Recognition of Orthoptera species by means of their song is widely used in field work but requires expertise. It is now possible to develop computer-based systems to achieve the same task with a number of advantages including continuous long term unattended operation and automatic species logging. The system described here achieves automated discrimination between different species by utilizing a novel time domain signal coding technique and an artificial neural network. The system has previously been shown to recognize 25 species of British Orthoptera with 99% accuracy for good quality sounds. This paper tests the system on field recordings of four species of grasshopper in northern England in 2002 and shows that it is capable of not only correctly recognizing the target species under a range of acoustic conditions but also of recognizing other sounds such as birds and man-made sounds. Recognition accuracies for the four species of typically 70-100% are obtained for field recordings with varying sound intensities and background signals.

The oak platypodid beetle, Platypus quercivorus, stridulates both during premating behavior and when stressed, as well as spontaneously. When a female was put onto the bark surface of a male-infested log, she began to walk and produce an "approaching chirp," searching for a gallery entrance. When finding one, she entered it and tried to pull a male out. If the male's abdomen became visible, she appeared to push her frons against his elytral declivity and made a "premating buzz" that lasted about 5-10 s. During this buzzing, the male backed out of the gallery in order to allow her in. Females that had been silenced via surgery did not evoke this reaction; thus, males apparently identified females by their buzzing sound. The male then followed the female into the gallery, and produced an "in-gallery chirp" with his posterior abdomen visible. After a while, both sexes backed out of the hole and copulated at the entrance. Both sexes produced "stress chirps" when confined inside a cotton ball, and "spontaneous chirps" when walking alone on the surface of an oak bark piece.

Specific identification of three Tibicen cicadas, T. japonicus, T. flammatus and T. bihamatus, by their chirping sounds was carried out using Principal Components Analysis (PCA). High quality recordings of each species were used as the standards. The peak and mean frequencies and the pulse rate were used as the variables. Out of 12 samples recorded in the fields one fell in the vicinity of T. japonicus and all other were positioned near T. bihamatus. Then the cluster analysis of the PCA scores clearly separated each species and allocated the samples in the same way.