This study presents two algorithms that count birds with wireless sensors equipped with microphones. Audio inputs are parametrized to get some kind of fingerprints which are used to recognize the song of the birds in a classification process. Afterward, counting algorithms derive an estimate of the number of singing birds in the habitat. Unlike traditional approaches which leverage the audio waveform to get the location of the targets, we use a trilateration technique on graphs derived from the detection of the birds. These graphs exhibit nice properties which enable the finding of the estimate in polynomial time. Further, experiments are conducted and confirmm the efficiency of our counting algorithms even in the presence of noise.