perception. The results were used to train BAM-Q. Two
-### different hearing aid algorithms were used to evaluate the performance of the model. The
-### correlations between subjective mean ratings and model predictions are higher than 0.9.
+### Abstract:
+Binaural or spatial presentation of audio signals has become increasingly important in
+consumer sound reproduction, but also for hearing assistive devices like hearing aids, where
+signals in both ears might undergo heavy signal processing. Such processing might introduce
+distortions to the interaural signal properties that affect perception. Here, an approach for
+intrusive binaural auditory-model-based quality prediction (BAM-Q) is introduced. BAM-Q
+uses a binaural auditory model as front-end to extract the three binaural features interaural
+level difference, interaural time difference, and a measure of interaural coherence. The current
+approach focuses on the general applicability (with respect to binaural signal differences) of
+the binaural quality model to arbitrary binaural audio signals. Thus, two listening experiments
+were conducted to subjectively measure the influence of these binaural features and their
+combinations on binaural quality perception. The results were used to train BAM-Q. Two
+different hearing aid algorithms were used to evaluate the performance of the model. The
+correlations between subjective mean ratings and model predictions are higher than 0.9.
 
 
-## Author of the Matlab implementation of BAM-Q: 
-### jan-hendrik.flessner@jade-hs.de
+### Author of the Matlab implementation of BAM-Q: 
+jan-hendrik.flessner@jade-hs.de
 
 
-##===============================================================================
+### ===============================================================================
 ### License and permissions
 ### ===============================================================================