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

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

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

Application of Speech Recognition to African Elephant (Loxodonta Africana) Vocalizations

This paper presents a novel application of speech processing research, classification of African elephant vocalizations. Speaker identification and call classification experiments are performed on data collected from captive African elephants in a naturalistic environment. The features used for classification are 12 mel-frequency cepstral coefficients plus log energy computed using a shifted filter bank to emphasize the infrasound range of the frequency spectrum used by African elephants. Initial classification accuracies of 83.8% for call classification and 88.1% for speaker identification were obtained. 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

Evolution of KSC EGS Post Space Shuttle - Success Factors and Lessons Learned

The Human Exploration and Operations Mission Directorate (HEOMD) Knowledge Capture & Transfer (KCT) team conducted video interviews with element managers in the Exploration Ground Systems (EGS) Program Office at Kennedy Space Center (KSC). The immediate goal was to capture a point-in-time profile of challenges, solutions, and lessons learned derived from EGS element development activity from the end of the Space Shuttle Program (SSP) to the present time

A Delay-Discounting Primer

Given the importance of research findings and the potential of further research to aid in the prediction and control of impulsivity, the primary focus of this chapter (and this book) is on choice and the failure of future events to affect current decisions. In this primer chapter, we consider two types of impulsive choice: (a) preferring a smaller-sooner reward while forgoing a larger-later one and (b) preferring a larger-later aversive outcome over a smaller-sooner one. The first of these is exemplified by the toy-pilfering child with whom we opened this chapter. Taking the toy is immediately rewarded, but it is a short-lived reward because the caretaker soon returns the toy to the victimized peer. Undoubtedly, the child would prefer to play with the toy for a longer period of time, but waiting until the toy is dropped by the peer seems a weak reinforcer when compared with brief access now. To put an economic term on this phenomenon, the child appears to have discounted the value of the delayed but otherwise preferred reward. Delay discounting describes the process of devaluing behavioral outcomes, be they rewarding or aversive events, that happen in the future (and perhaps the past; see chap. 7, this volume). This chapter provides a primer in delay discounting; it is intended for readers who have only a limited background in the procedures, measures, and outcomes of studies examining this form of impulsive choice. Following an overview of the delay-discounting process, its quantification, and its implications for the human condition, emphasis is placed on procedures (and critiques of these procedures). The remainder of the book is concerned with experimental findings, and for the most part, we do not review these here

Control of light transmission through opaque scattering media in space and time

We report the first experimental demonstration of combined spatial and temporal control of light trajectories through opaque media. This control is achieved by solely manipulating spatial degrees of freedom of the incident wavefront. As an application, we demonstrate that the present approach is capable to form bandwidth-limited ultrashort pulses from the otherwise randomly transmitted light with a controllable interaction time of the pulses with the medium. Our approach provides a new tool for fundamental studies of light propagation in complex media and has potential for applications for coherent control, sensing and imaging in nano- and biophotonics