Specifics of DEMON acoustic signatures for large and small boats

Abstract

Marine vessel propellers produce noise by the formation and shedding of cavitation bubbles. This process creates both narrow-band tones and broad-band amplitude modulated noise. The Detection of Envelope Modulation on Noise (DEMON) is an algorithm to determine the frequencies that modulate this noise. Results of DEMON processing depend on the selection of a ship noise frequency band to analyze. It is well known that the best passband to use may vary dramatically between vessels. Despite this, there has been no systematic investigation how the DEMON spectra depend of the carrier noise frequencies, and the modulation indices of vessel noise have not been investigated. We use a modification of the Cyclic Modulation Spectrum (CMS) to determine the modulation index of cavitation noise across the entire spectrum of carrier frequencies. We investigated how speed and vessel size affect the modulation index and carrier frequency of vessel noise. Several phenomena in the distribution of modulation indices for large and small boats were observed. These can be used for vessel classification. For small boats, the DEMON spectra have a different set of frequency peaks at various carrier frequencies. This is explained by the engine exhaust which produces amplitude modulated noise much like a propeller.

Automatic detection of vessel signatures in audio recordings with spectral amplitude variation signature

“…A well‐known method for vessel detection in audio recordings is the Detection of Envelope Modulation On Noise (DEMON) algorithm. By enhancing audio frequencies characteristic of vessel emissions, it creates an acoustic signature that can be employed for classification (Chung et al, ; Pollara et al, ; Pollara, Sutin, & Salloum, ). Introduced over 50 years ago (Tuteur, McDonald, Schultheiss, & Usher, ), the DEMON signature has inspired several novel ship classification algorithms (Chung et al, ).…”

“…While some solutions have employed classification approaches derived from the analysis of acoustic features (Leal, Leal, & Sanchez, 2015), others have focused on the specific spectral signatures of the sounds emitted by the different types of vessels, for example training neural networks to detect a set of known signatures (Chung, Sutin, Sedunov, & Bruno, 2011;Hanson, Antoni, Brown, & Emslie, 2008;Pollara, Lignan, Boulange, Sutin, & Salloum, 2017;Slamnoiu et al, 2016). Nonetheless, improving detection accuracy in noisy conditions and reducing the rate of false positives remain as challenges.…”