# Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion
## 2 State of the Art  
In spite of the significant importance that insects play in the fields of biology and ecological conservation, the number of studies in the field of automatic acoustic identification of insects is low. However, in the past years, some related interesting studies have been done. For instance, in [14], there is a good approach which has been used as the baseline method for comparison. This approach relies on three machine learning algorithms: Hidden Markov Models (HMM), Gaussian Mixture Models (GMM) and Probabilistic Neural Networks (PNN) to perform the classification of several singing insect species. Besides, the authors employed LFCC, the dominant harmonic and segment duration as features to parameterize the insect sounds, achieving an identification accuracy of 86.3% on the level of specific species. This study was conducted on 313 species of crickets, katydids and cicadas from the Insect Sound World collection of crickets and katydids from Japan and the North America sound collection (SINA). On the other hand, the HMM method was also proposed by [PERSON] and [PERSON] in [15] for insect pest management. This work was based on a sub-band decomposition method to extract parameters from the wavelet transform of the sound signal. The proposed method was successfully tested on 50 insect sound samples which were collected from the sound library of the United States Department of Agriculture (USDA) yielding a 90.98% accuracy. Another approach was followed in [16] to classify 25 British Orthoptera species and ten bird species using a method that relies exclusively on time domain features. The signal is split into epochs (successive zero-crossings) and encoded in terms of shape and duration by using a Time-Domain Signal Coding (TDSC) procedure. This technique fed a Neuronal Network (NN) achieving results of 99% recognition accuracy.  
In [17], Continuous Wavelet Transformation (CWT) and Convolutional Neural Network (CNN) were used for mosquito detection and bird identification getting promising results. The 57 mosquito recordings used in this work were labeled with a success rate of 92.2% detecting the presence or absence of mosquitoes surpassing human labelers. More recently, in [18], the authors identified three bee species and a hornet analyzing their flight sounds. The method was able to correctly distinguish between flight sounds and environmental sounds, but only had a precision of 70-90% in terms of species-specific identification. For classification, they employed MFCC and SVM.  
There are no doubts that significant advances have been reported in the literature regarding the recognition of biological acoustic signals. Nevertheless, an improvement to actual methodologies is needed, giving a better success ratio; and this could be obtained merging or fusing different sets of features to enhance the quality of the information supplied to the intelligent system to enable the recognition of a greater number of species. Hence, this work presents a novel method to successfully identify and classify 255 species of crickets, katydids and cicadas registered in the Singing Insects of North America (SINA) [19] collection and 88 species of cicadas from the web collection Insectsingers [20]. This approach has given a success rate of 98.07% in the identification stage through the novel use of fused frequency features in the pattern recognition process. This new approach allows a more effective representation of insects sounds, where MFCC represents the information at lower frequency regions and the LFCC represents the information at higher frequency regions. The fusion of coefficients increases the efficiency and reliability of the system making the proposed approach unique in terms of insect species recognition. Compared to previous works, this proposal is capable of identifying and classifying 343 species of either katydids crickets or cicadas on the level of specific species with higher success rates than previous systems, as shown in [14; 15; 16; 17; 18].