# Options for Performing DNN-Based Causal Speech Denoising Using the U-Net Architecture +
## 1 Introduction  
Most recorded speech signals are affected by noise, which degrades quality and hinders intelligibility. Speech enhancement techniques aim to maximize the perceptual quality of speech signals disturbed by background noise and reverberation. Background noise may include environmental sounds and instrumental interference, while reverberation occurs due to reflections in the transmission path. Although both types of noise can coexist and complicate the denoising task, effective solutions are achievable using deep neural networks (DNN) [1, 2], which aim to predict clean speech from corrupted inputs. To this end, the scope of discussion in this study will be limited to speech denoising without losing generality.  
Speech denoising is crucial for applications such as audio and video calls, hearing aids, and automatic speech recognition systems. While traditional statistical signal processing approaches have addressed this problem for years, recent research has shifted towards machine learning techniques that learn from real-world data. Following the success of deep learning in various classification and regression tasks [3], there has been a growing interest in applying DNNs for speech denoising.  
The core concept of DNN-based speech denoising involves training models to learn the complex mapping from noise-corrupted speech representations to their clean counterparts.  
This kind of approach offers two significant advantages: (1) it operates without requiring knowledge of the statistical properties of the speech and noise, and (2) it can handle fast-varying non-stationary noise. Several effective DNN architectures have emerged, with the U-Net becoming a prominent choice for speech denoising. Initially designed for biomedical image segmentation [4], the U-Net has proven highly adaptable and effective for speech applications [2; 5; 6; 7; 8; 9]. Its U-shaped structure comprises an encoder and a decoder linked by a bottleneck. The encoder compresses input data into a lower-dimensional representation, capturing essential features, while the decoder reconstructs the data to its original dimensions with improved output. The U-Net architecture preserves high-resolution features, facilitates hierarchical feature extraction, supports efficient training, and enhances generalization, making it a powerful tool in DNN-based speech denoising.  
When employing U-Net for speech denoising, the input can be represented in various forms, such as time-domain waveforms or spectral transformations (STFT and STDCT). Previous studies often focused on a specific domain and adjusted the U-Net's structure for optimization. However, discussions on the applicability of a U-Net model across multiple domains remain scarce. This paper aims to develop a versatile U-Net model and evaluate its denoising efficacy on narrowband speech sampled at 8 kHz. Through comparative analyses of experimental results, we hope to identify the domain that offers the greatest advantages for effective speech denoising.  
The contributions of this paper are threefold. Firstly, we establish a comparison framework for assessing U-Net performance across different processing domains. Secondly, our examination of classical and advanced U-Nets justifies the design choices for layer configurations, facilitating a balance between model complexity and computational efficiency. Thirdly, upon identifying the optimal domain for DNN-based speech denoising, we explore the use of composite loss functions to enhance perceptual quality further.  
The remainder of this paper is structured as follows: Section 2 outlines recent technical developments in the field. Section 3 discusses the U-Net architecture for speech denoising, including network design, causality implementation, input arrangements, and loss functions across domains. Section 4 presents experimental settings and performance evaluations. Conclusions are drawn in Section 5.