# Target Selection Strategies for Demucs-Based Speech Enhancement
## 1 Introduction  
When capturing speech in real-life scenarios, other audio sources are expected to also be present in the acoustic scene, resulting in the capture of a mixture of both target source and these other audio sources (also known as interferences and noise). It is then of interest to separate the speech source of interest by removing these other audio sources from the captured mixture using \"speech enhancement\" techniques, which recently have been carried out by means of deep learning with impressive results [1]. When carried out offline (i.e., by the use of previously captured audio recordings), several areas of applications have benefited, such as security [2] and music recording [3, 4]. Furthermore, it is also of interest to carry out speech enhancement in an online manner (i.e., by the use of live audio capture). It is important to clarify that we differentiate between running a speech enhancement technique in an online manner and running it in \"real-time\". While the former only explicitly requires that the computation time is less than the capture time, the latter also employs additional application-dependent latency or response time requirements [5, 6]. There are a varied set of applications that are to benefit from online speech enhancement, such as real-time automatic speech recognition [7], sound source localization in service robotics [8], hearing prosthesis [9], mobile telecommunication [10], and teleconferencing [11]. It would be difficult to impose additional response time requirements in a general sense to all theseapplications. Thus, we chose to employ only the computation-time-less-than-capture-time requirement for this work. The Demucs-Denoiser model [12] is an online speech enhancement technique that has been recently shown to outperform various other state-of-the-art techniques [13]. It not only provided a good enhancement performance (output signal-to-interference ratio above 20 dB in most cases), but was able to do so with very small window segments (64 ms). This makes it a very good representative of the current state of the art in online speech enhancement. However, in the same manner as any other speech enhancement technique, the Demucs-Denoiser model bears an important limiting assumption: that there is only one speech source present in the mix. In applications such as mobile telecommunication [10] and some teleconferencing scenarios [11], this may not be an such an issue given their one-user-at-a-time nature. Nevertheless, in applications such as service robotics [8] and hearing prosthesis [9], where more than one speech source is expected to be present, the use of speech enhancement techniques may not be appropriate. If more than one speech source is present, another set of techniques, known as \"speech separation\" [14], could be applied, since they aim to separate the various speech sources present in the mix into their own separate audio stream. However, speech separation techniques are not known to be able to be run in an online manner while speech enhancement techniques can [13]. It has been shown that when feeding a mixture of speech sources to current online speech enhancement techniques, some separate the speech mixture from the rest of the nonspech sources, while others (like the Demucs-Denoiser model) seem to separate the \"loudest\" speech source [13]. It is of interest, then, to carry out target selection as part of the speech enhancement process, such that the speech source of interest is the one being enhanced, while considering the rest of the speech sources as interferences. To this effect, two target selection strategies are proposed in this work:  
1. Embedding-based: A type of voice identity signature is computed from a previously captured audio recording of the target speech source. This, in turn, is used as part of the Demucs-Denoiser model to \"nudge\" its enhancement efforts towards the target speech source. 2. Location-based: The target speech source is assumed to be located in front of a two-microphone array.