46,969 research outputs found
Multi-Layer Neural Networks for Quality of Service oriented Server-State Classification in Cloud Servers
Task allocation systems in the Cloud have been recently proposed so that their performance is optimised in real-time based on reinforcement learning with spiking Random Neural Networks (RNN). In this paper, rather than reinforcement learning, we suggest the use of multi-layer neural network architectures to infer the state of servers in a dynamic networked Cloud environment, and propose to select the most adequate server based on the task that optimises Quality of Service. First, a procedure is presented to construct datasets for state classification by collecting time-varying data from Cloud servers that have different resource configurations, so that the identification of server states is carried out with supervised classification. We test four distinct multi-layer neural network architectures to this effect: multi-layer dense clusters of RNNs (MLRNN), the hierarchical extreme learning machine (H-ELM), the multi-layer perceptron, and convolutional neural networks. Our experimental results indicate that server-state identification can be carried out efficiently and with the best accuracy using the MLRNN and H-ELM
A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor
In this paper, a siamese DNN model is proposed to learn the characteristics
of the audio dynamic range compressor (DRC). This facilitates an intelligent
control system that uses audio examples to configure the DRC, a widely used
non-linear audio signal conditioning technique in the areas of music
production, speech communication and broadcasting. Several alternative siamese
DNN architectures are proposed to learn feature embeddings that can
characterise subtle effects due to dynamic range compression. These models are
compared with each other as well as handcrafted features proposed in previous
work. The evaluation of the relations between the hyperparameters of DNN and
DRC parameters are also provided. The best model is able to produce a universal
feature embedding that is capable of predicting multiple DRC parameters
simultaneously, which is a significant improvement from our previous research.
The feature embedding shows better performance than handcrafted audio features
when predicting DRC parameters for both mono-instrument audio loops and
polyphonic music pieces.Comment: 8 pages, accepted in IJCNN 201
End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks
In this work we present a novel end-to-end framework for tracking and
classifying a robot's surroundings in complex, dynamic and only partially
observable real-world environments. The approach deploys a recurrent neural
network to filter an input stream of raw laser measurements in order to
directly infer object locations, along with their identity in both visible and
occluded areas. To achieve this we first train the network using unsupervised
Deep Tracking, a recently proposed theoretical framework for end-to-end space
occupancy prediction. We show that by learning to track on a large amount of
unsupervised data, the network creates a rich internal representation of its
environment which we in turn exploit through the principle of inductive
transfer of knowledge to perform the task of it's semantic classification. As a
result, we show that only a small amount of labelled data suffices to steer the
network towards mastering this additional task. Furthermore we propose a novel
recurrent neural network architecture specifically tailored to tracking and
semantic classification in real-world robotics applications. We demonstrate the
tracking and classification performance of the method on real-world data
collected at a busy road junction. Our evaluation shows that the proposed
end-to-end framework compares favourably to a state-of-the-art, model-free
tracking solution and that it outperforms a conventional one-shot training
scheme for semantic classification
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