Abstract: Features that may be computed from a harmonic signal include a fractional chirp rate, a pitch, and amplitudes of the harmonics. A fractional chirp rate may be estimated, for example, by computing scores corresponding to different fractional chirp rates and selecting a highest score. A first pitch may be computed from a frequency representation that is computed using the estimated fractional chirp rate, for example, by using peak-to-peak distances in the frequency distribution. A second pitch may be computed using the first pitch, and a frequency representation of the signal, for example, by using correlations of portions of the frequency representation. Amplitudes of harmonics of the signal may be determined using the estimated fractional chirp rate and second pitch. Any of the estimated fractional chirp rate, second pitch, and harmonic amplitudes may be used for further processing, such as speech recognition, speaker verification, speaker identification, or signal reconstruction.

Abstract: An estimate of a fractional chirp rate of a signal may be computed by using multiple frequency representations of the signal. A first frequency representation may be computed using a first fractional chirp rate and a first score may be computed using the first frequency representation that indicates a match between the first fractional chirp rate and a fractional chirp rate of the signal. A second frequency representation may be computed using a second fractional chirp rate and a second score may be computed using the second frequency representation that indicates a match between the second fractional chirp rate and the fractional chirp rate of the signal. The fractional chirp rate of the signal may be estimated using the first score and the second score, for example, by selecting a fractional chirp rate corresponding to a highest score.

Abstract: A method and system gathers user tax data for a user, from one or more sources of tax information, to prepare the user's tax return within a tax return preparation system, in one embodiment. The method and system populate a database with relationships between existing user metadata and one or more sources of tax information, in one embodiment. The method and system analyze new user metadata for the user to identify which of the one or more sources of tax information are relevant to the user, in one embodiment. The method and system retrieve new user tax data from the identified ones of the one or more sources of tax information that are relevant to the new user metadata of the user, in one embodiment. The method and system populate the user's tax return with the new user data, within the tax return preparation system, in one embodiment.

Abstract: A method and system evaluates analytics modules to improve a personalization of tax questions delivered to a user in a tax return preparation system, according to one embodiment. The method and system retrieves historical tax return data and selects one or more interchangeable analytics modules for evaluation with the historical tax return data, according to one embodiment. The method and system applies the historical tax return data to the one or more analytics modules that are selected for evaluation, according to one embodiment. The method and system receives analytics outputs from the one or more analytics modules, in response to applying the historical tax return data, according to one embodiment. The method and system determines an effectiveness of each of the one or more analytics modules by correlating the analytics outputs with at least part of the historical tax return data, according to one embodiment.

Abstract: A method and system selects one or more interchangeable analytics modules for use in a tax return preparation system to provide a customized electronic tax return preparation interview to a user, according to one embodiment. The method and system receive user data associated with a user, according to one embodiment. The method and system apply one of a number of selection techniques to determine which of one or more analytics modules to use within the tax return preparation system, according to one embodiment. The method and system apply the one or more analytics modules to the user data to determine the relevance of tax return preparation interview questions to the user, according to one embodiment. The method and system deliver tax return preparation interview questions to the user, based on the determined relevance of the number of tax return preparation interview questions to the user, according to one embodiment.

Abstract: A method and system for providing a tax return preparation system with interchangeable analytics modules includes providing one or more interchangeable analytics modules. Each of the interchangeable analytics modules includes one or more analytics algorithms used to select user experience elements to be included in a tax return preparation interview process presented to a user through one or more tax return preparation systems. The one or more interchangeable analytics modules are distinct and independent analytical components provided to the tax return preparation system that can be interchanged, overwritten, and interfaced with individually, and without otherwise changing and/or modifying the tax return preparation system. Consequently, a tax return preparation system can provide a tax return preparation interview process capable of dynamically evolving to meet the specific needs of a given user.

Abstract: Methods, systems and articles of manufacture for using one or more predictive models to predict which tax matters are relevant to a particular taxpayer during preparation of an electronic tax return. A tax return preparation system accesses taxpayer data such as personal data and/or tax data regarding the particular taxpayer. The system executes a predictive model which receives the taxpayer data as inputs to the predictive model. The predictive model generates as output(s) one or more predicted tax matters which are determined to be likely to be relevant to the taxpayer. The system may then determine tax questions to present to the user based at least in part upon the predicted tax matters determined by the predictive model.

Abstract: In various embodiments, systems and methods are provided for query expansion using add-on terms with classifications. A query is received. An add-on term is identified for the query. A classification is determined for the add-on term. The classification is a designation associated with the add-on term that is used to distinguish the add-on term from the query. An appended query is generated based on the add-on term. The appended query is generated by concatenating the query with the add-on term. The appended query is executed on a resource stack as a single reformulated query to identify one or more resources. Upon execution, the classification of the add-on term distinguishes the one or more resources identified for the add-on term based on tagging the one or more resources with the classification of the add-on term. The appended query is used to generate content items.

Abstract: Voice enhancement and/or speech features extraction may be performed on noisy audio signals. An input signal may convey audio comprising a speech component superimposed on a noise component. The input signal may be segmented into discrete successive time windows including a first time window spanning a duration greater than a sampling interval of the input signal. A transform may be performed on individual time windows of the input signal to obtain corresponding sound models of the input signal in the individual time windows. A first sound model may describe a superposition of harmonics sharing a common pitch and chirp in the first time window of the input signal. Linear fits in time of the sound models over individual time windows of the input signal may be obtained. The linear fits may include a first linear fit in time of the first sound model over the first time window.

Abstract: Speech portions of a sound model may be identified using various statistics associated with the sound model for voice enhancement of noisy audio signals. A spectral motion transform may be performed on an input signal to obtain a linear fit in time of a sound model of the input signal. Statistics may be extracted from the linear fit of the sound model of the input signal. Speech portions of the linear fit of the sound model of the input signal may be identified by detecting a presence of harmonics as a function of time in the linear fit of the sound model of the input signal based on individual ones of the extracted statistics. An output signal may be provided that conveys audio comprising a reconstructed speech component of the input signal with a noise component of the input signal being suppressed.

Abstract: A dataflow of a distributed application is visualized in a locally simulated execution environment. A scheduler receives a job graph which includes a graph of computational vertices that are designed to be executed on multiple distributed computer systems. The scheduler queries a graph manager to determine which computational vertices of the job graph are ready for execution in a local execution environment. The scheduler queries a cluster manager to determine the organizational topology of the distributed computer systems to simulate the determined topology in the local execution environment. The scheduler queries a data manager to determine data storage locations for each of the computational vertices indicated as being ready for execution in the local execution environment. The scheduler also indicates an instance of each computational vertex to be spawned and executed in the local execution environment based on the organizational topology and indicated data storage locations.

Abstract: In various embodiments, systems and methods are provided for query expansion using add-on terms with classifications. A query is received. An add-on term is identified for the query. A classification is determined for the add-on term. The classification is a designation associated with the add-on term that is used to distinguish the add-on term from the query. An appended query is generated based on the add-on term. The appended query is generated by concatenating the query with the add-on term. The appended query is executed on a resource stack as a single reformulated query to identify one or more resources. Upon execution, the classification of the add-on term distinguishes the one or more resources identified for the add-on term based on tagging the one or more resources with the classification of the add-on term. The appended query is used to generate content items.

Abstract: A scheduler receives a job graph which includes a graph of computational vertices that are designed to be executed on multiple distributed computer systems. The scheduler queries a graph manager to determine which computational vertices of the job graph are ready for execution in a local execution environment. The scheduler queries a cluster manager to determine the organizational topology of the distributed computer systems to simulate the determined topology in the local execution environment. The scheduler queries a data manager to determine data storage locations for each of the computational vertices indicated as being ready for execution in the local execution environment. The scheduler also indicates to a vertex spawner that an instance of each computational vertex is to be spawned in the local execution environment based on the organizational topology and indicated data storage locations, and indicates to the local execution environment that the spawned vertices are to be executed.

Abstract: The present invention extends to methods, systems, and computer program products for propagating unhandled exceptions in distributed execution environments, such as clusters. A job (e.g., a query) can include a series of computation steps that are executed on multiple compute nodes each processing parts of a distributed data set. Unhandled exceptions can be caught while computations are running on data partitions of different compute nodes. Unhandled exception objects can be stored in a serialized format in a compute node's local storage (or an alternate central location) along with auxiliary details such as the data partition being processed at the time. Stored serialized exception objects for a job can be harvested and aggregated in a single container object. The single container object can be passed back to the client.

Abstract: The present invention extends to methods, systems, and computer program products for propagating unhandled exceptions in distributed execution environments, such as clusters. A job (e.g., a query) can include a series of computation steps that are executed on multiple compute nodes each processing parts of a distributed data set. Unhandled exceptions can be caught while computations are running on data partitions of different compute nodes. Unhandled exception objects can be stored in a serialized format in a compute node's local storage (or an alternate central location) along with auxiliary details such as the data partition being processed at the time. Stored serialized exception objects for a job can be harvested and aggregated in a single container object. The single container object can be passed back to the client.

Abstract: A scheduler receives a job graph which includes a graph of computational vertices that are designed to be executed on multiple distributed computer systems. The scheduler queries a graph manager to determine which computational vertices of the job graph are ready for execution in a local execution environment. The scheduler queries a cluster manager to determine the organizational topology of the distributed computer systems to simulate the determined topology in the local execution environment. The scheduler queries a data manager to determine data storage locations for each of the computational vertices indicated as being ready for execution in the local execution environment. The scheduler also indicates to a vertex spawner that an instance of each computational vertex is to be spawned in the local execution environment based on the organizational topology and indicated data storage locations, and indicates to the local execution environment that the spawned vertices are to be executed.