Keyboard　acoustic　side　channel　attacks　exploit　audio　leakage　from　typing　to　deduce　typed　words　with　a　certain　level　of　accuracy.　Researchers　have　been　improving　the　accuracy　of　these　attacks　through　various　techniques　of　feature　extraction　and　classification.　They　also　apply　machine　learning　methods　and　deep　learning　techniques　to　enhance　the　accuracy　of　their　results.　At　the　same　time,　defense　mechanisms　against　these　attacks　have　not　kept　up　with　the　increasing　precision　of　the　attacks.　In　this　study,　we　introduce　a　practical　defense　strategy　against　keyboard　acoustic　attacks　during　password　and　text　typing.　We　evaluate　its　effectiveness　against　multiple　attack　vectors.　Our　defense　strategy　involves　generating　unique　background　sounds　using　GANs　to　mask　sensitive　audio　leaks　from　the　keyboard,　thwarting　side　channel　attacks　from　extracting　usable　information　about　typed　content.　The　background　sounds　are　produced　by　the　device　used　for　text　input.　We　assess　the　usability　of　our　approach　for　short　and　prolonged　usage　durations,　demonstrating　that　the　addition　of　background　sounds　does　not　impede　users＇　ability　to　enter　passwords　or　perform　computer　tasks　effectively.　©　2024　SPIE.