A Methodology Based on Bioacoustic Information for Automatic Identification of Reptiles and Anurans

Nowadays, human activity is considered one of the main risk factors for the life of reptiles and amphibians. The presence of these living beings represents a good biological indicator of an excellent environmental quality. Because of their behavior and size, most of these species are complicated to recognize in their living environment with image devices. Nevertheless, the use of bioacoustic information to identify animal species is an efficient way to sample populations and control the conservation of these living beings in large and remote areas where environmental conditions and visibility are limited. In this chapter, a novel methodology for the identification of different reptile and anuran species based on the fusion of Mel and Linear Frequency Cepstral Coefficients, MFCC and LFCC, is presented. The proposed methodology has been validated using public databases, and experimental results yielded an accuracy above 95% showing the efficiency of the proposal

A Vocal-Based Analytical Method for Goose Behaviour Recognition

Since human-wildlife conflicts are increasing, the development of cost-effective methods for reducing damage or conflict levels is important in wildlife management. A wide range of devices to detect and deter animals causing conflict are used for this purpose, although their effectiveness is often highly variable, due to habituation to disruptive or disturbing stimuli. Automated recognition of behaviours could form a critical component of a system capable of altering the disruptive stimuli to avoid this. In this paper we present a novel method to automatically recognise goose behaviour based on vocalisations from flocks of free-living barnacle geese (Branta leucopsis). The geese were observed and recorded in a natural environment, using a shielded shotgun microphone. The classification used Support Vector Machines (SVMs), which had been trained with labeled data. Greenwood Function Cepstral Coefficients (GFCC) were used as features for the pattern recognition algorithm, as they can be adjusted to the hearing capabilities of different species. Three behaviours are classified based in this approach, and the method achieves a good recognition of foraging behaviour (86–97% sensitivity, 89–98% precision) and a reasonable recognition of flushing (79–86%, 66–80%) and landing behaviour(73–91%, 79–92%). The Support Vector Machine has proven to be a robust classifier for this kind of classification, as generality and non-linear capabilities are important. We conclude that vocalisations can be used to automatically detect behaviour of conflict wildlife species, and as such, may be used as an integrated part of a wildlife management system

Application of Speech Recognition for Swiftlet Vocalizations

This research is about speech recognition technique are used for swiftlet vocalization application. Swiftlet vocalization need a system for recognize because there are many types of swiftlet sounds use in industry only can inspection by human expert. This research use speech recognition by using Mel Frequency Cepstral Coefficient (MFCC) for feature extraction and Distance Time Warping (DTW) for classification to calculate accuracy and efficiency combination both techniques

Wildlife Communication

This report contains a progress report for the ph.d. project titled “Wildlife Communication”. The project focuses on investigating how signal processing and pattern recognition can be used to improve wildlife management in agriculture. Wildlife management systems used today experience habituation from wild animals which makes them ineffective. An intelligent wildlife management system could monitor its own effectiveness and alter its scaring strategy based on this

Algorithm of Abnormal Audio Recognition Based on Improved MFCC

AbstractCharacteristics extraction has a great effect on the audio training and recognition in the audio recognition system. MFCC algorithm is a typical characteristics extraction method with stable performance and high recognition rate. For the situation that MFCC has a large amount of computation, an improved algorithm MFCC_E is introduced. The computation of MFCC_E is reduced by 50% compared with the standard algorithm MFCC, and it make the hardware implementation is easy. The experimental result indicated that MFCC_E and MFCC have the same recognition rate roughly, yet the computational complexity of MFCC_E is much smaller

Characterising sheep vocals using a machine learning algorithm : A thesis submitted in partial fulfilment of the requirements for the Degree of Master of Applied Science at Lincoln University

New Zealand’s economy is mainly dependent on the farming sector and the sheep sector is one of the most important farming sectors, playing a backbone role to the agricultural industry and placing New Zealand among the top five sheep exporter countries in the world. International consumer trends show concerns over the well-being of animals before slaughter and research also indicates potential negative effects on meat quality of stressed animals. Indicators for sheep well-being have largely been limited to physical weight gain and visually observable behaviour and appearance. There has been recent interest but little substantive research on sheep vocalisation as a means of monitoring sheep well-being. This assumes that sheep vocalisation can be classified as representing different states of well-being. Therefore, this thesis investigated the potential to be able to classify sheep vocalisations in a way that would enable automated assessment of the well-being of New Zealand sheep using recorded vocalisations. A supervised machine learning approach was used to classify the sheep vocals into happy and unhappy classes. Sheep sounds were collected from a New Zealand Ryeland sheep stud farm and online databases. After collection, these sounds were labelled by an expert, pre-processed to make them clean from unwanted background sound noises and features were extracted and selected for classification. Models were built and trained and tested. Model use in this research shows that sheep sounds were classified into happy and unhappy classes with an accuracy of 87.5%, for the sheep vocals used in this research. Through demonstrating the ability for automated classification of sheep vocalisations this research opens the door for further study on the well-being of sheep through their vocalisations. Future researchers could also collect larger vocal data sets across different breeds to test for breed-related variance in vocalisations.. This may enable future sheep well-being certification systems to be established to assure consumers of the well-being of pre-slaughter sheep life