Because of the somewhat more specific requirements of ..

In this paper, a system for polyphonic sound event detection and tracking is proposed, based on spectrogram factorisation techniques and state space models. The system extends probabilistic latent component analysis (PLCA) and is modelled around a 4-dimensional spectral template dictionary of frequency, sound event class, exemplar index, and sound state. In order to jointly track multiple overlapping sound events over time, the integration of linear dynamical systems (LDS) within the PLCA inference is proposed. The system assumes that the PLCA sound event activation is the (noisy) observation in an LDS, with the latent states corresponding to the true event activations. LDS training is achieved using fully observed data, making use of ground truth-informed event activations produced by the PLCA-based model. Several LDS variants are evaluated, using polyphonic datasets of office sounds generated from an acoustic scene simulator, as well as real and synthesized monophonic datasets for comparative purposes. Results show that the integration of LDS tracking within PLCA leads to an improvement of +8.5-10.5% in terms of frame-based F-measure as compared to the use of the PLCA model alone. In addition, the proposed system outperforms several state-of-the-art methods for the task of polyphonic sound event detection.

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In this paper we present a new method for musical audiosource separation, using the information from the musicalscore to supervise the decomposition process. An originalframework using nonnegative matrix factorization (NMF)is presented, where the components are initially learnt onsynthetic signals with temporal and harmonic constraints. Anew dataset of multitrack recordings with manually alignedMIDI scores is created (TRIOS), and we compare our separationresults with other methods from the literature usingthe BSS EVAL and PEASS evaluation toolboxes. The resultsshow a general improvement of the BSS EVAL metrics forthe various instrumental configurations used.


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Apr 08, 2013 · This section covers hardware requirements for ..

In this paper, we consider the dictionary learning problem for the sparse analysis model. A novel algorithm is proposed by adapting the simultaneous codeword optimization (SimCO) algorithm, based on the sparse synthesis model, to the sparse analysis model. This algorithm assumes that the analysis dictionary contains unit l2-norm atoms and learns the dictionary by optimization on manifolds. This framework allows multiple dictionary atoms to be updated simultaneously in each iteration. However, similar to several existing analysis dictionary learning algorithms, dictionaries learned by the proposed algorithm may contain similar atoms, leading to a degenerate (coherent) dictionary. To address this problem, we also consider restricting the coherence of the learned dictionary and propose Incoherent Analysis SimCO by introducing an atom decorrelation step following the update of the dictionary. We demonstrate the competitive performance of the proposed algorithms using experiments with synthetic data and image denoising as compared with existing algorithms.


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In this paper we shall see that neural networks and communications are interlinked in a number of ways, towards the goal of efficient communication of information. One concrete example of this is the use of neural networks to ensure efficient use of communication channels, through connection admission control in ATM networks. In addition, however, efficient communication is also important within a decision making system such as a neural network. Finally we examine what type of neural network solutions are suggested by this approach.

Patent US8797260 - Inertially trackable hand-held controller

We are looking to use pitch estimators to provide an accurate high-resolution pitch track for resynthesis of musical audio. We found that current evaluation measures such as gross error rate (GER) are not suitable for algorithm selection. In this paper we examine the issues relating to evaluating pitch estimators and use these insights to improve performance of existing algorithms such as the well-known YIN pitch estimation algorithm.

Google Summer of Code 2008 | Google Developers

In this paper, we consider the dictionary learning problem for the sparse analysis model. A novel algorithm is proposed by adapting the simultaneous codeword optimization (SimCO) algorithm, based on the sparse synthesis model, to the sparse analysis model. This algorithm assumes that the analysis dictionary contains unit l2-norm atoms and learns the dictionary by optimization on manifolds. This framework allows multiple dictionary atoms to be updated simultaneously in each iteration. However, similar to several existing analysis dictionary learning algorithms, dictionaries learned by the proposed algorithm may contain similar atoms, leading to a degenerate (coherent) dictionary. To address this problem, we also consider restricting the coherence of the learned dictionary and propose Incoherent Analysis SimCO by introducing an atom decorrelation step following the update of the dictionary. We demonstrate the competitive performance of the proposed algorithms using experiments with synthetic data and image denoising as compared with existing algorithms.