Understanding how humans use auditory cues to interpret their surroundings is a challenge in various fields, such as music information retrieval, computational musicology and sound modeling. The most common ways of exploring the links between signal properties and human perception are through di↵erent kinds of listening tests, such as catego-rization or dissimilarity evaluations. Although such tests have made it possible to point out perceptually relevant signal structures linked to specific sound categories, rather small sound corpora (100-200 sounds in a categorization protocol) can be tested this way. The number of subjects generally do not exceed 20-30, since it is also very time consuming for an experimenter to include too many subjects. In this study we wanted to test whether it is possible to evaluate larger sound corpora through machine learning models for automatic timbre characterization. A selection of 1800 sounds produced by either wooden or metallic objects were analyzed by a deep learning model that was either trained on a perceptually salient acoustic descriptor or on a signal descriptor based on the energy contents of the signal. A random selection of 180 sounds from the same corpus was tested perceptually and used to compare sound categories obtained from human evaluations with those obtained from the deep learning model. Results revealed that when the model was trained on the perceptually relevant acoustic descriptors it performed a classification that was very close to the results obtained in the listening test, which is a promising result suggesting that such models can be trained to perform perceptually coherent evaluations of sounds.