###### Abstract  
The measurement of reverberation time is an essential procedure for the characterization of the acoustic performance of rooms. The values returned by these measurements allow us to predict how the sound will be transformed by the walls and furnishings of the rooms. The measurement of the reverberation time is not an easy procedure to carry out and requires the use of a space in an exclusive way. In fact, it is necessary to use instruments that reproduce a sound source and instruments for recording the response of the space. In this work, an automatic procedure for estimating the reverberation time based on the use of artificial neural networks was developed. Previously selected sounds were played, and joint sound recordings were made. The recorded sounds were processed with the extraction of characteristics, then they were labeled by associating to each sound the value of the reverberation time in octave bands of that specific room. The obtained dataset was used as input for the training of an algorithm based on artificial neural networks. The results returned by the predictive model suggest using this methodology to estimate the reverberation time of any closed space, using simple audio recordings without having to perform standard measurements or calculate the integration explicitly. Keywords:revberation time; sound recording; sound fields; spatial coherence; artificial neural network; Levenberg-Marquardt algorithm; power spectral density; supervised learning +
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gradually reflected by the surfaces delimiting the space, in the simplified hypothesis of specular reflection. A more rigorous approach, on the other hand, analyzes the propagation of sounds from the wave point of view, considering the space as a complex system where many standing waves of different wavelengths, called vibration modes, can coexist. In general, when the dimensions of the space are comparable to the wavelengths of sound waves, a rigorous analysis cannot be ignored [5; 6]. The acoustic quality of confined spaces has been the subject of considerable attention for years, even if a single procedure for their design has not yet been reached. Investigations carried out by statistically elaborating subjective judgments of many listeners have shown that, beyond the subjective evaluations, there are some objective parameters of the acoustic field capable of acoustically characterizing the spaces. Over the years, numerous scholars have proposed various methods of determining these parameters, assigning each of them a different weight [7; 8; 9; 10; 11]. The first example in this sense was given by [PERSON] [12], at the beginning of the last century, that identified the reverberation time as a fundamental parameter, and studied its optimal values for the various types of music and provided a simple relationship for its calculation. The reverberation time was the main, if not the only, physical descriptor used as an objective parameter for evaluating the acoustic behavior of a room until the 1950s. A single value, for each frequency band, can be representative of the sound field of the entire room only if this is sufficiently reverberant (where the average absorption coefficient is less of 0.25 [13]) and if it is homogeneously so, otherwise a value should be calculated at each listening point. The reverberation time indicates the time, in seconds, required so that, in a defined position, in a confined space, the sound level is reduced by a defined amount compared to what it had in the instant prior to the interruption of the sound source [14]. The measurement of the reverberation time is not an easy procedure to carry out and requires the use of a space in an exclusive way. In fact, it is necessary to use instruments that reproduce a sound source and instruments for recording the response of the space. In this work, an automatic procedure for estimating the reverberation time based on the use of artificial neural networks was developed.