FPGA Implementation of Blue Whale Calls Classifier Using High-Level Programming Tool

Abstract

In this paper, we propose a hardware-based architecture for automatic blue whale calls classification based on short-time Fourier transform and multilayer perceptron neural network. The proposed architecture is implemented on field programmable gate array (FPGA) using Xilinx System Generator (XSG) and the Nexys-4 Artix-7 FPGA board. This high-level programming tool allows us to design, simulate and execute the compiled design in Matlab/Simulink environment quickly and easily. Intermediate signals obtained at various steps of the proposed system are presented for typical blue whale calls. Classification performances based on the fixed-point XSG/FPGA implementation are compared to those obtained by the floating-point Matlab simulation, using a representative database of the blue whale calls.

Keywords

• hardware/software co-simulation
• FPGA
• feature extraction
• fourier transform
• neural network
• real-time classification
• blue whale calls