# Target Selection Strategies for Demucs-Based Speech Enhancement
## 7 Discussion  
As can be seen from the results shown in the previous section, Demucs-Embed is able to provide moderate improvements over the original Demucs (an increase of \(\sim\)2 dB output SIR); however, it does so in circumstances where the target speech and the interferences bear similar energy (\(<\)5 dB input SIR). In all other circumstances, the original Demucs consistently provides a better output SIR than when employing Demucs-Embed. Given that there is additional information that the Demucs-Denoiser model can use to select the target speech, an output SIR improvement over all circumstances was expected. Regarding why this is the case, the authors believe there are two possible reasons. First, the sequence modeling stage of the Demucs-Denoiser model is aimed specifically at speech enhancement, and it was designed as such using purely LSTM layers. This architecture may not be able to carry out both speech enhancement and target selection at the same time, which hints that other approaches or architectures for this stage should be explored in the future (some possible solutions are described in the following section). Second, it is important to remember that the original Demucs has been shown to perform well when one target speech source has a higher energy than the rest of the speech sources in the mix [13]. In these circumstances, no explicit target selection is actually carried out; thus, the sequence modeling stage of the original Demucs can focus solely on speech enhancement, while the sequence modeling stage of Demucs-Embed is still aiming to carry out both target selection and speech enhancement.  
Additionally, it is important to remind the reader that Demucs-Embed assumes that there is a previously captured recording of the target speech source at hand, so as to calculate the required embedding for this strategy. This may not be viable in some circumstances, although, to be fair, it could be trivially obtained with a brief human-computer interaction prior to running the speech enhancement.  
In any case, the results provide a strong incentive to only use Demucs-PhaseBeamform, since it not only provides a higher average performance compared to Demucs-Embed and the original Demucs, but also provides lower result variability. It should be noted that Demucs-PhaseBeamform also provides the potential to be used with a variety of microphone strategies, since it only requires a trivial reconfiguration of the beamformer to work. Only two microphones were considered here since, not only is it the lowest amount of microphones that could be used with this strategy (it can be expected that its performance will improve with a higher number of microphones), but currently a vast amount of teleconferencing and digital communication hardware employ a two-microphone array for their digital audio signal processing purposes. Indeed, the beamformer that precedes the Demucs-Denoiser model seems to be effectively supporting the Demucs-Denoiser model in several ways other than target selection: input SIR normalization, less result variability in high SNR scenarios, and even robustness against reverberation.  
However, all the benefits that Demucs-PhaseBeamform offers come with an important caveat: the target speech source needs to be located very close to its expected position. Although the strategy seems to \"tolerate\" slight location deviations and still provide a good enhancement result (\(>\)10 dB in output SIR), distances from the expected position as small as 0.05 m result in important performance degradation. Several substrategies could be used to avoid this issue as future work, and are commented on in the following section.