# Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask
## 4 The Proposed Method  
Acknowledging the complex-valued representation of speech signals in the spherical harmonic domain, our study introduces a novel MF-AMnet, as illustrated in Figure 1 to estimate DOAs. MF-AMnet uses the SHC feature of microphone array signals. These features are then processed by a series of key modules within the network. The network outputs unit vectors that represent the directions of the source signals. Special multi-scale fusion block and attention mask help to address the challenges posed by the nature of the complex-valued features, offering a promising way for sound source localization. AM module is a crucial component of MF-AMnet, depicted in Figure 2a. It operates on the input SHC and masks itself using a series of complex-valued convolutional layers (C-Conv). The output of AM is normalized through the application of the complex-valued sigmoid function, limiting the output to the interval \([0,1]\). The  
Figure 1: Overall diagram of MF-AMnet. element-wise multiplied by the original SHC to obtain the masked SHC. This mask amplifies significant spatial features while attenuating noise and interference. It is important to note that residual connections are used between the first and last C-Conv layers to facilitate information dissemination and enhance training process stability. The significance of the attention mechanism cannot be overlooked, as it enables the network to focus its computational resources on extracting and enhancing the spatial cues required for precise DOA estimation. AM serves as a pre-processing step for MF-AMnet. It filters out irrelevant information and amplifies signal components that indicate the location of sound sources. This enables the network to achieve robust and accurate DOA estimation capabilities. After the AM module, the multi-layer composite processing (MCP) module extracts information from the feature map comprehensively. This module is a crucial component and consists of three sub-modules: MF block, the channel attention mechanism (CAM), and a pooling operation. MF is essential for the network to distinguish complex spatial features, shown in Figure 2b. It processes the input feature map through a series of stacked C-Conv layers to further extract information in each layer. This overcomes the limitations of a single scale and allows for a global understanding of the input. Here, channel shuffle operation [46] is used to divide feature maps into two distinct sections, each containing unique information. One section undergoes further convolution processing to analyze the complexity of the spatial representation, while the other section is temporarily retained for subsequent fusion. This structure facilitates a thorough exploration of the feature map and enables the synergistic fusion of information collected at different scales. The shuffle operation enhances the flow and utilization of information, while also reducing channel dimensionality and the number of network parameters. Retained features after the final C-Conv layer are concatenated as the output of MF. MF uses C-Conv and fusion operations to help the network better understand the interrelationships within the input feature map. This multi-scale fusion strategy has low complexity and allows the network to efficiently learn spatial features at different scales, improving its ability to capture complex-valued spatial patterns in input signals. After MF, the feature map is fed into CAM [47]. It regulates the importance of individual channels in feature mapping dynamically and identifies their relevance to the underlying target estimation task. It amplifies channels that contain critical spatial information while attenuating irrelevant channels. This facilitates the formation of compact and significant representations that enhance the capabilities of the network to better overcome complex acoustic scenarios.