Bird species identification based on timbre and pitch features

This study proposes an automatic bird sound identification system to help people learn to identify bird species. The system is built upon a two-stage identification framework. The first stage performs call/song classification. If an unknown sound clip is classified as a call, it is then handled by a call identifier in the second stage; otherwise, it is handled by a song identifier. Both identifiers use two acoustic features, timbre and pitch, to determine which of the bird species the sound clip belong to. In using timbre features, bird sounds are converted into MFCCs and their first derivatives and then analyzed using Gaussian mixture models. In using pitch feature, we convert bird sounds into MIDI note sequences and then use bigram models to analyze the dynamic change information of the notes. Our experiments, conducted using a database including twenty common bird species, show that the proposed system can achieve 90.4% accuracy.