Acoustic environment classification using discrete hartley transform features

• Published in: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) pp. 1 - 4
• Main Authors: Jleed, Hitham, Bouchard, Martin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2017
• Subjects: acoustic environment classification; Acoustics; Computational modeling; Conferences; discrete Hartley transform; Feature extraction; features; Hidden Markov models; Mathematical model; Transforms
• DOI: 10.1109/CCECE.2017.7946646

This paper presents a new approach for acoustic environment classification based on the discrete Hartley transform. The approach applies a Hidden Markov Model based classifier on test data composed of audio clips, in order to determine which environment is surrounding these audio clips. The approach uses features obtained from the discrete Hartley transform, leading to a set of features that require only real arithmetic computations. This can make the technique advantageous in terms of simplicity and/or in terms of computational speed. The proposed approach performance is evaluated on benchmark datasets provided from the 2013 and 2016 Detection and Classification of Acoustic Scenes and Events (DCASE) challenges. Experiments show that the proposed method is competitive compared to other recently proposed methods, and that the use of the discrete Hartley transform improves the classification performance.