Bird Species Recognizer using LMS Algorithm

Abstract

Birds play a vital role in many ecosystems, acting as both predators and preys for other living organisms. Therefore its important to monitor the population of various bird species in the environment in order to maintain balance in the ecosystem. This process will become tedious if it is done manually as it involves handling large sets of data at the same instant. We can do this by developing an automatic bird species recognizer which identifies the bird species based on bird songs and voice signals. In this research, we have used a tenth-order LMS adaptive filter to remove noise from bird voice signals which are recorded in different environmental conditions where different noise frequencies are present. The design of a tenth-order LMS adaptive filter using MATLAB has been implemented. The performance and characteristics of the filter for five different methods of LMS has been shown. After removal of noise from the noisy bird voice signal using LMS algorithm, we have made use of cross correlation to identify the bird species that it corresponds to. Signal to Noise Ratio (SNR) and Mean Square Error (MSE) of the filtered bird signals obtained using the variants of LMS like Normalized LMS, Sign-Data LMS, Sign-Error LMS and Sign-Sign LMS have been estimated and compared. We have made use of signal processing tool kits and various noise parameter schemes have been computed to show the effectiveness of the designed filter in the field of bird recognition.