Commit 89d2e85c 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.
### '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
## 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
## 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
##===============================================================================
### License and permissions
### ===============================================================================
Unless otherwise stated, the GPSMq distribution, including all files is licensed
under Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
(CC BY-NC-ND 4.0).
In short, this means that you are free to use and share (copy, distribute and
transmit) the GPSMq distribution under the following conditions:
Attribution - You must attribute the GPSMq distribution by acknowledgement of
the author if it appears or if was used in any form in your work.
The attribution must not in any way that suggests that the author
endorse you or your use of the work.
Noncommercial - You may not use GPSMq for commercial purposes.
No Derivative Works - You may not alter, transform, or build upon GPSMq.
Exceptions are the following external Matlab functions (see their respective licence)
that were used within the BAM-Q:
- Gammatone filterbank from V. Hohmann (https://zenodo.org/record/2643400#.XQsf5TnVLCM), for details see[1,2]:
[1] Hohmann, V. (2002). Frequency analysis and synthesis using a Gammatone filterbank.
Acta Acustica united with Acustica, 88(3), 433-442.
[2] Herzke, T., & Hohmann, V. (2007). Improved numerical methods for gammatone filterbank analysis and
synthesis. Acta acustica united with acustica, 93(3), 498-500.
- Adaptive Regression Splines toolbox for Matlab/Octave (URL: http://www.cs.rtu.lv/jekabsons/)
Author: Gints Jekabsons (gints.jekabsons@rtu.lv)
- Code snipets from the Dietz Modell (Authors: Mathias Dietz, Martin-Klein Hennig), for details see:
M. Dietz, S. D. Ewert, and V. Hohmann. Auditory model based direction estimation of concurrent speakers
from binaural signals. Speech Communication, 53(5):592-605, 2011.
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