# Target Selection Strategies for Demucs-Based Speech Enhancement
## 3 Proposed Target Selection Strategies
### Location-Based Target Selection  
The location-based target selection strategy is summarized in Figure 5. As can be seen, two input signals (\(I_{1}\) and \(I_{2}\)) are captured from a two-microphone array. And in this case, \(I_{1}=X+N_{\Phi_{1}}+Y_{\Phi_{1}}\) and \(I_{2}=X+N_{\Phi_{2}}+Y_{\Phi_{2}}\), where \(X\) is still the source of interest, \(N\) is still the noise to be removed, and \(Y\) is still another speech source, but both \(N\) and \(Y\) arrive at different moments in time (represented by \(\Phi_{1}\) and \(\Phi_{2}\), respectively) at each microphone. It is assumed that the target speech source is positioned in front of the two-microphone array. A lightweight spatial filter (also known as a beamformer) steered towards that direction is used to amplify the presence of the target speech such that it is the loudest speech source in the output mixture. This is then fed to the Demucs-Denoiser model which has been shown to enhance the loudest speech source in a given mixture [13]. Thus, thanks to the amplification carried out by the beamformer, the result is that the the target speech source is enhanced. There are several beamforming techniques that can be used for this purpose, such as minimum variance distortionless response (MVDR) [33; 34] or generalized sidelobe canceller (GSC) [35; 36]. However, it has been shown [37] that a simple phase-based frequency masking (PFM) [38] is able to obtain a good amplification of the speech source at a given location while still being able to run in an online manner. Like any other beamformer, PFM relies on knowing a proti in what direction the target source is located relative to the microphone array. This is to calculate a series of phase shift weights that are applied to the input signals such that any information coming from the target direction is aligned and, thus, can be amplified by different means. In the case described in this work, such weights are not required to be calculated since the direction of the target source is assumed to be in front of the microphone array. Thus, the input signals are already aligned towards the target direction. Following this, a frequency binary mask (\(\Omega\)) is calculated by employing Equation (1). \[\Omega[f]= \begin{cases}1,&\text{if }|\phi(I_{1}[f])-\phi(I_{2}[f])|<\sigma\\ 0,&\text{otherwise}\end{cases}  \tag{1}\]  
where \(f\) is the frequency index (of \(F\) total frequency bins), \(I_{1}\) and \(I_{2}\) are the Fourier transform of the first and second input signals (respectively), the \(\phi(\cdot)\) function calculates the phase from a given frequency component, and \(|\cdot|\) is the absolute value mathematical operator. Finally, \(\sigma\) is the phase difference threshold, such that if the phase difference at frequency \(f\) is below it, it means that the frequency component at \(f\) belongs to the target source; otherwise, it belongs to an interference. Figure 5: Location-based target selection strategy. The beamformer output \(X_{PFM}\) is calculated using Equation (2). \[X_{PFM}[f]=I_{1}[f]\Omega[f] \tag{2}\]  
It is important to mention that the \(\sigma\) parameter needs to be tuned. Small values of \(\sigma\) may provide a better isolation of the target source, but this occurs at the expense of increased sensitivity to variation in the target location. Values between 10\({}^{\circ}\) and 30\({}^{\circ}\) have been shown to provide a good balance between these two issues [37]. Additionally, since the resulting frequency mask is binary, this beamformer introduces a considerable amount of discontinuities in the frequency domain. This, in turn, results in a considerable amount of musical artifacts in its time-domain output, which need to be removed by the subsequent Demucs-Denoiser model. This location-based target selection strategy is very similar to a previous work, described in [38], where the beamformer output as well as its counterpart (where the frequency mask is inverted to obtain an estimation of the interferences) were fed into a bidirectional LSTM network.