README.md with deps, build, usage, troubleshooting
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**APM Quality Assessment tool**
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# APM Quality Assessment tool
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Python wrapper of audioproc_f with which quality assessment can be automatized.
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The tool allows to simulate different noise conditions, input signals, APM
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configurations and it computes different scores.
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Python wrapper of `audioproc_f` with which quality assessment can be
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automatized. The tool allows to simulate different noise conditions, input
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signals, APM configurations and it computes different scores.
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Once the scores are computed, the results can be easily exported to an HTML page
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which allows to listen to the APM input and output signals and also the
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reference one used for evaluation.
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IMPORTANT: **the whole code has not been landed yet.**
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## Dependencies
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- OS: Linux
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- Python 2.7
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- Python libraries: numpy, scipy, pydub (0.17.0+)
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- `$ sudo apt install python-numpy python-scipy`
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- `$ sudo pip install pydub`
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- PolqaOem64 (see http://www.polqa.info/)
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- Tested with POLQA Library v1.180 / P863 v2.400
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- Aachen Impulse Response (AIR) Database
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- Download https://www2.iks.rwth-aachen.de/air/air_database_release_1_4.zip
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- Input probing signals and noise tracks (you can make your own dataset - *1)
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## Build
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- Compile WebRTC
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- Go to `out/Default/py_quality_assessment` and check that
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`apm_quality_assessment.py` exists
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## First time setup
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- Deploy PolqaOem64 and set the `POLQA_PATH` environment variable
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- e.g., `$ export POLQA_PATH=/var/opt/PolqaOem64`
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- Deploy the AIR Database and set the `AECHEN_IR_DATABASE_PATH` environment
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variable
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- e.g., `$ export AECHEN_IR_DATABASE_PATH=/var/opt/AIR_1_4`
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- Deploy probing signal tracks into
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- `out/Default/py_quality_assessment/probing_signals` (*1)
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- Deploy noise tracks into
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- `out/Default/py_quality_assessment/noise_tracks` (*1, *2)
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(*1) You can use custom files as long as they are mono tracks sampled at 48kHz
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encoded in the 16 bit signed format (it is recommended that the tracks are
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converted and exported with Audacity).
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(*2) Adapt `EnvironmentalNoiseGenerator._NOISE_TRACKS` accordingly in
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`out/Default/py_quality_assessment/quality_assessment/noise_generation.py`.
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## Usage (scores computation)
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- Go to `out/Default/py_quality_assessment`
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- Check the `apm_quality_assessment.sh` as an example script to parallelize the
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experiments
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- Adjust the script according to your preferences (e.g., output path)
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- Run `apm_quality_assessment.sh`
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- The script will end by opening the browser and showing ALL the computed
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scores
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## Usage (export reports)
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Showing all the results at once can be confusing. You therefore may want to
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export separate reports. In this case, you can use the
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`apm_quality_assessment_export.py` script as follows:
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- Set --output_dir to the same value used in `apm_quality_assessment.sh`
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- Use regular expressions to select/filter out scores by
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- APM configurations: `--config_names, -c`
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- probing signals: `--input_names, -i`
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- noise generators: `--noise_generators, -n`
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- scores: `--eval_scores, -e`
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- Assign a suffix to the report name using `-f <suffix>`
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For instance:
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```
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$ ./apm_quality_assessment-export.py \
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-o ~/data/apm_quality_assessment \
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-e \(polqa\) \
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-n \(echo\) \
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-c "(^default$)|(.*AE.*)" \
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-f echo
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```
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## Troubleshooting
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The input wav file must be:
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- sampled at a sample rate that is a multiple of 100 (required by POLQA)
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- in the 16 bit format (required by `audioproc_f`)
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- encoded in the Microsoft WAV signed 16 bit PCM format (Audacity default
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when exporting)
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Depending on the license, the POLQA tool may take “breaks” as a way to limit the
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throughput. When this happens, the APM Quality Assessment tool is slowed down.
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For more details about this limitation, check Section 10.9.1 in the POLQA manual
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v.1.18.
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In case of issues with the POLQA score computation, check
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`py_quality_assessment/eval_scores.py` and adapt
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`PolqaScore._parse_output_file()`.
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The code can be also fixed directly into the build directory (namely,
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`out/Default/py_quality_assessment/eval_scores.py`).
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