This　paper　presents　a　new　approach　for　identifying　birds　automatically　from　their　sounds,　which　first　converts　the　bird　songs　into　spectrograms　and　then　extracts　texture　features　from　this　visual　time-frequency　representation.　The　approach　is　inspired　by　the　finding　that　spectrograms　of　different　birds　present　distinct　textures　and　can　be　easily　distinguished　from　one　another.　In　particular,　we　perform　a　local　texture　feature　extraction　by　segmenting　the　bird　songs　into　a　series　of　syllables,　which　has　been　proved　to　be　quite　effective　due　to　the　high　variability　found　in　bird　vocalizations.　Finally,　Random　Forests,　an　ensemble　classifier　based　on　decision　tree,　is　used　to　classify　bird　species.　The　average　recognition　rate　is　96.5%　for　10　kinds　of　bird　species,　outperforming　the　well-known　MFCC　features.　©　2013　IEEE.