## 4 Experiments  
The experiments were conducted in a controlled environment where anechoic speech signals were convolved with real-recorded impulse responses (available at [[https://www.iks.rwth-aachen.de/en/research/tools-downloads/databases](https://www.iks.rwth-aachen.de/en/research/tools-downloads/databases)]([https://www.iks.rwth-aachen.de/en/research/tools-downloads/databases](https://www.iks.rwth-aachen.de/en/research/tools-downloads/databases))). The speech signals were from the Wall Street Journal (WSJ) corpus [34] and sampled at 16 kHz. The impulse responses were measured in a room with a configurable reverberation level. The reverberation times were set to be 160 ms, 360 ms, and 610 ms. A linear microphone array configuration of eight microphones was used with microphone spacing 3-3-3-8-3-3 cm. The source position was set to be 1 m or 2 m away in the azimuth range of \([-90^{\circ},90^{\circ}]\) in a 15\({}^{\circ}\) step. An illustration of the setup is shown in Figure 3.  
To generate the test data, speech signals were first randomly drawn from the WSJ development set and the evaluation set. They were truncated to 1 \(s\) in length for evaluation. The target speaker and one potential interfering speaker were then randomly placed at the _black dot_ positions, as shown in Figure 3, while it was ensured that they were not in the same direction. The microphone observations were a superposition of the reverberant target speaker, reverberant interfering speaker, and background noise. Two types of noise were tested, namely white Gaussian Noise (N0) and spatially-diffuse Noise (N1). The Signal-to-Interference Ratios (SIRs) were set to be \(-5\) dB, 0 dB, and 5 dB. The SIRs were set such that the signal powers of the target speaker were respectively weaker than, equal to, and stronger than that of the competing interference. The Signal-to-Noise Ratios (SNRs) were set to be 20 dB and 30 dB. For each test case, we ran 200 simulations, and we report the results in two different metrics: the Gross  
Figure 3: The evaluation setup. The black dots mark the positions where the impulse responses are measured.  
Figure 2: The processing pipeline of the proposed method.  
Error Rate (GER, in %) and the Mean Absolute Error (MAE, in \({}^{\circ}\)). The GER measures the percentage of DoA estimations whose error is larger than a threshold of \(5^{\circ}\), and the MAE measures the average estimation bias [24].  
For training the target time-frequency selection BLSTM, we used speech signals from the WSJ training set and generated reverberant mixtures with random room and random microphone-to-source distance configurations, following a similar procedure as in [35]. The simulated impulse responses were obtained using a fast implementation of the image source method [36]. In total, there were 20,000 utterances generated for training. The reverberant target speech was used as the reference, and the training target was defined as in Equation (7). The training procedure followed that as described in Section 3.1. For the input to the auxiliary network, a 10-s anechoic utterance containing only the target speaker was applied, which was found to be beneficial for speaker adaptation in the speech recognition task [37]. Note that the clean utterance did not overlap with the test utterances, and it was prepared in advance for each speaker and kept fixed all the time.