Speaker de-identification is an interesting and newly investigated task in speech processing. In the current implementations, this task is based on transforming one speaker speech to another speaker in order to hide the speaker identity. In this paper we present a discriminative approach for human speaker selection for speaker de-identification. We used two modules, a speaker identification system and a speaker transformation one, to select the most appropriate speaker to transform the source speaker speech from a set of speakers. In order to select the target speaker, we minimize the identification confidence of the transformed speech as the source speaker and maximize the confusion about the transformed speech membership to the rest of the speaker models and the identification confidence of the re-transformed speech using the source speaker model. These three factors are combined to achieve overall optimization performance in order to select the best target speaker to transform the source.
In this work we propose a method for classification of sports types from combined audio and visual features extracted from thermal video. From audio Mel Frequency Cepstral Coefficients (MFCC) are extracted, and PCA are applied to reduce the feature space to 10 dimensions. From the visual modality short trajectories are constructed to represent the motion of players. From these, four motion features are extracted and combined directly with audio features for classification. A k-nearest neighbour classifier is applied for classification of 180 1-minute video sequences from three sports types. Using 10-fold cross validation a correct classification rate of 96.11% is obtained with multimodal features, compared to 86.67% and 90.00% using only visual or audio features, respectively.
Abstract. PCG approaches are commonly categorised as constructive, generateand-test or search-based. Each of these approaches has its distinctive advantages and drawbacks. In this paper, we propose an approach to Content Generation (CG)-in particular level generation -that combines the advantages of constructive and search-based approaches thus providing a fast, flexible and reliable way of generating diverse content of high quality. In our framework, CG is seen from a new perspective which differentiates between two main aspects of the gameplay experience, namely the order of the in-game interactions and the associated level design. The framework first generates timelines following the search-based paradigm. Timelines are game-independent and they reflect the rhythmic feel of the levels. A progressive, constructive-based approach is then implemented to evaluate timelines by mapping them into level designs. The framework is applied for the generation of puzzles for the Cut the Rope game and the results in terms of performance, expressivity and controllability are characterised and discussed.
In this paper we present a procedural content generator using Non-negative Matrix Factorisation (NMF). We use representative levels from five dissimilar content generators to train NMF models that learn patterns about the various components of the game. The constructed models are then used to automatically generate content that resembles the training data as well as to generate novel content through exploring new combinations of patterns. We describe the methodology followed and we show that the generator proposed has a more powerful capability than each of generator taken individually. The generator's output is compared to the other generators using a number of expressivity metrics. The results show that the proposed generator is able to resemble each individual generator as well as demonstrating ability to cover a wider and more novel content space.
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