## 1 Introduction  
Although the quality used to store audio allows them to be very faithful to the originals, some information may be lost during transmission. In particular, we call noise a signal or set of signals that distort the wave that transmits the original sound. Within this concept, there can be artificial noises (those generated by the means of communication, such as interferences) or natural noises (those generated by the environment where the communication takes place). When the transmission is in noisy scenarios, ambient sounds may affect the intelligibility of the received message. This issue underscores the importance of addressing noise problems in audio, as highlighted in [1, 2], demonstrating the significant impact of noise on the fidelity of audio transmissions and the need for solutions to enhance the overall quality of communication. The motivation for this research arises from the critical need to enhance the clarity and intelligibility of speech in various communication settings, where background noise often compromises the quality of the transmitted audio. While existing noise reduction techniques have made strides in mitigating this issue, our work aims to develop a deep learning model specifically tailored to suppress background noise across a range of simulated scenarios. However, our objectives extend beyond mere noise elimination. We also seek to quantitatively assess the relative difficulty of filtering out different types of background noises present in the same audio fragment. By doing so, we aim to identify which types of noise have a more detrimental impact on speech quality, thereby creating a guide for future research and technological development in the field of audio signal processing. The real problem comes when the noise is equal to or stronger than the signal and causes its complete distortion. This fact opens the possibility of using techniques that can eliminate background noise for safer and more reliable transmissions, which is desirable in cases such as phone communications, especially in emergencies. Artificial intelligence has demonstrated its ability to remove noisy information from various formats, such as images or signals [3; 4]. Deep learning models have obtained the best results in recent years among all the artificial intelligence techniques. Deep learning was defined by [5] as models composed of multiple processing layers that learn representations of data with various levels of abstraction. These models have shown an exemplary performance in speech enhancement [6]. Audio signals can be analyzed either in the time domain or the frequency domain, with the latter often represented visually as images. Our approach capitalizes on the image-processing capabilities of convolutional layers in deep learning neural networks, specifically for tasks like cleaning and restoration. We have trained a deep learning model that can remove four types of background noise with pairs of original audio and audio mixed with background noise. Original audio signals have been transformed into a 2D representation by converting them into a power spectrogram. Following this transformation, we employ a two-dimensional Variational Autoencoder (VAE) to remove noise from the signal and reconstruct the clean audio. As no dataset solves the presented use cases, we have created our own. To carry out the model development and training, we have used a dataset with recordings of TED talks (TED-LIUM 3 dataset) as the expected output and the same dataset mixed with four different background noises (aircrafts, rain and thunderstorms, wind, and traffic) obtained from different specialized websites as the input to the network. The results show that our model performs better than other solutions in the state of the art. Regarding the proposed scenarios, although there are differences between them, we cannot assure that one of the background noises influences more than others.