Commit 80a7020c authored by dualberger's avatar dualberger

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### 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
### ===============================================================================
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