Bioacoustics, the study of animal vocalizations and natural soundscapes, has proven to be a valuable source of data for wildlife monitoring. Just as a human would use contextual information to identify species calls from acoustic recordings, one unexplored way to improve deep learning classifier in bioacoustics is to provide the algorithm with contextual meta-data, such as time and location. We developed an algorithm to classify 22 bird songs for which the location can help to distinguish the different species. We explored different multi-branch convolutional neural networks, trained on both spectrograms and location information, as well as a geographical prior separately trained on location to estimate the probability that a species occurs at a given location. We compared the classification of the models to a baseline model without the spatial meta-data. Our findings revealed in each case an increase in the performance of the classification with the highest improvement obtained with the geographical prior (F1-score of 87.78\%, compared to 61.02\% for the baseline model). The methods based on multi-branch neural network proved to be efficient as well and simpler to use than the geographical prior as it requires a single model. Adding metadata to the acoustic classifier is a valuable source of information to improve classification performance, with room for further progress, and opens new opportunities for generalizing models.