## 1 Introduction  
Sound Source Localization (SSL) plays an important role in many signal processing applications, including robot audition [1], camera surveillance [2], and source separation [3]. Conventionally, SSL algorithms focus on the task of localizing all active sources in the environment and cannot distinguish a target speaker from competing speakers or directional noises. Nevertheless, there is one speaker of interest in particular that we want to pay attention to in some scenarios, such as when attending to a host speaker or in voice control systems tailored to a master speaker. Retrieving the location of this target speaker, which is defined as Target Speaker Localization (TSL), usually could not succeed without the help of speaker identification [4] or visual information [5]. Popular SSL algorithms are the Generalized Cross-Correlation with Phase Transform (GCC-PHAT) algorithm [6], the Steered Response Power (SRP)-PHAT algorithm [7], which is a generalization of GCC-PHAT for more than two microphones, and the Multiple Signal Classification (MUSIC) algorithm [8]. These algorithms estimate the Direction of Arrival (DoA) by detecting peaks in a so-called angular spectrum. To facilitate localization of speech sources in noisy and reverberant conditions, time-frequency bin weighting methods have been proposed to emphasize the speech components, especially the direct sound components, in the observed signals [9]. These techniques include those based on the signal power [10], the coherent-to-diffuse ratio [11], and the speech presence probability [12]. The motivation behind time-frequency weighting is the sparsity assumption of the signal spectra, or in other words, the assumption that only one source is active in each time frequency bin [13; 14]. As suggested in [15], even if the signal is severely corrupted by noises or interferences, there exist target-dominant time-frequency regions that are sufficient enough for localization. It is also found that the human auditory system may perform source separation jointly with source localization [16]. The same motivation lies behind some fairly recent studies that perform robust source localization based on time-frequency masking [17; 18; 19], in which the authors propose the usage of Deep Neural Networks (DNNs) to perform mask estimation first. The masks are used to weight the narrowband estimates, and the combined algorithms achieve superior performance in adverse environments. Given that mask estimation is a well-defined task in monaural speech separation and large progress has been made with DNN [20], the combination of mask and the SRP-PHAT (or the MUSIC) algorithm is natural and straightforward. Using neural networks, there are also other practices to localize speech sources directly through spatial classification [21; 22]. The methods discussed above are directly applicable to TSL if the target speaker is the only speech source in the environment. When a competing speaker exits, multiple source localization techniques [23] could be applied; however, the localization results remain non-discriminative, and further post-processing for speaker identification is needed. TSL in a multi-speaker environment surely requires prior information of the target, such as a keyword uttered by the speaker [24]. Nevertheless, the point is that TSL could be addressed from a different perspective by first performing target speaker separation before localization. The challenge hence is to separate the target speaker from noises and competing interferences. The previous time-frequency masking-based localization algorithms focus on separating speech sources from noise, while not all the speech-dominant bins would be representative of the target speaker. Specifically, in this paper, a speaker-aware DNN is designed to extract the target speaker-dominant time-frequency regions.