Abstract: A method models trajectories of a signal source. Training signals generated by a signal source moving along known trajectories are acquired by each sensor in an array of sensors. Phase differences between all unique pairs of the training signals are determined. A wrapped-phase hidden Markov model is constructed from the phase differences. The wrapped-phase hidden Markov model includes multiple Gaussian distributions to model the known trajectories of the signal source.

Abstract: A method and system separates components in individual signals, such as time series data streams. A single sensor acquires concurrently multiple individual signals. Each individual signal is generated by a different source. An input non-negative matrix representing the individual signals is constructed. The columns of the input non-negative matrix represent features of the individual signals at different instances in time. The input non-negative matrix is factored into a set of non-negative bases matrices and a non-negative weight matrix. The set of bases matrices and the weight matrix represent the individual signals at the different instances of time.

Abstract: A system and method recognizes speech securely. The system includes a client and a server, The client is configured to provide securely speech in a form of an observation sequence of symbols, and the server is configured to provide securely a multiple trained hidden Markov models (HMMs), each trained HMM including a multiple states, a state transition probability distribution and an initial state distribution, and each state including a subset of the observation symbols and an observation symbol probability distribution. The observation symbol probability distributions are modeled by mixtures of Gaussian distributions. Also included are means for determining securely, for each HMM, a likelihood the observation sequence is produced by the states of the HMM, and means for determining a particular symbol with a maximum likelihood of a particular subset of the symbols corresponding to the speech.

Abstract: A method decomposes input data acquired of a signal. An input signal is sampled to acquire input data. The input data is represented as a probability distribution. An expectation-maximization procedure is applied iteratively to the probability distribution to determine components of the probability distributions.

Abstract: A method decomposes input data acquired of a signal. An input signal is sampled to acquire input data. The input data is represented as a probability distribution. An expectation-maximization procedure is applied iteratively to the probability distribution to determine components of the probability distributions.

Abstract: A method detects objects in a scene over time. Sets of time-aligned features are extracted from multiple signals representing a scene over time; each signal is acquired using a different modality. Each set of time-aligned features is arranged as a vector in a matrix to which a first transform is applied to produce a compressed matrix. A second transform is applied to the compressed matrix to extract spatio-temporal profiles of objects occurring in the scene.

Abstract: A first party has a data vector x and a second party has a classifier defined as a set of multivariate Gaussian distributions. A secure inner dot product procedure is applied to each multivariate Gaussian distribution and the data vector x to produce a vector ai for the first party and a vector bi for the second party for each application. The secure inner dot product is then applied to each vector bi and the data vector x to produce a scalar ri for the first party and a scalar qi for the second party for each application. A summed vector of elements [(a1x+q1+r1), . . . , (aNx+qN+rN)] is formed, and an index I of the summed vector for a particular element having a maximum value is the class of the data vector x.

Abstract: A method models trajectories of a signal source. Training signals generated by a signal source moving along known trajectories are acquired by each sensor in an array of sensors. Phase differences between all unique pairs of the training signals are determined. A wrapped-phase hidden Markov model is constructed from the phase differences. The wrapped-phase hidden Markov model includes multiple Gaussian distributions to model the known trajectories of the signal source.

Abstract: A method generates envelope spectra and harmonic spectra from an input broad-band training acoustic signal. Corresponding non-negative envelope bases are trained for the envelope spectra and non-negative harmonic bases are trained for the harmonic spectra using convolutive non-negative matrix factorization. Higher-band frequencies are generated for an input lower-band acoustic signal according to the non-negative envelope bases and the non-negative harmonic bases. Then, the input lower-band acoustic signal is combined with the higher-band frequencies to produce an output broad-band acoustic signal.

Abstract: A method constructs a location model for locating and tracking sources of acoustic signals. Acoustic training signals are acquired from an acoustic training source at an unknown location in an environment with an array of microphones placed at unknown positions in the environment. From each acoustic training signal, relative acoustic features are extracted to construct a location model that is trained with the relative acoustic features.

Abstract: A system and method includes visualizing acoustic activity, or its detected features, and displaying the acoustic activity as visual signals alongside videos acquired by cameras. Either through use of simple signal processing, or through use of more sophisticated audio analysis or sound recognition, useful information can be extracted from acoustic signals. The extracted information can be transformed to visual signals superimposed on the video signals acquired by the cameras.

Abstract: The invention provides sensors and event monitoring to a cellular telephone to perform surveillance. Using audio and visual sensing capabilities, an environment can be monitored to detect significant surveillance events and to trigger surveillance alerts in response to the events, or to report current sensor information in response to an in-coming call.

Abstract: A method and system separates components in individual signals, such as time series data streams. A single sensor acquires concurrently multiple individual signals. Each individual signal is generated by a different source. An input non-negative matrix representing the individual signals is constructed. The columns of the input non-negative matrix represent features of the individual signals at different instances in time. The input non-negative matrix is factored into a set of non-negative bases matrices and a non-negative weight matrix. The set of bases matrices and the weight matrix represent the individual signals at the different instances of time.

Abstract: A method and system propagates only interesting sound information to a user who is otherwise acoustically isolated from sound. This is a task particularly useful for people that work in, or enjoy noisy environments, and are placed in a soundproof room, or are otherwise unable to detect critical sounds. By automatically detecting critical sounds, the user can be alerted by a signal generated in response to detecting the selected sound information.

Abstract: A method synchronizes signals from unsynchronized sensors spatially dispersed in an environment. Each sensor acquires an unsynchronized signal from the environment. An identical timing signal is received in each sensor. Frequencies of the timing signal are substantially disjoint of frequencies of the unsynchronized signals. The timing signal is combined with the unsynchronized signal in each sensor. The combined signals are received by a signal processor where the combined signals are separated to recover the unsynchronized signals and the timing signals. Then, the unsynchronized signals can be time-aligned according to the recovered timing signals to produce time-aligned signals.

Abstract: A method synchronizes signals from unsynchronized sensors spatially dispersed in an environment. Each sensor acquires an unsynchronized signal from the environment. An identical timing signal is received in each sensor. Frequencies of the timing signal are substantially disjoint of frequencies of the unsynchronized signals. The timing signal is combined with the unsynchronized signal in each sensor. The combined signals are received by a signal processor where the combined signals are separated to recover the unsynchronized signals and the timing signals. Then, the unsynchronized signals can be time-aligned according to the recovered timing signals to produce time-aligned signals.

Abstract: A method detects objects in a scene over time. Sets of time-aligned features are extracted from multiple signals representing a scene over time; each signal is acquired using a different modality. Each set of time-aligned features is arranged as a vector in a matrix to which a first transform is applied to produce a compressed matrix. A second transform is applied to the compressed matrix to extract spatio-temporal profiles of objects occurring in the scene.

Abstract: A system for analyzing a mixture of chemical components. First, a liquid or gas chromatograph produces samples of the mixture. The samples include overlapping components. A spectrometer measures wavelength of each sample. A memory stores the measured wavelengths of each sample as rows in a first matrix. Independent component analysis is applied to the first matrix to obtain a second matrix and a third matrix. Columns of the first matrix are elution profiles of distinct component groups, and rows of the third matrix are corresponding spectra of the distinct component groups.

Abstract: A method detects components of a non-stationary signal. The non-stationary signal is acquired and a non-negative matrix of the non-stationary signal is constructed. The matrix includes columns representing features of the non-stationary signal at different instances in time. The non-negative matrix is factored into characteristic profiles and temporal profiles.