Optimisation of frameworks for development and operation of automated sound classification in real-world environmental monitoring

Project level: Honours

Effective methods for sound classification are widely published, but these works often reference highly curated datasets or are applied to tightly controlled scenarios. Accurate sound classification in real-world environments are confounded by variability in signal to noise ratios and variability in the characteristics of noise sources. This work seeks to explore the influence that data representations (i.e. data engineering) and construction of training algorithms may have on the performance of environmental noise classification. An existing classification framework and training dataset are available for the purposes of baselining ‘existing’ performance.

Expected Outcomes:

Improved understanding of influence that algorithms and data representations have on performance of noise classification problems. This is an industry-supported project. The interested student will work closely with Advitech. Suitable for Honours students.