川金丝猴声音的研究进展

川金丝猴是我国特有的濒危灵长类动物,主要分布于四川、甘肃、陕西、湖北的高山森林。在近年国家对生态保护的重视以及众多科研工作者的努力下,川金丝猴的科学研究已经广泛而深入地涉及了行为学、生理学、分子生物学等方面。然而,对川金丝猴声音的研究却相对较少。从川金丝猴的叫声类型以及基本声学参数、叫声特点及作用等方面对川金丝猴声音的研究进展进行了综述,并对今后川金丝猴生物声学研究的方向进行了展望。     

Estimation of population density of stored grain pests via bioacoustic detection

The potential of bioacoustics in estimating the population density of insect pests inside the stored grain mass was evaluated in the laboratory. We used a piezoelectric sensor and a portable acoustic emission amplifier connected to a computer for recording acoustic emissions of insects. The software analyses the vibration recordings of the piezoelectric sensor, performs signal parameterization and eventually classification of the infestation severity inside the grain mass in four classes, namely: Class A (densities ≤1 adult/kgr), Class B (densities 1–2 adults/kgr), Class C (densities 2–10 adults/kgr) and Class D (densities >10 adults/kgr). Adults of the most important beetle pests of stored cereals and pulses, in various population densities (1, 2, 10, 20, 50, 100, 200 & 500 beetle adults/kgr grain) were used during the present study. The linear model was very effective in describing the relationship between population density and number of sounds. Multiple classifiers were used to evaluate the accuracy of bioacoustics on predicting the pest density given per minute counts of vibration pulses. Based on our results, our system's performance was very satisfactory in most cases (∼68%) given that probabilities for successful prediction typically exceeding 70%. Our study suggests that automatic monitoring of infestations in bulk grain is feasible in small containers. This kind of service can assist with reliable decision making if it can be transferred to larger storage establishments (e.g. silos). Our results are discussed on the basis of enhancing the use of acoustic sensors as a decision support system in stored product IPM.     

Real-time analysis of chicken embryo sounds to monitor different incubation stages

The objective of this paper is to develop an algorithm that could be used in order to reduce the spread of chicken hatching in industrial incubators for chicken eggs. The approach that is used is based on frequency analysis of sounds recorded inside the industrial incubator and aims at identifying the time at which all the eggs inside the incubator have reached the internal pipping stage. The developed algorithm is able to be calibrated automatically in order to adjust for sounds around the incubator and the acoustics of every incubator. The algorithm has been implemented in a Digital Signal Processor and applied in real-time in an industrial environment. It is shown that the algorithm can correctly identify the time at which 93-98% of the eggs have had been in the internal pipping stage. This level of accuracy is considered adequate for a practical application focusing on reduction of the hatching window.