Multimodal　emotion　recognition　in　conversations　aims　to　accurately　detect　emotions　by　integrating　audio,　text,　and　video　modalities,　playing　an　important　role　in　various　systems.　Existing　approaches　utilize　convolutional　and　recurrent　networks　to　learn　short-term　emotional　information　from　individual　modalities,　or　employ　graph　and　attention　mechanisms　to　integrate　long-term　emotional　information　from　multiple　modalities.　These　methods　effectively　combine　emotional　information　within　the　conversational　content　in　the　time　domain.However,　psychological　research　shows　that　emotional　information　are　not　only　conveyed　in　the　time　domain　but　also　in　the　frequency　domain　(e.g.,　pitch　and　speech　rate).　To　capture　emotions　from　a　more　comprehensive　perspective,　we　propose　TF-MERC,　a　framework　that　integrates　both　time　and　frequency　domains.TF-MERC　uses　a　multi-domain　alignment　module　to　learn　modality　information　within　the　time　or　frequency　domains.　It　then　employs　FATransformer　to　deeply　integrate　the　multimodal　associations　between　the　time　and　frequency　domains,　providing　a　more　comprehensive　approach　for　emotion　prediction.Experimental　results　show　that　TF-MERC　outperforms　state-of-the-art　methods,　achieving　superior　performance　across　multiple　datasets.　©　2025　ACM.