The Impact of 9/11 and its Aftermath on Substance Use and Psychological Functioning: An Overview

This Essay provides a brief summary and evaluation of findings on the mental health and substance abuse consequences of the events of 9/11 throughout the nation and in United States\u27 cities. It also presents new data obtained from clients who entered substance abuse treatment in New York and other cities either before 9/11 or during a six-month period following the events. This Essay discusses how best to interpret these varying research findings. It concludes that crisis produces many responses and most people just coped with 9/11 in their individual ways

Automatic Classification of African Elephant (\u3cem\u3eLoxodonta africana\u3c/em\u3e) Follicular and Luteal Rumbles

Recent research in African elephant vocalizations has shown that there is evidence for acoustic differences in the rumbles of females based on the phase of their estrous cycle (1). One reason for these differences might be to attract a male for reproductive purposes. Since rumbles have a fundamental frequency near 10Hz, they attenuate slowly and can be heard over a distance of several kilometers. This research exploits differences in the rumbles to create an automatic classification system that can determine whether a female rumble was made during the luteal or follicular phase of the ovulatory cycle. This system could be used as the basis for a non-invasive technique to determine the reproductive status of a female African elephant. The classification system is based on current state-of-the-art human speech processing systems. Standard features and models are applied with the necessary modifications to account for the physiological, anatomical and language differences between humans and African elephants. The long-term goal of this research is to develop a universal analysis framework and robust feature set for animal vocalizations that can be applied to many species. This research represents an application of this framework. The vocalizations used for this study were collected from a group of three female captive elephants. The elephants are fitted with radio-transmitting microphone collars and released into one of three naturalistic yards on a daily basis. Although this data collection setup is good for determining the speaker of each vocalization, it suffers from many potential noise sources such as RF interference, passing vehicles, and the flapping of the elephant’s ears against the collar

Generalized Perceptual Linear Prediction (gPLP) Features for Animal Vocalization Analysis

A new feature extraction model, generalized perceptual linear prediction (gPLP), is developed to calculate a set of perceptually relevant features for digital signal analysis of animalvocalizations. The gPLP model is a generalized adaptation of the perceptual linear prediction model, popular in human speech processing, which incorporates perceptual information such as frequency warping and equal loudness normalization into the feature extraction process. Since such perceptual information is available for a number of animal species, this new approach integrates that information into a generalized model to extract perceptually relevant features for a particular species. To illustrate, qualitative and quantitative comparisons are made between the species-specific model, generalized perceptual linear prediction (gPLP), and the original PLP model using a set of vocalizations collected from captive African elephants (Loxodonta africana) and wild beluga whales (Delphinapterus leucas). The models that incorporate perceptional information outperform the original human-based models in both visualization and classification tasks

Managed Haying and Grazing of CRP

Agricultural and Food Policy,

Automatic Classification and Speaker Identification of African Elephant (\u3cem\u3eLoxodonta africana\u3c/em\u3e) Vocalizations

A hidden Markov model (HMM) system is presented for automatically classifying African elephant vocalizations. The development of the system is motivated by successful models from human speech analysis and recognition. Classification features include frequency-shifted Mel-frequency cepstral coefficients (MFCCs) and log energy, spectrally motivated features which are commonly used in human speech processing. Experiments, including vocalization type classification and speaker identification, are performed on vocalizations collected from captive elephants in a naturalistic environment. The system classified vocalizations with accuracies of 94.3% and 82.5% for type classification and speaker identification classification experiments, respectively. Classification accuracy, statistical significance tests on the model parameters, and qualitative analysis support the effectiveness and robustness of this approach for vocalization analysis in nonhuman species