I am Dr. Ayesha Hakim, a computer scientist with an aim to use my skills to do something 'Good for Nature'. I joined Cacophony to contribute towards preserving New Zealand's birds in the wild. My research interests include behaviour analysis using machine learning techniques and producing pretty graphics to visualise the complex data. To be specific, I am interested in recognition and behaviour analysis of humans and birds using audio and visual signals.
Acoustic recordings of birds have been used by conservationists and ecologists to determine population density of specific bird species in a region. However, it is very hard to analyse and visualise the presence/absence of a specific bird species by manually hearing these recordings even by an expert bird song specialist. I am working on developing computational tools to automatically classify and visualise bird sounds in order to recognise different bird species in the wild. It is a powerful combination of machine learning, ecology, and applications of multimedia visualisation. These tools can be used by conversationalists, ornithologists, ecologists, and evolutionary scientists to visually identify a bird’s species using their sound alone.
In this blog, I want to share some of the initial results obtained by automatic clustering of bird species based on their sound features using machine learning techniques. It is an attempt to find similarities between sounds of the same bird species and with other bird species as well as differentiating between birds and human sounds. Many thanks to Nirosha Priyadarshani, Stephen Marsland, Isabel Castro, and Amal Punchihewa for making their datasets available for further research. These datasets are available at https://github.com/smarsland/AviaNZ.