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17 results for “microphone array”
Associated dataset for "Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The conducted instrumental and perceptual evaluation utilize the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. However, the provided execution configurations (see below) are probably not exactly in accordance with the latest ReTiSAR code base. Hence, the at the time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:<br> <a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way you should obtain exactly the same Python setup as utilized in the instrumental and perceptual evaluation in the publication:</p> <pre><code>conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code>source activate ReTiSAR_ICASSP_freeze</code></pre> <p>Directory "SNR":</p> <ul> <li>Tools for instrumental evaluation (Section 4)</li> <li>Shell script to capture input and output signals of rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all specified configurations</li> <li>Matlab script to analyse captured signal and generate system transfer plots (Figure 1 to Figure 3 and further configurations)</li> </ul> <p>Directory "Relative Output Levels":</p> <ul> <li>Tools for preparation of perceptual evaluation (Section 5)</li> <li>Shell script to capture rendered uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse and level align captured signals and generate plot result plot (Figure 4)</li> </ul> <p>Directory "Absolute Output Levels":</p> <ul> <li>Tools for specification of perceptual evaluation (Section 5)</li> <li>Shell script to capture reproduced uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse the calibrated captured signals yielding the average level in the ear signals of 58.2 dBSPL (Section 5.1)</li> </ul> <p>Files in base directory and directory "Study Results":</p> <ul> <li>Tools for perceptual evaluation / user study (Section 5)</li> <li>Matlab GUI to conduct perceptual user study (employ by executing "ICASSP_gui.m", respective ReTiSAR instances are started and remote controlled by the GUI, raw study results will be stored in "results" directory)</li> <li>Matlab script to "calculate_conclusion.m" to analyse the raw study results and generate individual and conclusive result plots (Figure 5, Figure 6 and more)</li> </ul>
Auralization of virtual microphone array sensors considering coherence loss by atmospheric turbulence for two moving monopole sources
Open the record for dataset details and reuse information.
3D Microphone Array Recording Comparison (3D-MARCo)
<p>3D-MARCo is an open-access database of 3D sound recordings of musical performances and room impulse responses. The recordings were made in the St. Paul’s concert hall in Huddersfield, UK using a total of 71 microphones simultaneously. The main microphone arrays included in the database comprise PCMA-3D, OCT-3D, 2L-Cube, Decca Cubioid, First-order Ambisonics (FOA), Higher-order Ambisonics (HOA) and Hamasaki Square with height. In addition, ORTF, side/height, Voice of God and floor channels as well as a dummy head and spot microphones are included. The sound sources recorded are string quartet, piano trio, piano solo, organ, a cappella group, various single sources and room impulse responses of a virtual ensemble with 13 source positions captured by all of the microphones. 3D-MARCo would be useful for spatial audio research, recording education, critical ear training, etc.</p>
Microphone array data for basic examples in Acoular
<p>This record contains microphone array data for use with <a href="http://acoular.org">Acoular </a>software.</p> <p>example_data.h5 contains a 1 second time history from a windtunnel measurement with 56 microphones. array_56.xml contains the microphone coordinates, and example_calib.xml the calibration factors.</p> <p>three_sources.h5 and two_sources.h5 contain synthetic measurement for a three and a two source scenario, respectively. array_64 contains the microphone coordinates for this case.</p> <p>three_sources.csv, three_sourcesv7.mat, and three_sourcesv73.mat contain the same data, but in CSV, MATLAB old and MATLAB new format.</p>
Dataset of Violin Recordings with Spherical Microphone Arrays
<p>This data set contains simultaneous recordings of violin instrument sounds by multiple Spot microphones placed on a spherical skeleton.<br> There are 32 Spot microphones, recorded in 23 different ways of expression.</p> <p>Microphone ID</p> <p>Top Layer<br> 32<br> 27-28-29-30-31<br> 22-23-24-25-26<br> 17-18-19-20-21<br> 12-13-14-15-16<br> 7-8-9-10-11<br> 2-3-4-5-6<br> 1<br> Bottom Layer</p> <p> </p>
Associated dataset for "Instrumental Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The conducted instrumental evaluation utilizes the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. The at that time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:<br> <a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way, you will obtain exactly the same Python setup as utilized in the instrumental evaluation in the publication:</p> <pre><code class="language-bash">conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code class="language-bash">source activate ReTiSAR_FA_freeze</code></pre> <p>Directory "SMA sampling grids":</p> <ul> <li>Visualization of spatial arrangement (like Figure 4) for all investigated spherical microphone array rendering configurations (Table 1)</li> </ul> <p>Shell script "record_snr.sh":</p> <ul> <li>Record the input and output signals of the rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all configurations at multiple head orientations</li> <li>All captured signals are contained in the "SNR" directory</li> </ul> <p>Matlab script "calculate_snr.m":</p> <ul> <li>Visualize the raw captured input and output signals (like Figure 1 for all configurations)</li> <li>Visualize the resulting signal-to-noise ratio (like Figure 2 for all configurations)</li> <li>Visualize the comparison of the resulting signal-to-noise ratio of all configurations (Figure 3, also for the resulting SNR from signals with A-weighting)</li> <li>All generated plots are contained in the "SNR" directory</li> </ul> <p>Shell script "record_noise.sh":</p> <ul> <li>Record the calibration and noise signals of the mh acoustic Eigenmike 32 spherical microphone array in the anechoic chamber at Chalmers University of Technology (Appendix)</li> <li>All captured signals are contained in the "EM32 measurements" directory</li> <li>Pictures of the measurement setup are contained in the "Pictures" subdirectory</li> </ul> <p>Matlab script "calculate_EM32_noise_levels.m":</p> <ul> <li>Determine the resulting target signal sensitivity and equivalent input noise levels for the investigated pre-amplification gains (Table 2)</li> <li>Visualize the statistical distribution of the individual raw and weighted SMA channels (like Figure 6 for all configurations)</li> <li>Visualize the spatial distribution of the individual raw and weighted SMA channels for all configurations</li> <li>Visualize the smoothed and averaged magnitude spectra of the individual raw and weighted SMA channels (like Figure 5 for all configurations)</li> </ul>
3D microphone arrays survey
<p>Subjective ratings of audio recordings made using various 3D microphone arrays. </p>
Associated dataset for "Effects of Additive Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The instrumental evaluation utilized the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. The employed code state at that time should be used to reproduce the rendering results in this data set exactly. The frozen code state for this data set is available at:<br><a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2021.TASLP">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2021.TASLP</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the Conda environment file included here when setting up the Python environment. In this way, you will obtain the exact Python setup as utilized in the instrumental evaluation in the publication:</p> <pre><code>conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code>source activate ReTiSAR_TASLP_freeze</code></pre> <p>Matlab script "generate_norm_levels.m":</p> <ul> <li>The level contributions used in the publication are contained in "record_CLL_levels.sh", therefore this needs to be executed only in case other level distributions should be generated</li> <li>Generate the string for a ReTiSAR configuration (as used in "record_CLL_levels.sh") to emulate normally contributing EM32 self-noise based on Forum Acusticum publication data</li> <li>Generate the string for aReTiSAR configuration (as used in "record_CLL_levels.sh") to emulate normally contributing GL162 self-noise based on Gaussian normal distribution</li> </ul> <p>Matlab script "prepare_MagLS_HRIRs.m":</p> <ul> <li>Apply Magnitude Least Squares pre-processing to HRIRs (as used in "record_CLL_levels.sh")</li> </ul> <p>Shell script "record_CLL_levels.sh":</p> <ul> <li>Record the output ear signals of the rendering pipeline at multiple head orientations for all investigated configurations (according to Table 1)</li> <li>All captured signals are contained in the respective configuration directory, e.g. "rec_25ch_Fliege_sh4" to "rec_338ch_Gauss_sh12"</li> </ul> <p>Matlab script "calculate_CLL_levels.m":</p> <ul> <li>Read the individual rendering pipeline output recordings for arbitrary configurations</li> <li>Visualize the raw captured signals per configuration</li> <li>Visualize the RMS signal level variations over all head orientations per configuration</li> <li>Visualize the Interaural Level Difference variations over all head orientations per configuration</li> <li>Visualize the Composite Loudness Level variations over all head orientations per configuration (like Figure 2)</li> <li>Gather the above determined RMS, ILD, CLL, etc. metrics in a Matlab dataset per configuration (will be utilized in "plot_gathered_CLL_levels.m")</li> <li>All generated plots and Matlab datasets are contained in the respective configuration directory, e.g. "rec_25ch_Fliege_sh4" to "rec_338ch_Gauss_sh12"</li> </ul> <p>Matlab script "plot_gathered_CLL_levels.m":</p> <ul> <li>Visualize the resulting Composite Loudness Level gradient detections over all head orientations for arbitrary combinations of configurations (like Figure 3 to Figure 14)</li> <li>All generated plots are contained in the "CLL_results" directory</li> </ul>
Rendered Stimuli for "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room"
<h2>Rendered Stimuli from the Subjective Evaluation</h2> <p>This archive (<code>Stimuli.zip</code>) contains the rendered stimuli used in the subjective evaluation of various spatial analysis and synthesis methods, as described in the paper "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room" by Alan Pawlak, Hyunkook Lee, Aki Mäkivirta, and Thomas Lund.</p> <p>The stimuli are provided to improve the reproducibility of the study and to allow readers to listen to the same audio samples used in the subjective evaluation.</p> <h2>File Naming Convention:</h2> <p><code>SYSTEM_PROGRAMMEMATERIAL_AZIMUTH_ELEVATION_-26LUFS.wav</code></p> <p>- <code>SYSTEM</code>: The spatial analysis and synthesis method used (e.g., BSDM-6OM1-Omni, HO-SIRR, SDM-em32, etc.)<br>- <code>PROGRAMMEMATERIAL</code>: The anechoic audio sample used (Bongo, Speech, Orchestra)<br>- <code>AZIMUTH</code>: The azimuth angle of the sound source (e.g., 0, 30, 45, 90, 135)<br>- <code>ELEVATION</code>: The elevation angle of the sound source (e.g., 0, 45)</p> <h2>Audio File Specifications:</h2> <p>- Format: WAV<br>- Sample Rate: 48 kHz<br>- Bit Depth: 32-bit<br>- Loudness Normalization: -26 LUFS</p> <p>To use these stimuli, simply load the desired WAV file into your audio playback software.</p> <p>For more information about the study, please refer to the full paper.</p> <p>Pawlak, A., Lee, H., Mäkivirta, A. and Lund, T., 2024. Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room.</p>
Dataset of Room Impulse Responses from Baffled Microphone Arrays and Sound Sources at Three Elevations
<p>This data set contains a collection of impulse responses (stored in SOFA format) from <em>spherical microphone arrays</em> (<strong>SMA</strong>s), <em>equatorial microphone arrays</em> (<strong>EMA</strong>s), and <em>non-spherical microphone arrays</em> (<strong>XMA</strong>s). Thereby, impulse response sets are provided for each array type at various spatial resolutions, for a loudspeaker sound source at three source elevations, and in four diverse acoustic environments (see <strong>DATA</strong> section for a full description).</p> <p>The original purpose of the microphone array data is the binaural rendering in the <em>spherical harmonics</em> (<strong>SH</strong>) domain into ear signals for high-fidelity reproduction of the acoustic scenario via headphones. Therefore, <em>binaural room impulse responses</em> (<strong>BRIR</strong>s) for 360 horizontal head orientations of a <em>G.R.A.S KEMAR</em> acoustic dummy head are provided as a reference for each scenario.</p> <p>Please contact the authors for questions or additional information regarding the room setups and utilized measurement devices.</p> <p> </p> <p><strong>======<br> DATA<br>======</strong></p> <p>This archive contains the processed impulse response sets of various measurement configurations, as described in this section.</p> <p>Directory "resources/ARIR_processed/":</p> <ul> <li>Post-processed SMA and EMA impulse responses <ul> <li><strong>"_SMA*_"</strong> or <strong>"_EMA*_"</strong> in the file name</li> <li>In SOFA format with <em>"SingleRoomSRIR"</em> convention</li> <li>From 1x <em>DPA 4060</em> microphone flush mounted in a wooden spherical scattering body with an 8.5 cm radius</li> <li>High-resolution data (measured sequentially on VariSphear turntable with two degrees-of-freedom rotations): <ul> <li>Hall: <strong>1202</strong> <strong>channels</strong> (Lebedev grid) for maximum SH order 29</li> <li>Others: <strong>2702</strong> <strong>channels</strong> (Lebedev grid) for maximum SH order 44</li> </ul> </li> <li>Lower-resolution data via subsampling in the SH domain (arbitrary sampling grids and lower target orders can be achieved): <ul> <li>SH order 29: <strong>1742 channels</strong> (t-design grid) for SMA; <strong>59</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 12: <strong>314</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>25</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 8: <strong>146</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>17</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 4: <strong>42</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>9</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 2: <strong>14</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>5</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 1: <strong>6</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>3</strong> <strong>channels</strong> (equiangular grid) for EMA</li> </ul> </li> </ul> </li> <li>Post-processed XMA impulse responses <ul> <li><strong>"_XMA*_"</strong> in the file name</li> <li>In SOFA format with <em>"SingleRoomSRIR"</em> convention</li> <li>From 18x <em>Rode Lavalier GO</em> microphone mounted in an elastic band on a wooden head-shaped scattering body (7.5 cm to 10.5 cm radius)</li> <li>High-resolution data (measured simultaneously): <ul> <li><strong>18 channels</strong> for maximum SH order 8</li> </ul> </li> <li>Lower-resolution data via integer subsets of microphones: <ul> <li>SH order 4: <strong>9 channels</strong></li> <li>SH order 2: <strong>6 channels</strong></li> </ul> </li> <li>Anechoic: For 360 horizontal scattering body orientations (measured sequentially on a VariSphear turntable with azimuth in 1-degree steps)</li> <li>Rooms: For 36 horizontal scattering body orientations (measured sequentially on VariSphear turntable with azimuth in 10-degree steps)</li> </ul> </li> <li>Generated XMA calibration filters and equalization filters <ul> <li><strong>"_x_nm_"</strong> and <strong>"_e_nm_"</strong> in the file name</li> <li>In proprietary Matlab format</li> <li>Time-domain representation of filters in the respective orders of "real" spherical harmonics</li> </ul> </li> <li>Post-processed binaural impulse responses <ul> <li><strong>"_KEMAR_"</strong> in the file name</li> <li>In SOFA format with <em>"SingleRoomSRIR"</em> convention</li> <li>From <em>G.R.A.S KEMAR</em> dummy head with large pinna</li> <li>For 360 horizontal head orientations (measured sequentially on VariSphear turntable with azimuth in 1-degree steps)</li> </ul> </li> <li>Thereby, impulse response sets are included for five acoustic environments <ul> <li><strong>"Simulation_"</strong>: Anechoic simulation of a plane wave impinging from the frontal direction on the array (SMA and EMA only)</li> <li><strong>"Anechoic_"</strong>: Anechoic measurement of a <em>Genelec 8030A</em> loudspeaker at the same height of the array</li> <li>"<strong>LabDry_"</strong>: Room measurement in an acoustically damped laboratory of a <em>Genelec 8030A</em> loudspeaker at three different source heights (the direct floor reflection is attenuated with an additional porous absorber but otherwise identical to the following condition)</li> <li><strong>"LabWet_"</strong>: Room measurement in an acoustically damped laboratory of a <em>Genelec 8030A</em> loudspeaker at three different source heights (the direct reflection is not obstructed from the hard concrete floor, but otherwise identical to the former condition)</li> <li><strong>"Hall_"</strong>: Room measurement in a very reverberant hall of a <em>Genelec 8030A</em> loudspeaker at three different source heights</li> </ul> </li> <li>Thereby, the room impulse response sets are included for three relative source elevations (from placing the loudspeaker to varying heights on the same vertical axis) <ul> <li><strong>"_SrcHigh"</strong>: The source is located above the horizon of the receiver</li> <li>"<strong>_SrcEar"</strong>: The source and receiver are located at the same height</li> <li><strong>"_SrcLow"</strong>: The source is located below the horizon of the receiver</li> </ul> </li> <li>Additionally, anechoic impulse responses of the measurement loudspeaker and the utilized microphones are included <ul> <li><strong>"Anechoic_MicSMAnoTape_"</strong>: SMA measurement microphone without the applied tape (the source was compensated)</li> <li><strong>"Anechoic_MicSMAwithTape_"</strong>: SMA measurement microphone with the applied tape (the source was compensated)</li> <li><strong>"Anechoic_MicXMAmic19_"</strong>: XMA measurement microphone (the source was compensated)</li> <li><strong>"Anechoic_SrcFreeField_"</strong>: Measurement source (on-axis) (the influence of the utilized high-quality free-field measurement microphone can be neglected)</li> <li><strong>"Anechoic_SrcFreeField+MicSMAnoTape_"</strong>: Measurement source and SMA measurement microphone without the tape applied</li> <li><strong>"Anechoic_SrcFreeField+MicSMAwithTape_"</strong>: Measurement source and SMA measurement microphone with the tape applied</li> <li><strong>"Anechoic_SrcFreeField+MicXMAmic19_"</strong>: Measurement source and XMA measurement microphone</li> <li>Overall, the resulting impulse response sets contain the following compensations (including exact compensation of the phase/time behavior): <ul> <li>Anechoic KEMAR: Source</li> <li>Anechoic SMA/EMA/XMA: Source and array microphones</li> <li>Rooms KEMAR: None</li> <li>Rooms SMA/EMA/XMA: Array microphones</li> <li>There is the option to compensate for the source's on-axis response in the room measurement data. However, the direction-dependent directivity of the loudspeaker cannot be compensated. Therefore, we decided not to compensate for the source in the room measurement data since the on-axis frequency response of the utilized loudspeaker is reasonably flat.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>===========<br> DATA_RAW<br>===========</strong></p> <p><strong>This archive is too large to be uploaded to Zotero (around 77.5 GB). Please get in touch with the authors to request the data.</strong></p> <p>The archive contains the raw acoustic data of all measurement configurations captured by the measurement scripts (see section <strong>CODE_AND_PLOTS</strong>). The data yields the final impulse responses (see section <strong>DATA</strong>), as described in this section.</p> <p>Directory "resources/ARIR_raw/":</p> <ul> <li>Subdirectories by room and source position containing the raw SMA, XMA, and KEMAR acoustic measurement data</li> <li>In proprietary Matlab format, separate for every measurement position of each configuration</li> <li>Each data file contains extensive metadata, e.g., describing the utilized hardware devices, input/output ports, and descriptions.</li> <li>Each data file contains the raw utilized exponential sweep signal and the resulting captured microphone signals. Each impulse response may be recomputed with alternative deconvolution and post-processing parameters.</li> </ul> <p>Directory "resources/ARIR_raw/Logs_temp_humidity/":</p> <ul> <li>Air temperature and humidity data were captured in 5-second intervals during all acoustic measurements</li> <li>In CSV format (automatically loaded and included in the final impulse response sets as part of the measurement post-processing; see section <strong>CODE_AND_PLOTS</strong>)</li> <li>This data is not further utilized at the moment but seemed worthwhile to capture since some acoustic measurements (particularly the high-resolution SMA data sets) were conducted over multiple hours.</li> </ul> <p> </p> <p><strong>==================<br> CODE_AND_PLOTS<br>==================</strong></p> <p>This archive contains the code required to gather the raw acoustic measurement data (see section <strong>DATA_RAW</strong>), the code to post-process and yield the final impulse response data (see section <strong>DATA</strong>), and the resulting plots as described in this section.</p> <p>Directory "dependencies/":</p> <ul> <li>Matlab and Python functions that are utilized in the code</li> <li>Additional dependencies of available open-source projects may be required for certain code functions. If so, the source and setup process for the necessary dependencies are documented in the code header.</li> </ul> <p>Directory "plots/":</p> <ul> <li>Plots that were exported (and that may be regenerated) by the following scripts to validate different stages of the data simulation, measurement, and subsampling.</li> </ul> <p>Shell script "x1_Start_Jupyter.sh":</p> <ul> <li>Prepare a Python environment with the required tools described as dependencies.</li> <li>Activate the prepared Python environment to perform impulse response measurements using Jupyter Notebooks setup for different acoustic settings.</li> </ul> <p>Python Jupyter notebook "x1a_Measure_Microphones.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Perform a series of acoustic measurements of all utilized microphones in an anechoic environment.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Python Jupyter notebook "x1b_Measure_BRIRs.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a horizontal grid of measurement orientations for the VariSphear turntable according to the desired dummy head orientations.</li> <li>Perform a series of acoustic measurements of the dummy head at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Python Jupyter notebook "x1c_Measure_SMAs.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a spherical grid of measurement orientations for the VariSphear turntable according to the desired SMA sampling grid.</li> <li>Perform a series of acoustic measurements of the SMA microphone at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Python Jupyter notebook "x1d_Measure_XMAs.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a horizontal grid of measurement orientations for the VariSphear turntable according to the desired scattering body orientations.</li> <li>Perform a series of acoustic measurements of the XMA microphones at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Matlab script "x1e_Simulate_SMAs.m":</p> <ul> <li>Simulate a plane wave impinging from an arbitrary direction on SMAs and EMAs with a desired sampling grid in an anechoic environment.</li> <li>The simulations are helpful to evaluate the rendering method and to investigate the influence of different sampling grids and equalization methods on the rendered binaural signals.</li> </ul> <p>Matlab script "x2_Gather_And_Plot_Measurements.m":</p> <ul> <li>Gather the stored single files with individually measured impulse responses and the according metadata into a combined data set.</li> <li>The initial impulse responses can be recomputed with pre- and post-processing parameters tuned towards the specific acoustic scenario, including compensation of provided source and receiver impulse responses.</li> <li>Many plots may be generated during the processing to validate the input and output data.</li> </ul> <p>Matlab script "x2a_Compare_Measurement_Lengths.m":</p> <ul> <li>Compare the length of the resulting impulse responses of designated measurement configurations.</li> <li>This may be helpful for the tuning of pre-processing and post-processing parameters of the measured impulse responses.</li> </ul> <p>Matlab script "x3_Subsample_Measurements.m":</p> <ul> <li>Spatially subsample a high-resolution directional impulse response data set into a different (lower-resolution) sampling grid in the spherical harmonics domain.</li> <li>This is suitable for array and HRIR data sets.</li> <li>The script also compares the subsampled data against a reference set if available. In the current data set, an evaluation is performed for an anechoic simulation and a room measurement of an SMA at SH order 8.</li> </ul> <p>Matlab script "x3a_Gather_XMA_Measurements.m":</p> <ul> <li> <p>Transform anechoic XMA measurement data from SOFA into the data format required by the processing scripts to calculate the respective calibration and equalization filters.</p> </li> </ul> <p>Readme file "x3b_Generate_XMA_Filters.txt":</p> <ul> <li>The code for this functionality follows the publication [1] but is currently not polished enough for publication. Please contact Jens Ahrens (jens.ahrens@chalmers.se) for questions regarding this functionality.</li> <li>[1] J. Ahrens, H. Helmholz, D. Lou Alon, and S. V. Amengual Garí, “Spherical Harmonic Decomposition of a Sound Field Using Microphones on a Circumferential Contour Around a Non-Spherical Baffle,” <em>IEEE/ACM Trans. Audio, Speech, Lang. Process.</em>, vol. 30, pp. 3110–3119, 2022, doi: 10.1109/TASLP.2022.3209940.</li> </ul> <p>Matlab script "x3c_Gather_XMA_Filters.m":</p> <ul> <li>Rename the files containing the computed calibration and equalization filters into a suitable convention for this collection of scripts.</li> <li>The generated name includes an incremental index to track different versions of provided filter sets.</li> </ul> <p>Matlab script "x3d_Compare_XMA_Filters.m":</p> <ul> <li> <p>Generate various time domain and frequency domain plots to compare different versions of the generated XMA calibration and equalization filters.</p> </li> </ul> <p> </p> <p><strong>================<br> DOCUMENTATION<br>================</strong></p> <p>This archive contains additional documentation of the setups and processes while conducting the acoustic measurements, as described in this section.</p> <p>Directory "documentation/":</p> <ul> <li>Various photographs of the different room, source, and receiver arrangements of the data set</li> <li>The room dimensions and source and receiver positions are documented in the form of the original measurement notes (this may be improved in the future).</li> </ul> <p> </p>
Experiment resources for "Quality of Binaural Rendering From Baffled Microphone Arrays Evaluated Without an Explicit Reference"
<div>This data set contains the following resources to reproduce the listening experiment and statistical analysis of the referenced manuscript:</div> <div> <ul> <li>The <em>binaural room impulse responses</em> (<strong>BRIR</strong>s) of all listening conditions presented in the perceptual experiment.</li> <li>The tools to create the listening test infrastructure, including <em>Pure Data</em> (<strong>Pd</strong>) patches and configuration files for the <em>SoundScape Renderer</em> (<strong>SSR</strong>) and <em>graphical user interface</em> (<strong>GUI</strong>).</li> <li>The raw response data as gathered from the experiment subjects.</li> <li>The R and Stan scripts to perform the statistical analysis and generate the resulting plots and tables.</li> </ul> </div> <div> <p> </p> <p>The archive contains the following components described below.</p> <p>Directory "dependencies/":</p> <ul> <li>Matlab, R, Stan, and Pd functions that are utilized in the code and experimental setup</li> <li>Additional dependencies of available open-source projects may be required for certain code functions. If so, the source and setup process for the necessary dependencies are documented in the file header.</li> </ul> </div> <div> <p>Directory "plots/4_Equalization/":</p> <ul> <li>Plots of all available headphone equalization filters as generated by the following Matlab scripts.</li> </ul> <p>Directory "plots/8_User_study/":</p> <ul> <li>Plots of the raw and analyzed experimental results as generated by the following Matlab and R scripts.</li> </ul> </div> <div> <p>Directory "resources/BRIR_auralization/":</p> <ul> <li>Audio files with static binaural auralizations of all listening conditions presented in the perceptual experiment as generated by the following Matlab scripts.</li> <li>The files in "Kemar_HRTF_sofa_N44_adjusted" do not include a headphone equalization.</li> <li>The files in "Kemar_HRTF_sofa_N44_adjusted+Sennheiser_HD650_lin" include the equalization for the <em>Sennheiser HD650</em> headphones employed in the listening experiment. The files are identical to the annotated <a href="http://www.ta.chalmers.se/research/audio-technology-group/audio-examples/jaes-2024a/" target="_blank" rel="noopener">listening examples</a> published for the manuscript.</li> </ul> <p>Directory "resources/BRIR_rendered/":</p> <ul> <li>BRIRs and rendering parameters of all listening conditions presented in the perceptual experiment as generated from the associated <a href="../doi/10.5281/zenodo.8206570" target="_blank" rel="noopener">data set</a> and <a href="https://github.com/HaHeho/baffled-arrays-to-binaural/releases/tag/v2024.JAES" target="_blank" rel="noopener">rendering code</a>.</li> <li>Scene configuration files for the SSR with all listening conditions presented in the perceptual experiment as generated from the following Matlab scripts.</li> </ul> <p>Directory "resources/HPCF_KEMAR/":</p> <ul> <li>Impulse responses of equalization filters for various headphones on the G.R.A.S KEMAR acoustic dummy head as measured for this experiment.</li> </ul> <p>Directory "resources/User_study/":</p> <ul> <li>Various resources for the listening experiment.</li> <li>The files in "Exp1_analysis" include intermediate and final statistical analysis results as generated from the following R scripts.</li> <li>"Exp1_config.json" contains the configuration of the study GUI with conditions presented in the listening experiment.</li> <li>"Exp1_Introduction.pdf" contains the instructions presented to the subjects at the start of the listening experiment.</li> <li>"Exp1_Part2_data_strings.xls" contains all subjects' raw perceptual response data gathered from the listening experiment.</li> <li>"Questionnaire.pdf" contains the questionnaire given to the subjects at the end of the listening experiment.</li> </ul> <p>Matlab script "x4_Gather_Headphone_Compensations.m":</p> <ul> <li>Generate plots of measured headphone compensation filters. Furthermore, the generated minimum phase filters and filters to yield a linear phase response from the headphones are extracted as separate WAV files.</li> </ul> </div> <div> <p>Matlab script "x6_Gather_SSR_Configurations.m":</p> <ul> <li>Collect several specified pre-rendered binaural room impulse response sets into an ASD file. The SSR can load this scene to present all gathered configurations in direct comparison with head tracking.</li> </ul> </div> <div> <p>Readme file "x6a_Normalize_SSR_Loudnesses.txt":</p> </div> <div> <div> <div> <ul> <li>Ideally, the rendering script would implement a measure to provide a reliable estimation of the binaural loudness of the rendered configuration. This could be used to normalize all stimuli levels. However, such a measure is currently not available or implemented.</li> <li>Therefore, tuning the stimuli loudness for the user study was performed beforehand by ear. The adjusted playback levels are set in a modified SSR configuration file for the listening experiment.</li> </ul> </div> <div> <p>Matlab script "x6_Gather_SSR_Configurations.m":</p> <ul> <li>Perform convolution of (rendered) binaural room impulse responses with a source audio signal. This is done for a specified selection of static head orientations and a continuous rotation over all horizontal head orientations.</li> <li>The resulting auralizations are published as <a href="http://www.ta.chalmers.se/research/audio-technology-group/audio-examples/jaes-2024a/">supplementary materials</a> to the manuscript.</li> </ul> <p>Shell script "x7_Start_Study_GUI.sh":</p> <ul> <li>Initialize all required components to perform the perceptual user study, including: <ul> <li>SSR to perform the real-time rendering of the BRIRs with head tracking</li> <li>SSR to extract head-tracking data (in case a Polhemus tracker is used)</li> <li>Pd to extract head-tracking data (in case a Supperware tracker is used)</li> <li>Pd to perform real-time convolution to apply headphone compensation</li> <li>Pd to receive OSC messages from the study GUI</li> <li>Pd to trigger audio file playback from received OSC messages</li> <li>Pd to translate OSC messages into FUDI messages for the SSR</li> <li>The GUI to be used by the participants and implement the study procedure</li> </ul> </li> <li>Some static configuration variables can be adjusted, whereas other parameters are chosen during script execution.</li> </ul> <p>Matlab script "x8_Gather_Study_Data.m":</p> <ul> <li>Transform the raw result data from the questionnaire (*.xls) and the study GUI (*.json) into a compact format (*.xls) that can be loaded to plot the raw data and imported by software for the subsequent statistical analysis.</li> <li>Note that this contains the responses from all subjects, whereas responses from the investigators must be excluded from the statistical analysis (which is implemented in the analysis scripts).</li> </ul> <p>Matlab script "x8a_Plot_Study_Data.m":</p> <ul> <li>Generate a set of violin plots to visualize the initial distribution of the raw perceptual data. The data is split by specified attributes and plotted separately for visual inspection.</li> <li>The data may also be transformed into ranks for a first distribution inspection. Note that implementing the ranking method, notably how ties are resolved, may differ from the technique employed in the statistical analysis.</li> <li>Note that this contains the responses from all subjects, whereas responses from the investigators must be excluded from the statistical analysis (which is implemented in the analysis scripts).</li> </ul> <p>R script "x8b_Analyze_Exp1_Data.R":</p> <ul> <li>Perform the statistical analysis by transforming the observed subject ratings into a predicted distribution of ranks using a hierarchical generalized linear regression model.</li> <li>Executing the statistical model may take some time due to the Bayesian framework employing Markov-chain Monte Carlo simulations.</li> <li>Data is exported at various intermediate steps to be loaded and visualized by the following R script.</li> </ul> <p>R markdown script "x8c_Plot_Exp1_Results.Rmd":</p> <ul> <li>Generate various plots and data tables of the observed data and the predicted results to visualize the distribution and influence of different analysis parameters.</li> <li>Some of the resulting plots were used in the manuscript.</li> <li>"x8c_Plot_Exp1_Results.html" conveniently summarizes all plots and data tables generated by "knitting" the R markdown script.</li> </ul> </div> </div> </div>
TAU Spatial Sound Events 2019 - Ambisonic and Microphone Array, Evaluation Datasets
<p>This package consists of two evaluation datasets, <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong>. These datasets contain recordings from an identical scene, with <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> providing four-channel First-Order Ambisonic (FOA) recordings while <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> provides four-channel directional microphone recordings from a tetrahedral array configuration. Both formats are extracted from the same microphone array. The recordings in the two datasets consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. These evaluation datasets are part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a>. The corresponding development datasets can be downloaded <a href="https://doi.org/10.5281/zenodo.2599196">here</a>.</p> <p>The IRs were collected in Finland by Tampere University between 12/2017 - 06/2018. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.</p> <ul> <li>The <strong>foa_eval.zip</strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> evaluation dataset.</li> <li>The <strong>mic_eval.zip</strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> evaluation dataset.</li> </ul> <p>-- Version 2 updates --</p> <p>The<a href="http://dcase.community/challenge2019/task-sound-event-localization-and-detection-results"> DCASE 2019 sound event localization and detection task has now ended</a>. Hence we are releasing the reference labels for the evaluation dataset in this version.</p> <ul> <li>The <strong><em>metadata_eval.zip</em></strong> is the common metadata for both <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> evaluation datasets. </li> <li>The <strong>short2longnames.txt</strong> file consists of the corresponding names for each recording in the dataset in the <a href="http://dcase.community/challenge2019/task-sound-event-localization-and-detection#development-dataset">development-set format</a>, i.e., including the information of the impulse response location and the maximum number of overlapping sound events in the recording.</li> </ul> <p>Download the zip files corresponding to the dataset of interest and use your favorite compression tool to unzip these split zip files.<br> </p> <p> </p> <p> </p>
Microphone Comparison Array Validation Dataset
<p>Data generated as part of research to determine an appropriate method to make audio recordings suitable for use in comparing the perceptual characteristics imparted by microphones. Data comprise audio files, listening test interfaces and MATLAB code.</p> <p><strong>References</strong></p> <p>BBC SNN (2015): A.Pearce. T.Brookes, M.Dewhirst, "Timbral differences between microphones", BBC Sound Now & Next Technology Fair, London, UK, 19-20 May 2015</p> <p>AES139 (2015): A.Pearce. T.Brookes, M.Dewhirst, "Validation of experimental methods to record stimuli for microphone comparisons", Audio Eng.Soc. 139th Convention, New York, USA, 29 Oct - 1 Nov 2015</p>
HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays
<p>In this paper, we present HOMULA-RIR, a dataset of room impulse responses (RIRs) acquired using both higher-order microphones (HOMs) and a uniform linear array (ULA), in order to model a remote attendance teleconferencing scenario. Specifically, measurements were performed in a seminar room, where a 64-microphone ULA was used as a multichannel audio acquisition system in the proximity of the speakers, while HOMs were used to model 25 attendees actually present in the seminar room. The HOMs cover a wide area of the room, making the dataset suitable also for applications of virtual acoustics. Through the measurement of the reverberation time and clarity index, and sample applications such as source localization and separation we demonstrate the effectiveness of the HOMULA-RIR dataset.</p>
TAU Spatial Sound Events 2019 - Ambisonic and Microphone Array, Development Datasets
<p>This package consists of two development datasets, <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong>. These datasets contain recordings from an identical scene, with <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> providing four-channel First-Order Ambisonic (FOA) recordings while <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> provides four-channel directional microphone recordings from a tetrahedral array configuration. Both formats are extracted from the same microphone array. The recordings in the two datasets consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. These development datasets are part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a>.</p> <p>Both the development set consists of 400, one minute long recordings sampled at 48000 Hz, and divided into four cross-validation splits of 100 recordings each. These recordings were synthesized using spatial room impulse response (IRs) collected from five indoor locations, at 504 unique combinations of azimuth-elevation-distance. Furthermore, in order to synthesize the recordings, the collected IRs were convolved with <a href="http://www.cs.tut.fi/sgn/arg/dcase2016/task-sound-event-detection-in-synthetic-audio#audio-dataset">isolated sound events dataset from DCASE 2016 task 2</a>. Finally, to create a realistic sound scene recording, natural ambient noise collected in the IR recording locations was added to the synthesized recordings such that the average SNR of the sound events was 30 dB.</p> <p>The IRs were collected in Finland by Tampere University between 12/2017 - 06/2018. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.</p> <p><strong>Download instructions</strong></p> <p>The three files, <strong><em>foa_dev.z01</em></strong>,<strong><em> foa_dev.z02</em></strong> and <strong><em>foa_dev.zip</em></strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> development dataset.<br> The two files, <strong><em>mic_dev.z01</em></strong> and, <strong><em>mic_dev.zip</em></strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> development dataset.<br> The <strong><em>metadata_dev.zip</em></strong> is the common metadata for both <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> development datasets.</p> <p>Download the zip files corresponding to the dataset of interest and use your favorite compression tool to unzip these split zip files.<br> </p>
Data accompanying "Calibrated microphone array recordings reveal that a gleaning bat emits low-intensity echolocation calls even in open-space habitat"
<p>Introduction to the dataset: </p> <p>In this manuscript, we analyse recordings of brown long-eared bats in the wild collected with a 4-microphone array, which we use to measure the distance between the bat and the microphone.<br> The distance is then used to correct the recordings for distance-dependent frequency and amplitude attenuation.<br> To ensure that our measurements and corrections are correct, we fully calibrated the microphone array and analysis set-up. <br> The calibration includes a comparison between real distances and calculated distances between a loudspeaker and the microphone array as well as comparisons between the real amplitude and the calculated amplitude of the loudspeaker.<br> To obtain the original amplitude of the calibration recording, we calibrated the loudspeaker as well</p> <p>In this folder, we provide the original bat recordings as well as the datasheet with all analysed call measurements, and the original data from the microphone calibration.</p> <p>DATA & FILE OVERVIEW</p> <p>Folder list: <br> - Bat data: folder containing the original Plecotus recordings and raw call parameters<br> - Calibration data : folder containing the array calibration raw data, as well as codes and recordings necessary to calibrate the loudspeaker. <br> </p>
Data from: A spatiotemporal analysis of acoustic interactions between great reed warblers (Acrocephalus arundinaceus) using microphone arrays and robot audition software HARK
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