Implementation of the Real-Time Spherical Array Renderer for binaural reproduction in Python [1][2].

Contents: | Requirements | Setup | Quickstart | Execution parameters | Execution modes | Remote Control | Validation - Setup and Execution | Benchmark - Setup and Execution | References | Change Log | Contributing | Credits | License |

• macOS (tested on 10.14 Mojave and 10.15 Catalina) or Linux (tested on 5.9.1-1-rt19-MANJARO)
  (Windows is not supported due to an incompatibility with the current multiprocessing implementation)
• JACK library (brew install qjackctl or alternatively prebuilt installers are available)
  (macOS 11 Big Sur and following versions require jack >= 1.9.21 to work correctly!)
• Conda installation (miniconda is sufficient; provides an easy way to get Intel MKL or alternatively OpenBLAS optimized numpy versions which is highly recommended)
• Python installation (tested with 3.7 to 3.11; recommended way to get Python is to use Conda as described in the setup section)
• Installation of the required Python packages (recommended way is to use Conda as described in the setup section)
• Optional: Download of publicly available measurement data for alternative execution modes (always check the command line output or log files in case the rendering pipeline does not initialize successfully!)
• Optional: Install an OSC client for real-time feedback and remote control options during runtime

• Clone repository with command line or any other git client:
  git clone https://github.com/AppliedAcousticsChalmers/ReTiSAR.git
  • Alternative: Download and extract snapshot manually from provided URL (not recommended due to not being able to pull updates)
  • Alternative: Update your local copy with changes from the repository (if you have cloned it in the past):
    git pull
• Navigate into the repository (the directory containing setup.py):
  cd ReTiSAR/
• Install required Python packages i.e., Conda is recommended:
  • Make sure that Conda is up-to-date:
    conda update conda
  • Create new Conda environment from the specified requirements (--force to overwrite potentially existing outdated environment):
    conda env create --file environment.yml --force
  • Activate created Conda environment:
    conda activate ReTiSAR

• Follow requirements and setup instructions
• During first execution, some small amount of additional mandatory external measurement data will be downloaded automatically, see remark in execution modes (requires Internet connection)
• Start JACK server with desired sampling rate (all demo configurations are in 48 kHz) preferably with the QjackCtl application or terminal:
  jackd -d coreaudio -r 48000 [macOS]
  jackd -d alsa -r 48000 [Linux]
  Remark: Select the correct audio interface that should be used in QjackCtl or terminal with the help of e.g.
  jackd -d coreaudio -d -l !
• Run package with [default] parameters to hear a binaural rendering of a raw Eigenmike recording:
  python -m ReTiSAR
• Option 1: Modify the configuration by changing the default parameters in config.py (prepared block comments for the specific execution modes below exist).
• Option 2: Modify the configuration by command line arguments (like in the following examples showing different execution parameters and modes, see --help).

JACK initialization — In case you have never started the JACK audio server on your system or want to make sure it initializes with appropriate values. Open the QjackCtl application to set your system specific default settings.
At this point the only relevant JACK audio server setting is the sampling frequency, which has to match the sampling frequency of your rendered audio source file or stream (no resampling will be applied for that specific file).

FFTW optimization — In case the rendering takes very long to start (after the message "initializing FFTW DFT optimization ..."), you might want to endure this long computation time once (per rendering configuration) or lower your FFTW planner effort (see --help).

Rendering performance — Follow these remarks to expect continuous and artifact free rendering:

• Optional components like array pre-rendering, headphone equalization, noise generation, etc. will save performance in case they are not deployed.
• Extended IR lengths (particularly for modes with an array IR pre-rendering) will massively increase the computational load depending on the chosen block size (partitioned convolution).
• Currently, there is no partitioned convolution for the main binaural renderer with SH based processing, hence the FIR taps of the applied HRIR, Modal Radial Filters and further compensations (e.g. Spherical Head Filter) need to cumulatively fit inside the chosen block size.
• Higher block size means lower computational load in real-time rendering but also increased system latency, most relevant for modes with array live-stream rendering, but also all other modes in terms of a slightly "smeared" head-tracking experience (noticeable at 4096 samples).
• Adjust output levels of all rendering components (default parameters chosen accordingly) to prevent signal clipping (indicated by warning messages during execution).
• Check JACK system load (e.g. QjackCtl or OSC_Remote_Demo.pd) to be below approx. 95% load, in order to prevent dropouts (i.e. the OS reported overall system load is not a good indicator).
• Check JACK detected dropouts ("xruns" indicated during execution).
• Most of all, use your ears! If something sounds strange, there is probably something going wrong... ;)

Always check the command line output or generated log files in case the rendering pipeline does not initialize successfully!

The following parameters are all optional and available in combinations with the named execution modes subsequently:

• Run with a specific processing block size (choose the value according to the individual rendering configuration and performance of your system)
  
  • The largest block size (the best performance but noticeable input latency):
    python -m ReTiSAR -b=4096 [default]
    
  • Try smaller block sizes according to the specific rendering configuration and individual system performance:
    python -m ReTiSAR -b=1024
python -m ReTiSAR -b=256
• Run with a specific processing word length
  
  • Single precision 32 bit (better performance):
    python -m ReTiSAR -SP=TRUE [default]
    
  • Double precision 64 bit (no configuration with an actual benefit is known):
    python -m ReTiSAR -SP=FALSE
• Run with a specific IR truncation cutoff level (applied to all IRs)
  
  • Cutoff -60 dB under peak (better performance and perceptually irrelevant in most cases):
    python -m ReTiSAR -irt=-60 [default]
  • No cutoff to render the entire IR (this constitutes tough performance requirements in the case of rendering array IRs with long reverberation):
    python -m ReTiSAR -irt=0 [applied in all scientific evaluations]
• Run with a specific head-tracking device (paths are system dependent!)
  
  • No tracking (head movement can be remote controlled):
    python -m ReTiSAR -tt=NONE [default]
  • Automatic rotation:
    python -m ReTiSAR -tt=AUTO_ROTATE
  • Tracker Razor AHRS:
    python -m ReTiSAR -tt=RAZOR_AHRS -t=/dev/tty.usbserial-AH03F9XC
  • Tracker Polhemus Patriot:
    python -m ReTiSAR -tt=POLHEMUS_PATRIOT -t=/dev/tty.UC-232AC
  • Tracker Polhemus Fastrack:
    python -m ReTiSAR -tt=POLHEMUS_FASTRACK -t=/dev/tty.UC-232AC
• Run with a specific HRTF dataset as MIRO [6] or SOFA [7] files
  
  • Neumann KU100 artificial head from [6] as SOFA:
    python -m ReTiSAR -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA [default]
  • Neumann KU100 artificial head from [6] as MIRO:
    python -m ReTiSAR -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir_struct.mat -hrt=HRIR_MIRO
  • Neumann KU100 artificial head from [8] as SOFA:
    python -m ReTiSAR -hr=res/HRIR/KU100_SADIE2/48k_24bit_256tap_8802dir.sofa -hrt=HRIR_SOFA
  • GRAS KEMAR artificial head from [8] as SOFA:
    python -m ReTiSAR -hr=res/HRIR/KEMAR_SADIE2/48k_24bit_256tap_8802dir.sofa -hrt=HRIR_SOFA
  • FABIAN artificial head from [9] as SOFA:
    python -m ReTiSAR -hr=res/HRIR/FABIAN_TUB/44k_32bit_256tap_11950dir_HATO_0.sofa -hrt=HRIR_SOFA
  • Employ an arbitrary (artificial or individual) dataset by providing a relative / absolute path!
  • The length of the employed HRIR dataset constrains the minimum usable rendering block size!
  • Mismatched IRs with a sampling frequency different to the source material will be resampled!
• Run with a specific headphone equalization / compensation filters (arbitrary filter length). The compensation filter should match the used headphone model or even the individual headphone. In the best case scenario, the filter was also gathered on the identical utilized HRIR (artificial or individual head).
  
  • No individual headphone compensation:
    python -m ReTiSAR -hp=NONE [default]
  • Beyerdynamic DT990 headphone on Neumann KU100 artificial head from [8]:
    python -m ReTiSAR -hp=res/HPCF/KU100_SADIE2/48k_24bit_1024tap_Beyerdynamic_DT990.wav
  • Beyerdynamic DT990 headphone on GRAS KEMAR artificial head from [8]:
    python -m ReTiSAR -hp=res/HPCF/KEMAR_SADIE2/48k_24bit_1024tap_Beyerdynamic_DT990.wav
  • AKG K701 headphone on FABIAN artificial head from [9]:
    python -m ReTiSAR -hp=res/HPCF/FABIAN_TUB/44k_32bit_4096tap_AKG_K701.wav
  • Sennheiser HD800 headphone on FABIAN artificial head from [9]:
    python -m ReTiSAR -hp=res/HPCF/FABIAN_TUB/44k_32bit_4096tap_Sennheiser_HD800.wav
  • Sennheiser HD600 headphone on GRAS KEMAR artificial head from TU Rostock:
    python -m ReTiSAR -hp=res/HPCF/KEMAR_TUR/44k_24bit_2048tap_Sennheiser_HD600.wav
  • Check the res/HPCF/. directory for numerous other headphone models or employ arbitrary (artificial or individual) compensation filters by providing a relative / absolute path!
  • Mismatched IRs with a sampling frequency different to the source material will be resampled!
• Run with a specific SH processing compensation techniques (relevant for rendering modes utilizing spherical harmonics)
  
  • Modal Radial Filters [always applied] with an individual amplification soft-limiting in dB according to [3]:
    python -m ReTiSAR -arr=18 [default]
  • Spherical Head Filter according to [4]:
    python -m ReTiSAR -sht=SHF
  • Spherical Harmonics Tapering in combination with the Spherical Head Filter according to [5]:
    python -m ReTiSAR -sht=SHT+SHF [default]
• Run with some specific emulated self-noise as additive component to each microphone array sensor (the performance requirements increase according to channel count)
  
  • No noise (yielding the best performance):
    python -m ReTiSAR -gt=NONE [default]
  • White noise (also setting the initial output level and mute state of the rendering component):
    python -m ReTiSAR -gt=NOISE_WHITE -gl=-30 -gm=FALSE
  • Pink noise by IIR filtering (higher performance requirements):
    python -m ReTiSAR -gt=NOISE_IIR_PINK -gl=-30 -gm=FALSE
  • Eigenmike noise coloration by IIR filtering from [10]:
    python -m ReTiSAR -gt=NOISE_IIR_EIGENMIKE -gl=-30 -gm=FALSE
• For further configuration parameters, check Alternative 1 and Alternative 2 above!

This section list all the conceptually different rendering modes of the pipeline. Most of the other beforehand introduced execution parameters can be combined with the mode-specific parameters. In case no manual value for all specific rendering parameters is provided (as in the following examples), their respective default values will be used.

Most execution modes require additional external measurement data, which cannot be republished here. However, all provided examples are based on publicly available research data. Respective files are represented here by provided source reference files (see res/), containing a source URL and potentially further instructions. In case the respective resource data file is not yet available on your system, download instructions will be shown in the command line output and generated log files.

• Run as array recording renderer
  

  • Eigenmike at Chalmers lab space with speaker moving horizontally around the array:
    python -m ReTiSAR -sh=4 -tt=NONE -s=res/record/EM32ch_lab_voice_around.wav -ar=res/ARIR/RT_calib_EM32ch_struct.mat -art=AS_MIRO -arl=0 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA [default]
  • Eigenmike at Chalmers lab space with speaker moving vertically in front of the array:
    python -m ReTiSAR -sh=4 -tt=NONE -s=res/record/EM32ch_lab_voice_updown.wav -ar=res/ARIR/RT_calib_EM32ch_struct.mat -art=AS_MIRO -arl=0 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA
  • Zylia ZM-1 at TH Cologne office (recording file not provided!):
    python -m ReTiSAR -b=512 -sh=3 -tt=NONE -s=res/record/ZY19_off_around.wav -sl=9 -ar=res/ARIR/RT_calib_ZY19_struct.mat -art=AS_MIRO -arl=0 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA
  • HØSMA-7N at TH Cologne lecture hall (recording file not provided!):
    python -m ReTiSAR -b=2048 -sh=7 -tt=NONE -s=res/record/HOS64_hall_lecture.wav -sp="[(90,0)]" -sl=9 -ar=res/ARIR/RT_calib_HOS64_struct.mat -art=AS_MIRO -arl=0 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA

• Run as array live-stream renderer with minimum latency (e.g. Eigenmike with the respective channel calibration provided by the manufacturer)
  

  • Eigenmike Chalmers EM32 (SN 28):
    python -m ReTiSAR -b=512 -sh=4 -tt=NONE -s=None -ar=res/ARIR/RT_calib_EM32ch_struct.mat -art=AS_MIRO -arl=0 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA
    
  • Eigenmike Facebook Reality Labs EM32 (SN ??):
    python -m ReTiSAR -b=512 -sh=4 -tt=NONE -s=None -ar=res/ARIR/RT_calib_EM32frl_struct.mat -art=AS_MIRO -arl=0 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA
  • Zylia ZM-1:
    python -m ReTiSAR -b=512 -sh=3 -tt=NONE -s=None -ar=res/ARIR/RT_calib_ZY19_struct.mat -art=AS_MIRO -arl=0 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA
  • TH Cologne HØSMA-7N:
    python -m ReTiSAR -b=2048 -sh=7 -tt=NONE -s=None -ar=res/ARIR/RT_calib_HOS64_struct.mat -art=AS_MIRO -arl=0 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA

• Run as array IR renderer, e.g. Eigenmike
  

  • Simulated plane wave:
    python -m ReTiSAR -sh=4 -tt=AUTO_ROTATE -s=res/source/Drums_48.wav -ar=res/ARIR/DRIR_sim_EM32_PW_struct.mat -art=ARIR_MIRO -arl=-6 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA
  • Anechoic measurement:
    python -m ReTiSAR -sh=4 -tt=AUTO_ROTATE -s=res/source/Drums_48.wav -ar=res/ARIR/DRIR_anec_EM32ch_S_struct.mat -art=ARIR_MIRO -arl=0 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA

• Run as array IR renderer, e.g. sequential VSA measurements from [11] at the maximum respective SH order (different room, source positions and array configurations are available in res/ARIR/)
  

  • 50ch (sh5), LBS center:
    python -m ReTiSAR -sh=5 -tt=AUTO_ROTATE -s=res/source/Drums_48.wav -ar=res/ARIR/DRIR_LBS_VSA_50RS_PAC.sofa -art=ARIR_SOFA -arl=-12 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA
  • 86ch (sh7), SBS center:
    python -m ReTiSAR -sh=7 -tt=AUTO_ROTATE -s=res/source/Drums_48.wav -ar=res/ARIR/DRIR_SBS_VSA_86RS_PAC.sofa -art=ARIR_SOFA -arl=-12 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA
  • 110ch (sh8), CR1 left:
    python -m ReTiSAR -sh=8 -tt=AUTO_ROTATE -s=res/source/Drums_48.wav -sp="[(37,0)]" -ar=res/ARIR/DRIR_CR1_VSA_110RS_L.sofa -art=ARIR_SOFA -arl=-12 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA
  • 194ch (sh11, open sphere, cardioid microphones), LBS center:
    python -m ReTiSAR -sh=11 -tt=AUTO_ROTATE -s=res/source/Drums_48.wav -ar=res/ARIR/DRIR_LBS_VSA_194OSC_PAC.sofa -art=ARIR_SOFA -arl=-12 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA
  • 1202ch (truncated sh12), CR7 left:
    python -m ReTiSAR -sh=12 -tt=AUTO_ROTATE -s=res/source/Drums_48.wav -sp="[(37,0)]" -ar=res/ARIR/DRIR_CR7_VSA_1202RS_L.sofa -art=ARIR_SOFA -arl=-12 -hr=res/HRIR/KU100_THK/48k_32bit_128tap_2702dir.sofa -hrt=HRIR_SOFA

Note that the rendering performance is mostly determined by the chosen combination of the following parameters: number of microphones (ARIR channels), room reverberation time (ARIR length), IR truncation cutoff level and rendering block size.

• Run as BRIR renderer (partitioned convolution in frequency domain) for any BRIR compatible to the SoundScape Renderer, e.g. pre-processed array IRs by [12]:
  python -m ReTiSAR -tt=AUTO_ROTATE -s=res/source/Drums_48.wav -art=NONE -hr=res/HRIR/KU100_THK/BRIR_CR1_VSA_110RS_L_SSR_SFA_-37_SOFA_RFI.wav -hrt=BRIR_SSR -hrl=-12
• Run as "binauralizer" for an arbitrary number of virtual sound sources via HRTF (partitioned convolution in frequency domain) for any HRIR compatible to the SoundScape Renderer:
  python -m ReTiSAR -tt=AUTO_ROTATE -s=res/source/PinkMartini_Lilly_44.wav -sp="[(30, 0),(-30, 0)]" -art=NONE -hr=res/HRIR/FABIAN_TUB/hrirs_fabian.wav -hrt=HRIR_SSR (provide respective source file and source positions!)

• During runtime, certain parameters of the application can be remote controlled via Open Sound Control. Individual clients can be accessed by targeting them with specific OSC commands on port 5005 [default].
  Depending on the current configuration and rendering mode different commands are available i.e., arbitrary combinations of the following targets and values:
  /generator/volume 0, /generator/volume -12 (set any client output volume in dBFS),
  /prerenderer/mute 1, /prerenderer/mute 0, /prerenderer/mute -1, /prerenderer/mute (set/toggle any client mute state),
  /hpeq/passthrough true, /hpeq/passthrough false, /hpeq/passthrough toggle (set/toggle any client passthrough state)
• The target name is derived from the individual JACK client name for all commands, while the order of target client and command can be altered, while further commands might be available:
  /renderer/crossfade, /crossfade/renderer (set/toggle the crossfade state),
  /renderer/delay 350.0 (set an additional input delay in ms),
  /renderer/order 0, /renderer/order 4 (set the SH rendering order),
  /tracker/zero (calibrate the tracker), /tracker/azimuth 45 (set the tracker orientation),
  /player/stop, /player/play, /quit (quit all rendering components)
• During runtime, individual JACK clients with their respective "target" name also report real-time feedback or analysis data on port 5006 [default] in the specified exemplary data format (number of values depends on output ports) i.e., arbitrary combinations of the name and parameters:
  /player/rms 0.0, /generator/peak 0.0 0.0 0.0 0.0 (the current audio output metrics),
  /renderer/load 100 (the current client load),
  /tracker/AzimElevTilt 0.0 0.0 0.0 (the current head orientation),
  /load 100 (the current JACK system load)
• In the package included is an example remote control client implemented for "vanilla" PD, see further instructions in OSC_Remote_Demo.pd.

• Install ecasound library for signal playback and capture with JACK support:
  brew install ecasound
• Optional: Install sendosc tool to be used for automation in shell scripts:
  brew install yoggy/tap/sendosc
• Remark: Make sure all subsequent rendering configurations are able to start up properly before recording starts (particularly FFTW optimization might take a long time, see above)
• Validate impulse responses by comparing against a reference implementation, in this case the output of sound_field_analysis-py [11]
  • Execute recording script, consecutively starting the package and capturing impulse responses in different rendering configurations:
    ./res/research/validation/record_ir.sh
    Remark: Both implementations compensate the source being at an incidence angle of -37 degrees in the measurement IR set
  • Run package in validation mode, executing a comparison of all beforehand captured IRs in res/research/validation/ against the provided reference IRs:
    python -m ReTiSAR --VALIDATION_MODE=res/HRIR/KU100_THK/BRIR_CR1_VSA_110RS_L_SSR_SFA_-37_SOFA_RFI.wav
• Validate signal-to-noise-ratio by comparing input and output signals of the main binaural renderer for wanted target signals and emulated sensor self-noise respectively
  • Execute recording script consecutively starting the package and capturing target-noise as well as self-noise input and output signals in different rendering configurations:
    ./res/research/validation/record_snr.sh
  • Open (and run) MATLAB analysis script to execute an SNR comparison of beforehand captured signals:
    open ./res/research/validation/calculate_snr.m

• Install additionally required Python packages into Conda environment:
  conda env update --file environment_dev.yml
• Run the JACK server with arbitrary sampling rate via QjackCtl or in a new command line window ([CMD]+[T]):
  jackd -d coreaudio
• Run in benchmark mode, instantiating one rendering JACK client with as many convolver instances as possible (40-60 minutes):
  python -m ReTiSAR --BENCHMARK_MODE=PARALLEL_CONVOLVERS
• Run in benchmark mode, instantiating as many rendering JACK clients as possible with one convolver instance (10-15 minutes):
  python -m ReTiSAR --BENCHMARK_MODE=PARALLEL_CLIENTS
• Find generated results in the specified files at the end of the script.

[1] H. Helmholz, C. Andersson, and J. Ahrens, “Real-Time Implementation of Binaural Rendering of High-Order Spherical Microphone Array Signals,” in Fortschritte der Akustik -- DAGA 2019, 2019, pp. 1462–1465.
[2] H. Helmholz, T. Lübeck, J. Ahrens, S. V. Amengual Garí, D. Lou Alon, and R. Mehra, “Updates on the Real-Time Spherical Array Renderer (ReTiSAR),” in Fortschritte der Akustik -- DAGA 2020, 2020, pp. 1169–1172.
[3] B. Bernschütz, C. Pörschmann, S. Spors, and S. Weinzierl, “SOFiA Sound Field Analysis Toolbox,” in International Conference on Spatial Audio, 2011, pp. 7–15.
[4] C. Hold, H. Gamper, V. Pulkki, N. Raghuvanshi, and I. J. Tashev, “Improving Binaural Ambisonics Decoding by Spherical Harmonics Domain Tapering and Coloration Compensation,” in International Conference on Acoustics, Speech and Signal Processing, 2019, pp. 261–265, doi: 10.1109/ICASSP.2019.8683751.
[5] Z. Ben-Hur, F. Brinkmann, J. Sheaffer, S. Weinzierl, and B. Rafaely, “Spectral Equalization in Binaural Signals Represented by Order-Truncated Spherical Harmonics,” J. Acoust. Soc. Am., vol. 141, no. 6, pp. 4087–4096, 2017, doi: 10.1121/1.4983652.
[6] B. Bernschütz, “A Spherical Far Field HRIR/HRTF Compilation of the Neumann KU 100,” in Fortschritte der Akustik -- AIA/DAGA 2013, 2013, pp. 592–595, doi: 10.5281/zenodo.3928296.
[7] P. Majdak et al., “Spatially Oriented Format for Acoustics: A Data Exchange Format Representing Head-Related Transfer Functions,” in AES Convention 134, 2013, pp. 262–272.
[8] C. Armstrong, L. Thresh, D. Murphy, and G. Kearney, “A Perceptual Evaluation of Individual and Non-Individual HRTFs: A Case Study of the SADIE II Database,” Appl. Sci., vol. 8, no. 11, pp. 1–21, 2018, doi: 10.3390/app8112029.
[9] F. Brinkmann et al., “The FABIAN Head-Related Transfer Function Data Base.” Technische Universität Berlin, Berlin, Germany, 2017, doi: 10.14279/depositonce-5718.5.
[10] H. Helmholz, D. Lou Alon, S. V. Amengual Garí, and J. Ahrens, “Instrumental Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals,” in Forum Acusticum, 2020, pp. 1349–1356, doi: 10.48465/fa.2020.0074.
[11] P. Stade, B. Bernschütz, and M. Rühl, “A Spatial Audio Impulse Response Compilation Captured at the WDR Broadcast Studios,” in 27th Tonmeistertagung -- VDT International Convention, 2012, pp. 551–567.
[12] C. Hohnerlein and J. Ahrens, “Spherical Microphone Array Processing in Python with the sound_field_analysis-py Toolbox,” in Fortschritte der Akustik -- DAGA 2017, 2017, pp. 1033–1036.
[13] H. Helmholz, J. Ahrens, D. Lou Alon, S. V. Amengual Garí, and R. Mehra, “Evaluation of Sensor Self-Noise In Binaural Rendering of Spherical Microphone Array Signals,” in International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 161–165, doi: 10.1109/ICASSP40776.2020.9054434.
[14] H. Helmholz, D. Lou Alon, S. V. Amengual Garí, and J. Ahrens, “Effects of Additive Noise in Binaural Rendering of Spherical Microphone Array Signals,” IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 29, pp. 3642–3653, 2021, doi: 10.1109/TASLP.2021.3129359.

See CHANGE_LOG for full details.

See CONTRIBUTING for full details.

Written by Hannes Helmholz.

Scientific supervision by Jens Ahrens.

Contributions by Carl Andersson and Tim Lübeck.

This work was funded by Facebook Reality Labs.

This software is licensed under a Non-Commercial Software License (see LICENSE for full details).

Copyright (c) 2018
Division of Applied Acoustics
Chalmers University of Technology