Abstract: A system processes data signals consisting of sums of independent signal terms, zero or more of which signal terms may already have been identified, in order to generate one or more additional terms. Deflated versions of the data signals are created by subtracting from the data signals any previously identified signal terms. Additional independent signal terms are computed using a set of reference signals organized into mutually independent partioning support sets. The images of each support set are computed on the data signals. Computed images on a data signal that are non-zero are identified as additional independent signal terms of that data signal.

Abstract: In environments (such as acoustic and bioelectrical environments) characterized by multiple simultaneous sources, effective blind source separation from sensor response mixtures becomes difficult as the number of sources increases-especially when the true number of sources is both unknown and changing over time. However, in some environments, non-sensor information can provide useful hypotheses for some sources. Embodiments of the present invention provide an adaptive filtering architecture for validating such source hypotheses, extracting an estimated representation of source signals corresponding to valid hypotheses, and improving the separation of the remaining “hidden” source signals from the sensor response mixtures.

Abstract: A system processes data signals consisting of sums of independent signal terms, zero or more of which signal terms may already have been identified, in order to generate one or more additional terms. Deflated versions of the data signals are created by subtracting from the data signals any previously identified signal terms. Additional independent signal terms are computed using a set of reference signals organized into mutually independent partioning support sets. The images of each support set are computed on the data signals. Computed images on a data signal that are non-zero are identified as additional independent signal terms of that data signal.

Abstract: In environments (such as acoustic and bioelectrical environments) characterized by multiple simultaneous sources, effective blind source separation from sensor response mixtures becomes difficult as the number of sources increases-especially when the true number of sources is both unknown and changing over time. However, in some environments, non-sensor information can provide useful hypotheses for some sources. Embodiments of the present invention provide an adaptive filtering architecture for validating such source hypotheses, extracting an estimated representation of source signals corresponding to valid hypotheses, and improving the separation of the remaining “hidden” source signals from the sensor response mixtures.

Abstract: A system processes data signals consisting of sums of independent signal terms, zero or more of which signal terms may already have been identified, in order to generate one or more additional terms. Deflated versions of the data signals are created by subtracting from the data signals any previously identified signal terms. Additional independent signal terms are computed using a set of reference signals organized into mutually independent partioning support sets. The images of each support set are computed on the data signals. Computed images on a data signal that are non-zero are identified as additional independent signal terms of that data signal.

Abstract: A computer-implemented method comprises: (A) receiving first acoustic data representing a first cough train of a first human subject, wherein the first cough train comprises at least one first cough of the first human subject; (B) identifying at least one first value of at one first acoustic property of the first acoustic data; and (C) determining, based on the at least one first value of the at least one first acoustic property, whether the first acoustic data indicates that the first human subject has a severe respiratory illness.

Abstract: In environments (such as acoustic and bioelectrical environments) characterized by multiple simultaneous sources, effective blind source separation from sensor response mixtures becomes difficult as the number of sources increases—especially when the true number of sources is both unknown and changing over time. However, in some environments, non-sensor information can provide useful hypotheses for some sources. Embodiments of the present invention provide an adaptive filtering architecture for validating such source hypotheses, extracting an estimated representation of source signals corresponding to valid hypotheses, and improving the separation of the remaining “hidden” source signals from the sensor response mixtures.

Abstract: A system processes data signals consisting of sums of independent signal terms, zero or more of which signal terms may already have been identified, in order to generate one or more additional terms. Deflated versions of the data signals are created by subtracting from the data signals any previously identified signal terms. Additional independent signal terms are computed using a set of reference signals organized into mutually independent partioning support sets. The images of each support set are computed on the data signals. Computed images on a data signal that are non-zero are identified as additional independent signal terms of that data signal.

Abstract: A system processes data signals consisting of sums of independent signal terms in order to generate one or more of those terms. The generated terms are computed using a set of reference signals to construct alternative support sets, whose images are computed on the data signals. Computed images that are independent of the residue of the data signal minus the image are identified as independent signal terms, as are the residues. These initial signal terms are used to compute additional terms. Identified terms from different data signals are organized into independent slices, and slices whose terms are supported by sets of reference signals are associated with those supporting sets.

Abstract: The available audio tracks of a multi-track television program are processed to remove or reduce certain components of the program, especially audience sounds, such as laughter. Such processing may be particularly applicable to “canned” laughter. This applies to any program which is supplied with a plurality of audio channels, whether provided by cable, DVD, Internet streaming, or otherwise.

Abstract: A computer-implemented method comprises: (A) receiving first acoustic data representing a first cough train of a first human subject, wherein the first cough train comprises at least one first cough of the first human subject; (B) identifying at least one first value of at one first acoustic property of the first acoustic data; and (C) determining, based on the at least one first value of the at least one first acoustic property, whether the first acoustic data indicates that the first human subject has a severe respiratory illness.

Abstract: A computer-implemented method comprises: (A) receiving first acoustic data representing a first cough train of a first human subject, wherein the first cough train comprises at least one first cough of the first human subject; (B) identifying at least one first value of at one first acoustic property of the first acoustic data; and (C) determining, based on the at least one first value of the at least one first acoustic property, whether the first acoustic data indicates that the first human subject has a severe respiratory illness.

Abstract: A system processes data signals consisting of sums of independent signal terms in order to generate one or more of those terms. The generated terms are computed using a set of reference signals to construct alternative support sets, whose images are computed on the reference signals. Computed images that are independent of the residue of the data signal minus the image are identified as independent signal terms, as are the residues. These initial signal terms are used to compute additional terms. Identified terms from different data signals are organized into independent slices, and slices whose terms are supported by sets of reference signals are associated with those supporting sets.

Abstract: A computer-implemented method comprises: (A) receiving first acoustic data representing a first cough train of a first human subject, wherein the first cough train comprises at least one first cough of the first human subject; (B) identifying at least one first value of at one first acoustic property of the first acoustic data; and (C) determining, based on the at least one first value of the at least one first acoustic property, whether the first acoustic data indicates that the first human subject has a severe respiratory illness.

Abstract: A computer-implemented method identifies a spoken audio signal representing speech of a person and estimates a physiological state of the person based on the spoken audio signal. For example, the method may identify articulatory patterns (such as landmarks) in the speech and estimate the person's physiological state based on those articulatory patterns. The method may estimate, for example, the amount of time the person has been without sleep. The method may produce the physiological state estimate without performing speech recognition on the spoken audio signal. The method may produce the physiological state estimate in real-time.

Abstract: In environments (such as acoustic and bioelectrical environments) characterized by multiple simultaneous sources, effective blind source separation from sensor response mixtures becomes difficult as the number of sources increases—especially when the true number of sources is both unknown and changing over time. However, in some environments, non-sensor information can provide useful hypotheses for some sources. Embodiments of the present invention provide an adaptive filtering architecture for validating such source hypotheses, extracting an estimated representation of source signals corresponding to valid hypotheses, and improving the separation of the remaining “hidden” source signals from the sensor response mixtures.

Abstract: A computer-implemented method comprises: (A) receiving first acoustic data representing a first cough train of a first human subject, wherein the first cough train comprises at least one first cough of the first human subject; (B) identifying at least one first value of at one first acoustic property of the first acoustic data; and (C) determining, based on the at least one first value of the at least one first acoustic property, whether the first acoustic data indicates that the first human subject has a severe respiratory illness.

Abstract: A computer-implemented method identifies a spoken audio signal representing speech of a person and estimates a physiological state of the person based on the spoken audio signal. For example, the method may identify articulatory patterns (such as landmarks) in the speech and estimate the person's physiological state based on those articulatory patterns. The method may estimate, for example, the amount of time the person has been without sleep. The method may produce the physiological state estimate without performing speech recognition on the spoken audio signal. The method may produce the physiological state estimate in real-time.

Abstract: A technique for determining the number of constraints on a set of input data, or equivalently the topological dimension, especially when such data are produced by a nonlinear system, such as a pathological vocal system or econometric data and the like. The technique characterizes the tangent space about a predetermined base point by identifying a maximal set of non-redundant nonlinear fits to the data. It needs only a modest number of data points and does not assume prior knowledge of the functional form of the true constraints, other than smoothness. Each fit is equivalent to a set of contours (including curves, surfaces, and other manifolds), with the data themselves all lying along the zero-value contour of the fit. For each fit, the gradient of the fit at the base point in the uphill direction across the contours identifies the constraint direction.