The　purpose　of　speech　enhancement　is　to　extract　useful　speech　signal　from　noisy　speech.　The　performance　of　speech　enhancement　has　been　improved　greatly　in　recent　years　with　fast　development　of　the　deep　learning.　However,　these　studies　mainly　focus　on　the　frequency　domain,　which　needs　to　complete　time-frequency　transformation　and　the　phase　information　of　speech　is　ignored.　Therefore,　the　end-to-end　(i.e.　waveform-in　and　waveform-out)　speech　enhancement　was　investigated,　which　not　only　avoids　fixed　time-frequency　transformation　but　also　allows　modelling　phase　information.　In　this　paper,　a　fully　convolutional　network　with　skip　connections　(SC-FCN)　for　end-to-end　speech　enhancement　is　proposed.　Without　the　fully　connected　layers,　this　network　can　effectively　characterize　local　information　of　speech　signal,　and　better　restore　high　frequency　components　of　waveform　using　lesser　number　of　the　parameters.　Meanwhile,　because　of　existence　of　skip　connections　in　different　layers,　it　is　easier　to　train　deep　networks　and　the　problem　of　gradient　vanishing　can　also　be　tackled.　In　addition,　these　skip　connections　can　obtain　more　details　of　speech　signal　in　different　convolutional　layers,　which　is　beneficial　for　recovering　the　original　speech　signal.　According　to　our　experimental　results,　the　proposed　method　can　recover　the　waveform　better.　©　2019　IEEE.