AUDIO FEATURE EXTRACTION FOR VEHICLE ENGINE NOISE CLASSIFICATION

In this paper we propose a new scheme for vehicle engine noise classification as a more privacy-preserving alternative to classifying vehicles based on video recordings. We establish two scenarios: diesel vs. petrol and heavy goods vehicle vs. personal car classification. Our approach includes a novel modulation-spectrum-based feature representation that is used in conjunction with a siamese neural network classifier. Additionally, a database containing recordings from diverse urban acoustic scenarios is provided. The obtained results show the advantage of the proposed approach compared to conventional feature representations and classifiers. This is achieved by de-correlating background noise from target noise and by quantifying the degree of variation of noise characteristics.