Male songbirds produce songs that are critical for competing with rivals and attracting mates. These songs are learned: juveniles listen to surrounding adults, memorize their songs, and produce imitations of the songs they hear early in life. Fathers are supposed to be important influences on their son's song development, but this hypothesis remains controversial due to the challenges of tracking what juveniles hear in nature. My research group has recorded roughly 2 terabytes of audio (approx 3000 hours) at the nests of approximately 175 nests of breeding pied flycatchers (svartvit flugsnappare, Ficedula hypoleuca) in Sweden. We have qualitatively demonstrated that fathers sing to their offspring, but need to quantify how often, variation across nests, and the type of songs males sing in order to understand the potential impact on their offspring. The amount of audio makes it impractical to manually scan, so I have employed a full-time AI analyst from April 2024 - April 2025 to develop a machine learning model to detect and classify songs from these recordings. The analyst has substantial experience developing detection models and the aim is to develop a well-functioning, validated model over the next year.