Abstract-The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research field. Robust sound event classification, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classification methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefficients, have been shown to perform reasonably well for the sound event classification task, although spectrogrambased or auditory image analysis techniques reportedly achieve superior performance in noise. This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classifiers. Performance is evaluated on a standard robust classification task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classification techniques.

The beetle Monochamus alternatus Hope (Coleoptera: Cerambycidae) is an efficient vector of pine wood nematode, the causal pathogen of pine wilt disease, that has resulted in devastating losses of pines in much of Asia. We assessed the response of adult M. alternatus to 2-(undecyloxy)-ethanol, the male-produced pheromone of the congeneric M. galloprovincialis Dejean, in field experiments in Fujian Province, People's Republic of China. Both sexes of M. alternatus were attracted to lures consisting of 2-(undecyloxy)-ethanol combined with the host plant volatiles alpha-pinene and ethanol. A follow-up experiment showed that 2-(undecyloxy)-ethanol was synergized by both ethanol and alpha-pinene. Coupled gas-chromatography mass-spectrometry analyses of volatiles sampled from field-collected beetles of both sexes revealed that 2-(undecyloxy)-ethanol was a sex-specific pheromone component produced only by males. The combination of 2- (undecyloxy) -ethanol with ethanol and/or alpha-pinene will provide a valuable and badly needed tool for quarantine detection, monitoring, and management of M. alternatus.

Understanding the relationship between brain activity and specific mental function is important for medical diagnosis of brain symptoms, such as epilepsy. Magnetoencephalography (MEG), which uses an array of high-sensitivity magnetometers to record magnetic field signals generated from neural currents occurring naturally in the brain, is a noninvasive method for locating the brain activities. The MEG is normally performed in a magnetically shielded room. Here, we introduce an unshielded MEG system based on optically pumped atomic magnetometers. We build an atomic magnetic gradiometer, together with feedback methods, to reduce the environment magnetic field noise. We successfully observe the alpha rhythm signals related to closed eyes and clear auditory evoked field signals in unshielded Earth’s field. Combined with improvements in the miniaturization of the atomic magnetometer, our method is promising to realize a practical wearable and movable unshielded MEG system and bring new insights into medical diagnosis of brain symptoms.

No abstract

No abstract