It is investigated whether an automated classification of loudspeaker enclosures can be realized. The acoustic load of the enclosure is reflected in the electrical impedance of the loudspeaker and is hence detectable from the point of view of the power amplifier. In order to classify the enclosures of passive one-way speakers, an artificial neural network is trained with synthetic impedance spectra based on equivalent electrical circuit models. The generalization capability is tested with measured test sets of closed, vented, band-pass and transmission-line enclosures. The resulting classification procedure works well within a synthetic test set. However, a generalization to the measured test-data has shown to require deeper investigations to achieve better separation between the different vented enclosures types.
