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4 results for “spherical 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>
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>
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>
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