## 2 Materials and Methods
### Preparation of the Dataset
Right image shows the selected positions of the 2 sources and 25 receivers respectively in the virtual room. Odeon has the ability to import recorded pulses and compare them with simulated ones. The accuracy of the results depends on the precise choice of the surface absorption coefficients, and initially the results varied considerably. Another Odeon tool, \"Genetic Material Optimizer\", was then used [23]. It compares the characteristics of the recorded and simulated pulses and tries to recalculate the possible absorption coefficients. Before running the algorithm, it is necessary to select the permissible limits of variation of the absorption coefficient for each material. For porous bricks and plasterboard, we have set a higher modification limit. These materials cover 3 walls and the ceiling; in general, most of the room surface. We can see that the algorithm only slightly changed the absorption coefficient of the materials with a modification limit of 50%, whereas the absorption coefficient of the materials with a higher modification limit was changed in detail. The differences between the recorded and simulated impulses are evaluated by the JND (just-noticeable difference) value [24], which is also described in the ISO standard and corresponds to 1 dB for most acoustic evaluation parameters. This means that if the difference between the impulses is less than 1 JND, it can be assumed to be negligible and can be ignored. Before the algorithm was run, this value ranged from 13 to 15 JND in the individual frequency bands; after optimization it ranged from 0.7 to 3. Only in the lower frequency bands did the differences remain larger, but the developers of Odeon warn the user that the algorithm is not able to reduce the differences to below 1 JND in the lower frequency bands. Once the absorption coefficients have been optimized and the differences between the simulated and recorded impulse parameters have been verified to be within acceptable limits, it can be said that we have simulated the acoustics of a virtual room that closely matches the acoustics of a real room. In this case, we can create RIRs not only for the 3 fixed measurement locations but also for any point in the virtual room. In Figure 3 a comparison of the measured and simulated RIRs can be seen in terms of the early decay time before and after optimisation of the absorption coefficients. Figure 4 shows  
Figure 2: Comparison of the directionality of the human voice in different vowels (**a**–**h**) with the directionality of the Genelec 8010A loudspeaker used in the measurements (**i**–**l**), in the horizontal ((**a**–**d**) and red line in (**i**–**l**)) and in the vertical planes ((**e**–**h**) and blue line in (**i**–**l**)) in different bands: 1 kHz (**a**,**e**,**i**), 2 kHz (**b**,**f**,**j**), 4 kHz (**c**,**g**,**k**), and 8 kHz (**d**,**h**,**l**). the similarity of the spectrum of the human voice when such a signal is convolved with a measured or simulated RIR impulse. Using the methodology described above, 50 RIRs were created for this study from 2 source positions and 25 receiver positions spaced 0.5 m apart. Using our calibrated virtual room model, we can create a larger dataset if necessary. Most importantly, the simulation is realistic, validated by real records. This makes any new study more valuable, as newly implemented models can be trained and tested on real acoustic behaviour, rather than on a dataset that is usually built using simplified models in an environment that will never be close to a real room. The latest version of the described dataset and more detailed technical information can be found in [[https://github.com/tamulionism/Room-Impulse-Response-dataset](https://github.com/tamulionism/Room-Impulse-Response-dataset)]([https://github.com/tamulionism/Room-Impulse-Response-dataset](https://github.com/tamulionism/Room-Impulse-Response-dataset)), accessed on 30 April 2023.