Machine Learning-Based Classification of Mosquito Wing Beats Using Mel Spectrogram Images and Ensemble Modeling

These days, many dreadful diseases are caused by mosquitoes, along with other types of infections. Mosquitoes are also called silent feeders. Due to this ability, mosquitoes take advantage of increasing their capacity to spread diseases. Many life-threatening diseases such as malaria, dengue, Zika, yellow fever, and chikungunya are caused by these mosquitoes. These diseases are caused by viruses, parasites, and bacterial pathogens through various vectors like Aedes aegypti and Culex. Due to the rapid increase in cases worldwide, there is a necessity to deploy an intelligent machine-automated model to decrease the spread of infections. The method used in this study detects different types of mosquitoes responsible for spreading these diseases. The key to controlling the spread of infection is to detect the type of mosquito based on the beat of its wings. The sound recordings related to mosquito wing beats, collected from different sources, are used in this study. These recordings are divided based on the mosquito species through max pooling and convolution models. The entire work is framed under three segments: identifying the recorded sound audio file to get a Mel spectrogram image, extracting features using pooling and convolution methods, and identifying the mosquito type through an ensemble method using classifiers like Random Forest, Support Vector Machine (SVM), and Decision Tree. The frequency waves are used to transform the audio recordings into spectrograms in the preprocessing phase. The spectrogram filter is used to eliminate noise from the spectrogram images. Vector values are obtained using pooling and convolution methods. The values from the classifiers used in this work are then fed into the ensemble method to identify the mosquito type based on its wing beats. Based on the final results and observations, the SVM classifier achieved the highest accuracy, with 95.05% for the type Aedes albopictus, compared to the other classifiers.