## 2 State of the Art  
Building on the foundational need to improve speech communication in noisy environments, as outlined in our introduction, a variety of models have been developed to address challenges in speech enhancement and background noise reduction. This section aims to review these related works, highlighting their contributions and limitations, to contextualize our own approach, which extends beyond mere noise removal to a nuanced understanding of how different types of noise uniquely impact speech quality. Some of these works apply techniques not encompassed in the deep learning field. Ref. [7] reduces the presence of background music over conversational audios. It usestrigonometric transformation and wavelet denoising techniques. In [8], the audio denoising is made with speech audios and noises like buzzing equipment or background noise from the street. Spectrograms form the training dataset and use a block thresholding estimation procedure. The same authors present, in [9], a similar approach to suppressing the harmonic noise of music by using its spectrograms. The method applies block attenuation techniques. Finally, ref. [10] implements a process to denoise speech audios containing slight background noises (which do not work with loud ones). It works with raw audio and applies Wiener filters, a well-known method of the previous age of audio denoising. The present work focuses on speech enhancement but uses deep learning techniques. The following studies make use of these methods in similar use cases. In [11], an autoencoder with a bottleneck that uses Recurrent Neural Networks (RNNs) addresses speech enhancement in recordings made with mobile phones. In [12], a U-Net model is applied directly to the audio waveform to restore audios in cases of background noise and loss of signal. The results are evaluated with a loss metric and by people listening to the audios. The use of Generative Adversarial Networks (GANs) for speech enhancement can be found in [13]. In this case, they used raw audios of 10 use cases (eight of them are genuine cases and two of them artificially created). Another interesting work is [14], which separates the different waves of raw audios with the voices of women and men mixed with songs. In this case, deep autoencoders are used to achieve the issue. To achieve a similar objective, ref. [15] addresses the field of deep learning-based speech enhancement techniques, focusing on their real-time applications. Evaluating three popular models in terms of signal processing metrics, such as a signal-to-interference ratio, response time, and memory usage, the research offers valuable insights into the online viability of these methods. Another distinguishing feature is the use of power log spectrograms to train the deep learning model, as in [16], that evaluate twelve deep learning models for single-channel ego-noise reduction on drones, exploring various domains and architectures. The findings highlight the superiority of U-Net models in the time-frequency complex domain, achieving substantial improvements in speech enhancement measures across low Signal-to-Noise ratio scenarios. In [17], a based convolutional system is developed to enhance spectral information by simultaneously utilizing multiple bandwidth spectrograms, specifically augmenting wider bandwidth (16 ms and 8 ms) spectrograms as auxiliary information. Experimental results on the VB dataset demonstrate that incorporating different bandwidth spectrograms provides supplementary information, resulting in an over 0.1 improvement, with the embedding dimension influencing the fusion strategy in the encoder. In [18], a Long Short-Term memory neural network (LSTM) is trained with frequency spectrograms to improve the speech quality in audios with background noises, reverberations, and a poor communication environment. Another work that uses power spectrograms is [19], which represents cases of passing cars and cafe babble noise. It uses a convolutional neural network in the approach.