# Target Selection Strategies for Demucs-Based Speech Enhancement
## 8 Conclusions  
Speech enhancement has been improved thanks in great part from deep-learning-based techniques and recently has grown in interest to be run in an online manner. The Demucs-Denoiser model has been shown to be a prime representative of the current state of the art of online speech enhancement, but is still limited by the assumption of having only one speech source present in the input mixture. In this work, two target selection strategies were proposed and evaluated: one that uses an embedding calculated from a previously captured recording of the target speech source (Demucs-Embed), and another that assumesthat the target speech source is located in front of a two-microphone array (Demucs-PhaseBeamform). The results presented in this work point out that Demucs-Embed is effective in improving the original Demucs performance; however, it is not a considerable improvement (\(\sim\) 2 dB output SIR improvement in the best of cases), and it can only be appreciated in low-input-SIR scenarios. This is quite possibly because the sequence modeling stage of the Demucs-Denoiser model is not designed to carry out both target selection and speech enhancement. Thus, other neural architectures could be explored, such as bidirectional LSTM layers or self-attention mechanisms. However, such exploration (proposed here as future work) should be carried out considering approaches that do not increase the response time considerably. Demucs-PhaseBeamform consistently outperformed both Demucs-Embed and the original Demucs by a wide margin (by over 10 dB in low-input-SIR scenarios). Since other beamformers could be used, it would be of interest to explore their use as future work. However, if the target speech source moves from where Demucs-PhaseBeamform assumes it is located, its performance degrades considerably (\(>\) 5 dB drop in output SIR with a distance from the center of just 0.1 m). To avoid this issue, several substategies are worth evaluating in future work: (a) train the Demucs-Denoiser model with data that were created with artificial location deviations so as to make it robust against them; (b) the chosen beamformer has a calibration parameter (\(\sigma\), the phase difference threshold) that was established as static in this work and may provide higher tolerance by dynamically adjusting it via a given quality metric; and/or (c) incorporate a sound source localization mechanism as part of the strategy. Additionally, no experiments were carried out in scenarios where interferences are located behind the target speech source. In these cases, both the interferences and the target speech source bear the same direction of arrival to the microphone array and, thus, will not be diminished by the beamformer. Hopefully, since the interferences tend to be behind the target speech source, they are located further away and, thus, may be quieter in the mix, which may point to their removal by the Demucs-Denoiser model. These issues are worth exploring as future work by the authors. Conceptualization, C.R. and G.F.-P.; methodology, C.R. and G.F.-P.; software, C.R. ; validation, C.R. ; formal analysis, C.R. ; investigation, C.R. ; resources, C.R. and G.F.-P.; data curation, C.R. ; writing--original draft preparation, C.R. ; writing--review and editing, C.R. and G.F.-P.; visualization, C.R. ; supervision, C.R. and G.F.-P.; project administration, C.R. ; funding acquisition, C.R. and G.F.-P. All authors have read and agreed to the published version of the manuscript. This work was supported by PAPIIT-UNAM through the grant IA100222. Not applicable. The complete implementation of both strategies and trained weights, as well as dataset creation and evaluation methodologies, can be found at [[https://github.com/balkce/demucstargetsel](https://github.com/balkce/demucstargetsel)]([https://github.com/balkce/demucstargetsel](https://github.com/balkce/demucstargetsel)) (accessed on 12 June 2023).