Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection

Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks (CNN) are able to extract higher level features that are invariant to local spectral and temporal variations. Recurrent neural networks (RNNs) are powerful in learning the longer term temporal context in the audio signals. CNNs and RNNs as classifiers have recently shown improved performances over established methods in various sound recognition tasks. We combine these two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it on a polyphonic sound event detection task. We compare the performance of the proposed CRNN method with CNN, RNN, and other established methods, and observe a considerable improvement for four different datasets consisting of everyday sound events.Comment: Accepted for IEEE Transactions on Audio, Speech and Language Processing, Special Issue on Sound Scene and Event Analysi

Aero/structural tailoring of engine blades (AERO/STAEBL)

This report describes the Aero/Structural Tailoring of Engine Blades (AERO/STAEBL) program, which is a computer code used to perform engine fan and compressor blade aero/structural numerical optimizations. These optimizations seek a blade design of minimum operating cost that satisfies realistic blade design constraints. This report documents the overall program (i.e., input, optimization procedures, approximate analyses) and also provides a detailed description of the validation test cases

Family Supportive Supervision Around the Globe

Family-supportive supervision (FSS) refers to the degree to which employees perceive their immediate supervisors as exhibiting attitudes and behaviors that are supportive of their family role demands (Hammer, Kossek, Zimmerman, & Daniels, 2007; Kossek, Pichler, Bodner & Hammer, 2011: Thomas & Ganster, 1995). A growing body of research suggests that leaders\u27 and supervisors\u27 social support of employees\u27 needs to jointly carry out work and family demands is important for general health and job attitudes, such as satisfaction, work-family conflict, commitment, and intention to turn over (Hammer, Kossek, Anger, Bodner, & Zimmerman, 2009; Kossek et al., 2011). Thus, employee perceptions of FSS are critical to individual well-being and productivity (Hammer, Kossek, Yragui, Bodner, & Hansen, 2009). [excerpt

Structural Tailoring of Advanced Turboprops (STAT) programmer's manual

The Structural Tailoring of Advanced Turboprops (STAT) computer program was developed to perform numerical optimizations on highly swept propfan blades. This manual describes the functionality of the STAT system from a programmer's viewpoint. It provides a top-down description of module intent and interaction. The purpose of this manual is to familiarize the programmer with the STAT system should he/she wish to enhance or verify the program's function

A generic news story segmentation system and its evaluation

The paper presents an approach to segmenting broadcast TV news programmes automatically into individual news stories. We first segment the programme into individual shots, and then a number of analysis tools are run on the programme to extract features to represent each shot. The results of these feature extraction tools are then combined using a support vector machine trained to detect anchorperson shots. A news broadcast can then be segmented into individual stories based on the location of the anchorperson shots within the programme. We use one generic system to segment programmes from two different broadcasters, illustrating the robustness of our feature extraction process to the production styles of different broadcasters

3-D inelastic analysis methods for hot section components (base program)

A 3-D inelastic analysis methods program consists of a series of computer codes embodying a progression of mathematical models (mechanics of materials, special finite element, boundary element) for streamlined analysis of combustor liners, turbine blades, and turbine vanes. These models address the effects of high temperatures and thermal/mechanical loadings on the local (stress/strain) and global (dynamics, buckling) structural behavior of the three selected components. These models are used to solve 3-D inelastic problems using linear approximations in the sense that stresses/strains and temperatures in generic modeling regions are linear functions of the spatial coordinates, and solution increments for load, temperature and/or time are extrapolated linearly from previous information. Three linear formulation computer codes, referred to as MOMM (Mechanics of Materials Model), MHOST (MARC-Hot Section Technology), and BEST (Boundary Element Stress Technology), were developed and are described

Improving parallel program performance using critical path analysis

A programming tool that performs analysis of critical paths for parallel programs has been developed. This tool determines the critical path for the program as scheduled onto a parallel computer with P processing elements, the critical path for the program expressed as a data flow graph (when maximal parallelism can be expressed), and the minimum number of processing elements (P_opt) needed to obtain maximum program speedup. Experiments were performed using several versions of a Gaussian elimination program to examine how speedup varied with changes in granularity and critical path length. These experiments showed that when the available numer of processing elements P < P_opt, increasing granularity improved program speedup more than reducing (the data flow graph's) critical path length, whereas when P ≥ P_opt, increasing granularity degraded program speedup while reducing critical path length improved program speedup

A Speech Recognizer based on Multiclass SVMs with HMM-Guided Segmentation

Automatic Speech Recognition (ASR) is essentially a problem of pattern classification, however, the time dimension of the speech signal has prevented to pose ASR as a simple static classification problem. Support Vector Machine (SVM) classifiers could provide an appropriate solution, since they are very well adapted to high-dimensional classification problems. Nevertheless, the use of SVMs for ASR is by no means straightforward, mainly because SVM classifiers require an input of fixed-dimension. In this paper we study the use of a HMM-based segmentation as a mean to get the fixed-dimension input vectors required by SVMs, in a problem of isolated-digit recognition. Different configurations for all the parameters involved have been tested. Also, we deal with the problem of multi-class classification (as SVMs are initially binary classifers), studying two of the most popular approaches: 1-vs-all and 1-vs-1