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Content and style representations have been widely studied in the field ofstyle transfer. In this paper, we propose a new loss function using speakercontent representation for audio source separation, and we call it speakerrepresentation loss. The objective is to extract the target speaker voice fromthe noisy input and also remove it from the residual components. Compared tothe conventional spectral reconstruction, our proposed framework maximizes theuse of target speaker information by minimizing the distance between thespeaker representations of reference and source separation output. We alsopropose triplet speaker representation loss as an additional criterion toremove the target speaker information from residual spectrogram output.VoiceFilter framework is adopted to evaluate source separation performanceusing the VCTK database, and we achieved improved performances compared to thebaseline loss function without any additional network parameters.

The sound of my voice: speaker representation loss for target voice separation