Patent application number | Description | Published |
20130197904 | Indirect Model-Based Speech Enhancement - Enhanced speech is produced from a mixed signal including noise and the speech. The noise in the mixed signal is estimated using a vector-Taylor series. The estimated noise is in terms of a minimum mean-squared error. Then, the noise is subtracted from the mixed signal to obtain the enhanced speech. | 08-01-2013 |
20130262083 | Method and Apparatus for Processing Text with Variations in Vocabulary Usage - Text is processed to construct a model of the text. The text has a shared vocabulary. The text is partitioned into sets and subsets of texts. The usage of the shared vocabulary in two or more sets is different, and the topics of two or more subsets are different. A probabilistic model is defined for the text. The probabilistic model considers each word in the text to be a token having a position and a word value, and the usage of the shared vocabulary, topics, subtopics, and word values for each token in the text are represented using distributions of random variables in the probabilistic model, wherein the random variables are discrete. Parameters are estimated for the model corresponding to the vocabulary usages, the word values, the topics, and the subtopics associated with the words. | 10-03-2013 |
20130317804 | Method of Text Classification Using Discriminative Topic Transformation - Text is classified by determining text features from the text, and transforming the text features to topic features. Scores are determined for each topic features using a discriminative topic model. The model includes a classifier that operates on the topic features, wherein the topic features are determined by the transformation from the text features, and the transformation is optimized to maximize the scores of a correct class relative to the scores of incorrect classes. Then, a class label with a highest score is selected for the text. In situations where the classes are organized in a hierarchical structure, the discriminative topic models apply to classes at each level conditioned on previous levels and scores are combined across levels to evaluate the highest scoring class labels. | 11-28-2013 |
20140114650 | Method for Transforming Non-Stationary Signals Using a Dynamic Model - An input signal, in the form of a sequence of feature vectors, is transformed to an output signal by first storing parameters of a model of the input signal in a memory. Using the vectors and the parameters, a sequence of vectors of hidden variables is inferred. There is at least one vector h | 04-24-2014 |
20140244214 | Method for Localizing Sources of Signals in Reverberant Environments Using Sparse Optimization - Source signals emitted in a reverberant environment from different locations are processed by first receiving input signals corresponding to the source signals by a set of sensors. Then, a sparsity-based support estimation is applied to the input signals according to a reverberation model to produce estimates of the source signals and locations of a set of sources emitting the source signals. | 08-28-2014 |
20150025880 | Method for Processing Speech Signals Using an Ensemble of Speech Enhancement Procedures - A method processes an acoustic signal that is a mixture of a target signal and interfering signals by first enhancing the acoustic signal by a set of enhancement procedures to produce a set of initial enhanced signals. Then, an ensemble learning procedure is applied to the acoustic signal and the set of initial enhancement signals to produce features of the acoustic signal. | 01-22-2015 |
20150088422 | Method and System for Dynamically Adapting user Interfaces in Vehicle Navigation Systems to Minimize Interaction Complexity - A method adapts a user interface of a vehicle navigation system. Based on an input vector representing a current state related to the vehicle, probabilities of actions are predicted to achieve a next state using a predictive model representing previous states. Then, a subset of the actions with highest probabilities that minimize a complexity of interacting with the vehicle navigation system are displayed in the vehicle. | 03-26-2015 |
20150112670 | Denoising Noisy Speech Signals using Probabilistic Model - A method determines from an input noisy signal sequences of hidden variables including at least one sequence of hidden variables representing an excitation component of the clean speech signal, at least one sequence of hidden variables representing a filter component of the clean speech signal, and at least one sequence of hidden variables representing the noise signal. The sequences of hidden variables include hidden variables determined as a non-negative linear combination of non-negative basis functions. The determination uses the model of the clean speech signal that includes a non-negative source-filter dynamical system (NSFDS) constraining the hidden variables representing the excitation and the filter components to be statistically dependent over time. The method generates an output signal using a product of corresponding hidden variables representing the excitation and the filter components. | 04-23-2015 |