Audio parameterization with robust frame selection for improved bird identification. Issue 22 (1st December 2015)

• Record Type: Journal Article
• Authors: Ventura, Thiago M.; de Oliveira, Allan G.; Ganchev, Todor D.; de Figueiredo, Josiel M.; Jahn, Olaf; Marques, Marinez I.; Schuchmann, Karl-L.
• Is Part Of: Expert systems with applications. Volume 42:Issue 22(2015)
• Page Start: 8463
• Page End: 8471
• Publication Date: 2015-12-01
• Subjects: Computational bioacoustics -- Bird identification -- Hidden Markov Model (HMM) -- Mel Frequency Cepstral Coefficients (MFCCs) -- Robust frame selection; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.07.002
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Audio parameterization method with robust frame selection. Automated acoustic recognition of 40 bird species. HMM-based bird identification. Abstract: A major challenge in the automated acoustic recognition of bird species is the audio segmentation, which aims to select portions of audio that contain meaningful sound events and eliminates segments that contain predominantly background noise or sound events of other origin. Here we report on the development of an audio parameterization method with integrated robust frame selection that makes use of morphological filtering applied on the spectrogram seen as an image. The morphological filtering allows to exclude from further processing certain audio events, which otherwise could cause misclassification errors. The Mel Frequency Cepstral Coefficients (MFCCs) computed for the selected audio frames offer a good representation of the spectral information for dominant vocalizations because the morphological filtering eliminates short bursts of noise and suppresses weak competing signals. Experimental validation of the proposed method on the identification of 40 bird species from Brazil demonstrated superior accuracy and faster operation than three traditional and recent approaches. This is expressed as reduction of the relative error rate by 3.4% and the overall operational time by 7.5% when compared to the second best result. The improved frame selection robustness, precision, and operational speed facilitate applications like multi-species identification of real-field recordings.