This paper addresses the problem of improving the quality of metadata in biological observation databases, in particular those associated with observations of living beings, and which are often used as a starting point for biodiversity analyses. Poor quality metadata lead to incorrect scientific conclusions, and can mislead experts. Thus, it is important to design and develop methods to detect and correct metadata quality problems. This is a challenging problem because of the variety of issues concerning such metadata, e.g., misnaming of species, location uncertainty and imprecision concerning where observations were recorded. Related work is limited because it does not adequately model such issues. We propose a geographic approach based on expertled classification of place and/or range mismatch anomalies detected by our algorithms. Our approach enables detection of anomalies in both species' reported geographic distributions and in species' identification. Our main contribution is our geographic algorithm that deals with uncertain/imprecise locations. Our work is tested using a case study with the Fonoteca Neotropical Jacques Vielliard, one of the 10 largest animal sound collections in the world.