The present disclosure relates to an apparatus, that obtains a far-end signal and a near-end microphone signal, determines, based on at least the far-end signal, a far-end speech signal estimate and a far-end noise signal estimate, determines, based on at least the near-end microphone signal, a near-end microphone speech signal estimate and a near-end microphone noise signal estimate, determines, based on at least the far-end speech signal estimate and the near-end microphone speech signal estimate, a predicted near-end speech signal, determines, based on at least the far-end noise signal estimate and the near-end microphone noise signal estimate, a predicted near-end noise signal and outputs at least the predicted near-end speech signal and predicted near-end noise signal.

According to an example embodiment, a method (200) for audio-based monitoring is provided, the method (200) comprising: deriving (202), via usage of a predefined conversion model (M), based on audio data that represents sounds captured in a monitored space, one or more audio features that are descriptive of at least one characteristic of said sounds; identifying (204) respective occurrences of one or more predefined acoustic events in said space based on the one or more audio features; and carrying out (206), in response to identifying an occurrence of at least one of said one or more predefined acoustic events, one or more predefined actions associated with said at least one of said one or more predefined acoustic events, wherein said conversion model (M) is trained to provide said one or more audio features such that they include information that facilitates identification of respective occurrences of said one or more predefined acoustic events while preventing identification of speech characteristics.