Vocal signal recognition of ewes based on power spectrum and formant analysis method

Abstract: Inner Mongolia and its surrounding areas in the west are developing an intensive and large-scale sheep farming operation, in which sheep are bred with planting forage and are placed in captive facilities. However, the breeding pattern of such operation needs a high level of animal welfare management. Considering that sheep makes different vocalization in different emergent situations, ewes' vocalization can be used as an important evidence for ewes' health monitoring and breeding welfare evaluation. In this paper, taking Small Tail Han sheep as an example, ewes' vocalization signals were evenly collected from 80 adult ewes under 3 stress behaviors including searching lamb, hunger, and scare via a wireless audio surveillance device. Then, these continuous vocal signals of ewes were split into 1 200 single call signals using Audacity Acoustic Edit software. The band-pass filter and wavelet denoising methods were applied to preprocess those single sound signals. Six hundred of those sound signals, which were comprised of three different stress behaviors by random selected 200 signals, were analyzed to extract ewes' acoustic characteristic parameters using auto-regressive (AR) power spectrum estimation and formant extraction methods, respectively. Therefore, six representative frequency characteristic parameters from AR power spectrum estimation method were extracted: the power spectrum density mean, the geometric mean, the median value, the trimmed mean, the mean absolute deviation, and inter quartile deviation, and characteristics parameters from formant analysis method were the first, second and third formant frequency. Moreover, typical time-domain characteristic parameters such as signal maximum value, duration value and interval value were taken as well. Then, these characteristic parameters were used to train the back propagation (BP) neural network model of ewes' vocalization recognition, and the rest of 600 vocal signals were used to test the effects of the recognition mode. The results demonstrated that characteristic parameters of ewes' vocal signals were obviously different under different stress behaviors. Furthermore, if BP recognition network was trained by formant parameters, the average correct recognition rate of ewes' vocal signal was 85.3%, higher than AR power spectrum estimation parameters of 81.0%. When BP network was trained by a combination of above two kinds of characteristic parameters, the average correct recognition rate was 93.8%, which meant that the performance of the combination parameters was better than another two methods. However, the average false positive rate still reached 6.2% because ewes' vocal signals under the same stress behavior had a certain degree of difference due to the different age and weight as well as sound volume strength. The results of this study also indicated that analysis of vocalization could be an indicator of different physiological conditions in sheep and may be an important role for understanding communications in ewes.