An audio signal classifier comprising: a feature extractor adapted to extract metadata from an audio signal the metadata defining a plurality of features of the audio signal, the feature extractor being adapted to generate a feature vector including selected features (X 1,..., X N) of the audio signal, the selected features including a duration of the audio signal, and each selected feature having a feature value; and to generate a robustness value indicating whether the extracted metadata is valid or invalid, a machine learning model trained to classify the audio signal as one of a plurality of audio signal classes based on the feature vector, the machine learning model being adapted to generate a plurality of class values based on the feature values, each class value (CV 1,..., CV N) corresponding to one of the plurality of audio signal classes, the plurality of class values together indicating the class of the audio signal, the class of the audio signal being useable to select audio presets to adjust audio output of loudspeakers, the audio signal classifier further including: a deep learning model adapted to generate a plurality of class values ( CV') based on audio frames of the audio signal, the plurality of class values together indicating the class of the audio signal a reliability evaluator to generate a reliability value (β) indicating whether the plurality of class values generated by the machine learning model is reliable or unreliable; the classifier further comprising an output decision model adapted to determine a class of the audio signal from: only the plurality of class values generated by the machine learning model when the robustness value indicates that the extracted metadata is valid and when the reliability value indicates that the plurality of class values generated by the machine learning model is reliable; only the plurality of class values generated by the deep learning model when the robustness value indicates that the extracted metadata is invalid; and the plurality of class values generated by the machine learning model and the plurality of class values generated by the deep learning model when the robustness value indicates that the extracted metadata is valid and the reliability value indicates that the plurality of class values generated by the machine learning model is unreliable. a deep learning model adapted to generate a plurality of class values ( CV') based on audio frames of the audio signal, the plurality of class values together indicating the class of the audio signal a reliability evaluator to generate a reliability value (β) indicating whether the plurality of class values generated by the machine learning model is reliable or unreliable; the classifier further comprising an output decision model adapted to determine a class of the audio signal from: only the plurality of class values generated by the machine learning model when the robustness value indicates that the extracted metadata is valid and when the reliability value indicates that the plurality of class values generated by the machine learning model is reliable; only the plurality of class values generated by the deep learning model when the robustness value indicates that the extracted metadata is invalid; and the plurality of class values generated by the machine learning model and the plurality of class values generated by the deep learning model when the robustness value indicates that the extracted metadata is valid and the reliability value indicates that the plurality of class values generated by the machine learning model is unreliable. only the plurality of class values generated by the machine learning model when the robustness value indicates that the extracted metadata is valid and when the reliability value indicates that the plurality of class values generated by the machine learning model is reliable; only the plurality of class values generated by the deep learning model when the robustness value indicates that the extracted metadata is invalid; and the plurality of class values generated by the machine learning model and the plurality of class values generated by the deep learning model when the robustness value indicates that the extracted metadata is valid and the reliability value indicates that the plurality of class values generated by the machine learning model is unreliable.