In-room loudspeaker equalization requires a significant amount of microphone positions in order to characterize the sound field in the room. This can be a cumbersome task for the user. This paper proposes the use of artificial intelligence to automatically estimate and equalize, without user interaction, the in-room response. To learn the relationship between loudspeaker near-field response and total sound power, or energy average over the listening area, a neural network was trained using room measurement data. Loudspeaker near-field SPL at discrete frequencies was the input data to the neural network. The approach has been tested in a subwoofer, a full-range loudspeaker, and a TV. Results showed that the in-room sound field can be estimated within 1--2 dB average standard deviation.
