The LOST Algorithm: finding lines and separating speech mixtures

Robust clustering of data into linear subspaces is a frequently encountered problem. Here, we treat clustering of one-dimensional subspaces that cross the origin. This problem arises in blind source separation, where the subspaces correspond directly to columns of a mixing matrix. We propose the LOST algorithm, which identifies such subspaces using a procedure similar in spirit to EM. This line finding procedure combined with a transformation into a sparse domain and an L1-norm minimisation constitutes a blind source separation algorithm for the separation of instantaneous mixtures with an arbitrary number of mixtures and sources. We perform an extensive investigation on the general separation performance of the LOST algorithm using randomly generated mixtures, and empirically estimate the performance of the algorithm in the presence of noise. Furthermore, we implement a simple scheme whereby the number of sources present in the mixtures can be detected automaticall

Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint

Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), a method for finding parts-based representations of non-negative data. Here, we present an extension to convolutive NMF that includes a sparseness constraint, where the resultant algorithm has multiplicative updates and utilises the beta divergence as its reconstruction objective. In combination with a spectral magnitude transform of speech, this method discovers auditory objects that resemble speech phones along with their associated sparse activation patterns. We use these in a supervised separation scheme for monophonic mixtures, finding improved separation performance in comparison to classic convolutive NMF

Block Coordinate Descent for Sparse NMF

Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data analysis. An important variant is the sparse NMF problem which arises when we explicitly require the learnt features to be sparse. A natural measure of sparsity is the L$_0$ norm, however its optimization is NP-hard. Mixed norms, such as L$_1$/L$_2$ measure, have been shown to model sparsity robustly, based on intuitive attributes that such measures need to satisfy. This is in contrast to computationally cheaper alternatives such as the plain L$_1$ norm. However, present algorithms designed for optimizing the mixed norm L$_1$/L$_2$ are slow and other formulations for sparse NMF have been proposed such as those based on L$_1$ and L$_0$ norms. Our proposed algorithm allows us to solve the mixed norm sparsity constraints while not sacrificing computation time. We present experimental evidence on real-world datasets that shows our new algorithm performs an order of magnitude faster compared to the current state-of-the-art solvers optimizing the mixed norm and is suitable for large-scale datasets

UTCI field measurements in an urban park in Florence (Italy)

The aim of this study is to evaluate human thermal comfort in different green area settings in the city of Florence by using the Universal Thermal Climate Index (UTCI). Field measurements of air temperature, solar radiation, relative humidity, wind speed and black globe thermometer were collected during hot summer days in various parts of Cascine Park, the biggest urban park in Florence (Italy). UTCI was evaluated over different surfaces (asphalt, gravel and grass) completely exposed to the sun or shaded by a large lime tree (Tilia × europaea). The results showed strong differences in UTCI values depending on the exposure to tree shade, while no significant difference was found among ground-cover materials when all surfaces were equally exposed to solar radiation. Future studies are needed to investigate the microclimatic effects of different tree species on UTCI

Sequential Sparse NMF

Nonnegative Matrix Factorization (NMF) is a standard tool for data analysis. An important variant is the Sparse NMF problem. A natural measure of sparsity is the L₀ norm, however its optimization is NP-hard. Here, we consider a sparsity measure linear in the ratio of the L₁ and L₂ norms, and propose an efficient algorithm to handle the norm constraints which arise when optimizing this measure. Although algorithms for solving these are available, they are typically inefficient. We present experimental evidence that our new algorithm performs an order of magnitude faster compared to the previous state-of-the-art

Sequential Sparse NMF

Nonnegative Matrix Factorization (NMF) is a standard tool for data analysis. An important variant is the Sparse NMF problem. A natural measure of sparsity is the L₀ norm, however its optimization is NP-hard. Here, we consider a sparsity measure linear in the ratio of the L₁ and L₂ norms, and propose an efficient algorithm to handle the norm constraints which arise when optimizing this measure. Although algorithms for solving these are available, they are typically inefficient. We present experimental evidence that our new algorithm performs an order of magnitude faster compared to the previous state-of-the-art

The Compositional Nature of Verb and Argument Representations in the Human Brain

How does the human brain represent simple compositions of objects, actors,and actions? We had subjects view action sequence videos during neuroimaging (fMRI) sessions and identified lexical descriptions of those videos by decoding (SVM) the brain representations based only on their fMRI activation patterns. As a precursor to this result, we had demonstrated that we could reliably and with high probability decode action labels corresponding to one of six action videos (dig, walk, etc.), again while subjects viewed the action sequence during scanning (fMRI). This result was replicated at two different brain imaging sites with common protocols but different subjects, showing common brain areas, including areas known for episodic memory (PHG, MTL, high level visual pathways, etc.,i.e. the 'what' and 'where' systems, and TPJ, i.e. 'theory of mind'). Given these results, we were also able to successfully show a key aspect of language compositionality based on simultaneous decoding of object class and actor identity. Finally, combining these novel steps in 'brain reading' allowed us to accurately estimate brain representations supporting compositional decoding of a complex event composed of an actor, a verb, a direction, and an object.Comment: 11 pages, 6 figure