Green areas are often stressed from different types of human activity, resulting in degraded biodiversity and poor quality. On the other hand, green areas are understood to provide benefits to individuals and society and can be considered as supportive environments. The study of the interactions between humans and nature is of key importance. As human and animal activity can be observed from acoustic recordings, bioacoustic studies are an interesting way to study the ecology of the human-nature interactions. Soundecological indicators can be used with this purpose, but advanced machine learning has opened pathways to advance the field of computational bioacoustics. Use of knowledge systems with learned knowledge based on different levels of supervision need evaluation in this field. Different datasets, reflecting the wide range of applications, and different machine learning techniques will be used to extract the desired information and to demonstrate advances in this field.