Summary1. Passive acoustic monitoring is frequently used for marine mammals, and more recently it has also become popular for terrestrial species. Key advantages are the monitoring of (1) elusive species, (2) different taxa simultaneously, (3) large temporal and spatial scales, (4) with reduced human presence and (5) with considerable time saving for data processing. However, terrestrial sound environments can be highly complex; they are very challenging when trying to automatically detect and classify vocalizations because of low signal-to-noise ratios. Therefore, most studies have used manual preselection of high-quality sounds to achieve better classification rates. Consequently, most systems have never been validated under realistic field conditions. 2. In this study, we evaluated the performance of a passive acoustic monitoring system for four primate species in the highly noisy rain forest environment of the Ta€ ı National Park, Côte d'Ivoire. We collected 12 851 h of recordings with 20 autonomous recording units and did not preselect high-quality sounds manually. To automatically detect and classify the sounds of interest, we used an automated system built on speaker segmentation, support vector machines and Gaussian mixture models. One hundred and seventy-nine hours of recordings were used for validating the system. 3. The system performed well in detecting the loud calls of Cercopithecus diana and Colobus polykomos with a recall of 50% and 42%, respectively. Recall rates were lower for Pan troglodytes and Procolobus badius. To determine the presence of Cercopithecus diana and Colobus polykomos with a certainty of P > 0Á999, 2 and 7 h of recordings were needed, respectively. For these two species, our automated approach reflected the spatio-temporal distribution of vocalization events well. Despite the seemingly low precision, time investment for the manual removal of false positives in the system's output was only 3Á5% compared to a human collecting and processing the primate vocalization data. 4. The proposed monitoring system is already fully applicable for Cercopithecus diana and Colobus polykomos, whereas it needs further improvement for the other species tested. In principle, it can be applied to any distinctive animal sound and can be implemented for the collection of acoustic data for behavioural, ecological and conservation studies.

In this paper, we present a feature-based approach for the classification of different playing techniques in bass guitar recordings. The applied audio features are chosen to capture typical instrument sounds induced by 10 different playing techniques. A novel database that consists of approx. 4300 isolated bass notes was assembled for the purpose of evaluation. The usage of domain-specific features in a combination of feature selection and feature space transformation techniques improved the classification accuracy by over 27% points in comparison to a state-of-the-art baseline system. Classification accuracy reached 93.25% and 95.61% for the recognition of plucking and expression styles respectively

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Supervised learning requires adequately labeled training data. In this paper, we present an approach for automatic detection of outliers in image training sets using an one-class support vector machine (SVM). The image sets were downloaded from photo communities solely based on their tags. We conducted four experiments to investigate if the one-class SVM can automatically differentiate between target and outlier images. As testing setup, we chose four image categories, namely Snow & Skiing, Family & Friends, Architecture & Buildings and Beach. Our experiments show that for all tests a significant tendency to remove the outliers and retain the target images is present. This offers a great possibility to gather big data sets from the Web without the need for a manual review of the images

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