###### Abstract
proposed improvements using a Monte Carlo path-tracing method that can model diffuse reflections, which means better simulation of existing obstacles [13]. However, the authors point out that this algorithm also has the disadvantage of not being able to model low frequencies and diffraction well. There have been attempts to to solve the problem with the use of artificial neural networks which, trained on existing RIRs, can predict the desired data. The use of neural networks can be a more flexible approach and a good alternative to this task. The RIR can be estimated using its spectrogram as an image, as well as individual RIR parameters such as the geometry of the simulated room and the absorption coefficients of its surfaces. In the study by [PERSON] and [PERSON], the RIR parameters were estimated separately, with convolutional neural networks (CNNs) used for room geometry and feedforward multilayer perceptrons (MLPs) for surface absorption coefficients [14]. The authors claim that their method works when neural networks are trained with a single RIR impulse. In fact, it should be noted that this condition is only partially fulfilled, as the algorithm is initially allowed to learn from a single simulated RIR impulse that has been generated by the ISM method using the RIR generator [15]. Afterwards, it has been shown that much better results can be achieved by increasing the number of RIRs dedicated to training. In addition, the performance of the algorithm is tested by training the networks on the recorded RIRs. The BUT ReverbDB dataset is used for this purpose [16]. Machine learning methods are applied not only to RIR generation but also to other acoustic environment analysis tasks. Classification of rooms by volume using RIR can be performed using statistical pattern recognition [17]. The authors of this paper claim that their algorithm does not require data about the distance between the sound source and the microphone. However, good results were only achieved using simulated rather than measured RIRs. Convolutional neural networks are used to perform speech recognition tasks and to build speech-to-text models. In [18], the authors used a CNN-based approach to recognise tonal speech signals. Feature extraction was performed using Mel frequency cepstral coefficients (MFCC). Machine learning can also be used to assess the competence of psychotherapists by performing speech recognition from audio and text analysis from a report together. In [19], the possibility of determining the quality of a practitioner's performance by analysing audio recordings and transcripts of psychotherapeutic conversations and comparing the result with manual assessments of competency was explored. The best predictive performance was achieved by a Lasso regression model. In [20], the authors used time-domain features (MFCCT) in addition to MFCCs in speech emotion recognition (SER) to extract features from an audio signal. The CNN-based SER model outperformed comparable models that used non-hybrid features. Machine learning is also being applied in the field of tourism to generate additional recommendations for destinations with fewer reviews on specialised tourism portals. Missing reviews can be identified and selected from social media posts containing geolocation information. In [21], the authors used machine learning-based clustering and classification methods, namely a fine-tuned transformer neural network-based BERT model. In this paper, we present a new dataset for RIR estimation based on the fusion of recorded and simulated RIRs. In addition, we present a study of an alternative method for modeling the spectrum of a reverberated signal. The idea of this paper is to check if a neural network can learn the effects of acoustics and replace the traditional method of using RIR filters. We train the neural network with frequency-domain data, dividing logarithmically into 1/3 or 1/12 octave.