The INTERSPEECH 2017 Computational Paralinguistics Challenge : Addressee, Cold & Snoring

AbstractThe INTERSPEECH 2017 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well-defined conditions: In the Addressee sub-challenge, it has to be determined whether speech produced by an adult is directed towards another adult or towards a child; in the Cold sub-challenge, speech under cold has to be told apart from ‘healthy’ speech; and in the Snoring sub-challenge, four different types of snoring have to be classified. In this paper, we describe these sub-challenges, their conditions, and the baseline feature extraction and classifiers, which include data-learnt feature representations by end-to-end learning with convolutional and recurrent neural networks, and bag-of-audio-words for the first time in the challenge series.Abstract The INTERSPEECH 2017 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well-defined conditions: In the Addressee sub-challenge, it has to be determined whether speech produced by an adult is directed towards another adult or towards a child; in the Cold sub-challenge, speech under cold has to be told apart from ‘healthy’ speech; and in the Snoring sub-challenge, four different types of snoring have to be classified. In this paper, we describe these sub-challenges, their conditions, and the baseline feature extraction and classifiers, which include data-learnt feature representations by end-to-end learning with convolutional and recurrent neural networks, and bag-of-audio-words for the first time in the challenge series

Teaching machines on snoring: a benchmark on computer audition for snore sound excitation localisation

This paper proposes a comprehensive study on machine listening for localisation of snore sound excitation. Here we investigate the effects of varied frame sizes, and overlap of the analysed audio chunk for extracting low-level descriptors. In addition, we explore the performance of each kind of feature when it is fed into varied classifier models, including support vector machines, k-nearest neighbours, linear discriminant analysis, random forests, extreme learning machines, kernel-based extreme learning machines, multilayer perceptrons, and deep neural networks. Experimental results demonstrate that, wavelet packet transform energy can outperform most other features. A deep neural network trained with subband Energy ratios reaches the highest performance achieving an unweighted average recall of 72.8% from four types for snoring

Simulationen zum Kohlenstoffhaushalt europäischer Wälder des Forstlichen Umweltmonitorings (Level II) unter Klimawandel

Der Kohlenstoffhaushalt der Waldökosysteme ausgewählter ICP Forests Level II-Flächen wurde im Rahmen des FutMon-Projektes mit dem Modell BIO- ME-BGC (Version ZALF) simuliert. Die Level II-Datenbank des vTI-Instituts für Weltforstwirtschaft sowie zusätzliche Daten nationaler Forstinstitute bildeten die Datengrundlage für die Initialisierung und Kalibrierung des Modells. Mit dem kalibrierten Modell wurden Simulationen mit gegenwärtigen sowie mit in die Zukunft projizierten Klimabedingungen durchgeführt. Die Simulationen des C-haushalts mit dem kalibrierten Modell weisen die meisten Waldökosysteme aktuell als C-Senken aus. Unter Klimawandel steigen die Bilanzgrößen des C-Haushalts (NEP und NBP) an, so dass von einer erhöhten C-Sequestrierung ausgegangen werden kann

Simulationen zum Kohlenstoffhaushalt europäischer Wälder des Forstlichen Umweltmonitorings (Level II) unter Klimawandel

Der Kohlenstoffhaushalt der Waldökosysteme ausgewählter ICP Forests Level II-Flächen wurde im Rahmen des FutMon-Projektes mit dem Modell BIO-ME-BGC (Version ZALF) simuliert. Die Level II-Datenbank des vTI-Instituts für Weltforstwirtschaft sowie zusätzliche Daten nationaler Forstinstitute bildeten die Datengrundlage für die Initialisierung und Kalibrierung des Modells. Mit dem kalibrierten Modell wurden Simulationen mit gegenwärtigen sowie mit in die Zukunft projizierten Klimabedingungen durchgeführt. Die Simulationen des C-Haushalts mit dem kalibrierten Modell weisen die meisten Waldökosysteme aktuell als C-Senken aus. Unter Klimawandel steigen die Bilanzgrößen des C-Haushalts (NEP und NBP) an, so dass von einer erhöhten C-Sequestrierung ausgegangen werden kann

Simulation of stand transpiration based on a xylem water flow model for individual trees.

Quantifying the water exchange between a forest stand and the atmosphere is of major interest for the prediction of future growth conditions and the planning of silvicultural treatments. In the present study, we address (i) the uncertainties of sap flow estimations at the tree level and (ii) the performance of the simulation of stand transpiration. Terrestrial laser scan images (. TLS) of a mature beech stand (. Fagus sylvatica L.) in Southwestern Germany serve as input data for a representation of the aboveground tree architecture of the study stand. In the single-tree xylem water flow model (. XWF) used here, 98 beech trees are represented by 3D graphs of connected cylinders with explicit orientation and size. Beech-specific hydraulic parameters and physical properties of individual trees determine the physiological response of the tree model to environmental conditions.The XWF simulations are performed without further calibration to sap flow measurements. The simulations reliably match up with sap flow estimates derived from sap flow density measurements. The density measurements strongly depend on individual sapwood area estimates and the characterization of radial sap flow density gradients with xylem depth. Although the observed pure beech stand is even-aged, we observe a high variability in sap flow rates among the individual trees. Simulations of the individual sap flow rates show a corresponding variability due to the distribution of the crown projection area in the canopy and the different proportions of sapwood area.Stand transpiration is obtained by taking the sum of 98 single-tree simulations and the corresponding sap flow estimations, which are then compared with the stand-level root water uptake model (. RWU model) simulation. Using the RWU model results in a 35% higher simulation of seasonal stand transpiration relative to the XWF model. These findings demonstrate the importance of individual tree dimensions and stand heterogeneity assessments in estimating stand water use. As a consequence of species-specific model parameterization and precise TLS-based stand characterization, the XWF model is applicable to various sites and tree species and is a promising tool for predicting the possible water supply limitations of pure and mixed forest stands