In　conventional　speech　enhancement　methods,　the　target　of　noise　mask　in　the　time-frequency　domain　is　based　on　deep　neural　networks　(DNN),　such　as　ideal　ratio　mask　and　phase-sensitive　mask,　in　which　they　do　not　consider　the　dependency　of　spectrum.　In　this　paper,　an　ideal　real-valued　ratio　mask　(IRVRM)　extraction　method　is　proposed　based　on　the　analysis-by-synthesis　(ABS)　for　utilizing　the　dependency　of　spectrum.　In　the　synthesis　process,　the　enhanced　speech　is　obtained　by　inverse　short-time　Fourier　transform　(ISTFT)　of　the　masked　spectrum,　whereas　in　the　analysis　process,　the　IRVRM　is　determined　by　maximizing　speech　quality　of　the　reconstructed　speech　from　mask　space.　The　ABS　loop　algorithm　is　proposed　to　reduce　computational　complexity,　namely,　the　best　mask　in　the　specifically　generated　subspace　is　conducted　in　each　loop.　After　the　ABS　loop,　the　approximated　IRVRM　is　conducted.　This　IRVRM　is　further　utilized　as　the　training　target　of　the　DNN.　The　experimental　results　show　that　when　the　extracted　IRVRM　with　the　ABS　loop　is　employed　as　the　training　target　of　the　DNN,　the　speech　quality　is　effectively　improved　in　the　DNN-based　noise　masking.　©　2022　Elsevier　B.V.