###### Abstract  
This paper discusses an algorithm that attempts to automatically calculate the effect of room reverberation by training a mathematical model based on a recurrent neural network on anechoic and reverberant sound samples. Modelling the room impulse response (RIR) recorded at a 44.1 kHz sampling rate using a system identification-based approach in the time domain, even with deep learning models, is prohibitively complex and it is almost impossible to automatically learn the parameters of the model for a reverberation time longer than 1 s. Therefore, this paper presents a method to model a reverberated audio signal in the frequency domain. To reduce complexity, the spectrum is analyzed on a logarithmic scale, based on the subjective characteristics of human hearing, by calculating 10 octaves in the range 20-20,000 Hz and dividing each octave by 1/3 or 1/12 of the bandwidth. This maintains equal resolution at high, mid, and low frequencies. The study examines three different recurrent network structures: LSTM, BiLSTM, and GRU, comparing the different sizes of the two hidden layers. The experimental study was carried out to compare the modelling when each octave of the spectrum is divided into a different number of bands, as well as to assess the feasibility of using a single model to predict the spectrum of a reverberated audio in adjacent frequency bands. The paper also presents and describes in detail a new RIR dataset that, although synthetic, is calibrated with recorded impulses. room reverberation; room impulse response; recurrent neural networks; audio signal spectrum; filter bank +
Footnote †: journal: Medical  
method was proposed and performed better than the standard MCC [9]. The RIR impulse is observed to be a sparse set of coefficients, i.e., some of its intermediate values are close to zero. On this basis, it was decided that the equalization process could be further improved if the adaptive algorithm took advantage of the sparseness of the RIR impulse [10]. To create the impression of realistic room acoustics as the listener's position varies in the virtual room, we need to continuously convolve an anechoic signal in real time with a different RIR filter from a large dataset. The dataset should consist of RIRs recorded in a real room, but this is time consuming, as each new RIR requires a new measurement when a new position is chosen for the sound source or receiver. This means that, for example, to capture a set of RIR data covering the entire area of a small room (up to 10 sq. m.), more than 1000 measurements may be required, with the position of the measuring microphone changing every 10 cm. In addition, a quiet environment is needed to ensure the quality of the RIR measurements. According to ISO 3382-1, the sound source must emit a sound pressure level at least 35 dB above the background noise in the room [11]. As an alternative to RIR measurements, RIR can be modeled using one of the geometric acoustics methods. The most commonly used one is the image source method (ISM) [12]. However, satisfactory results can only be achieved in this way by modelling an almost empty room with clear geometry (e.g., a rectangle). The ISM method is a simplified assumption that sound waves propagate in straight lines at a fixed speed, the energy is uniformly attenuated, and the waves are mirrored when they reach a surface. In the real world, the sound wave is not perfectly reflected; some of it is scattered in different directions, depending on the roughness of the surface. Only the early reflections are mirror-like, and later they become increasingly diffuse. Thus, in practice, a hybrid approach is often used, where the first reflections are modeled by an ISM and the later ones by the ray-tracing method. The ISM method also does not allow the modelling of objects in the room that interfere with the propagation of the sound wave and cause reflections. [PERSON] et al.