Publications Repository - Gdańsk University of Technology

Machine learning is the process of learning functional relationships between measured signals (called percepts in the artificial intelligence literature) and some output of interest. In some cases, we wish to learn very specific relationships from signals such as identifying the language of a speaker (e.g. Zissman, 1996) which has direct applications such as in call center routing or performing a music information retrieval task (Schedl et al., 2014). Alternatively, we may be interested in an exploratory analysis, such as discovering relationships between animal-produced sounds and potential call categories that may carry signaling information (e.g. Sainburg et al., 2020). Machine learning can be used to discover information about the physical world such as determining the distance to a source based on pressure levels in a vertical line array (Niu et al., 2017) or solving inversion problems to find geoacoustic parameters of a seabed (Benson et al., 2000). In this article, we provide a gentle, and hopefully intuitive introduction to machine learning with only a limited number of examples and techniques. For readers who wish to read a more detailed introduction, we recommend the recently published review by Bianco et al. (2019) that focuses on machine learning and acoustics, or one of the many excellent book-length treatments of machine learning (e.g. Bishop, 2006; Goodfellow et al., 2016; Hastie et al., 2009).

Authors

• Prof. Marie A. Roch,
• Prof. Peter Gerstoft,
• prof. dr hab. inż. Bożena Kostek link open in new tab ,
• Prof. Zoi-Heleni Michalopoulou