Abstract: Disclosed is an apparatus and method for power efficient processor scheduling of features. In one embodiment, features may be scheduled for sequential computing, and each scheduled feature may receive a sensor data sample as input. In one embodiment, scheduling may be based at least in part on each respective feature's estimated power usage. In one embodiment, a first feature in the sequential schedule of features may be computed and before computing a second feature in the sequential schedule of features, a termination condition may be evaluated.

Abstract: Systems and methods for classification of audio environments are disclosed. In one embodiment, a method of classifying an audio environment comprises sampling the audio environment to obtain sampled audio data in accordance with a first time interval, computing features of the sampled audio data, inferring an audio cluster identifier from the features of the sampled audio data in accordance with a second time interval, and updating an audio environment model using the features of the sampled audio data in accordance with a third time interval.

Abstract: Transmit power for an access point is controlled based on information received by the access point. For example, an access point may employ one or more algorithms that use messages received from nearby access terminals to maintain an acceptable tradeoff between providing an adequate coverage area for access point transmissions and mitigating interference that these transmissions cause at nearby access terminals. Here, the access point may employ a network listen-based algorithm upon initialization of the access terminal to provide preliminary transmit power control until sufficient information is collected for another transmit power control algorithm (e.g., an access terminal assisted algorithm). Also, the access terminal may employ an active access terminal protection scheme to mitigate interference the access point may otherwise cause to a nearby access terminal that is in active communication with another access point.

Abstract: Systems and methods for applying and using context labels for data clusters are provided herein. A method described herein for managing a context model associated with a mobile device includes obtaining first data points associated with a first data stream assigned to one or more first data sources; assigning ones of the first data points to respective clusters of a set of clusters such that each cluster is respectively assigned ones of the first data points that exhibit a threshold amount of similarity and are associated with times within a threshold amount of time of each other; compiling statistical features and inferences corresponding to the first data stream or one or more other data streams assigned to respective other data sources; assigning context labels to each of the set of clusters based on the statistical features and inferences.

Abstract: Systems and methodologies are described that facilitate identifying peers based upon encoded signals during peer discovery in a peer to peer network. For example, direct signaling that partitions a time-frequency resource into a number of segments can be utilized to communicate an identifier within a peer discovery interval; thus, a particular segment selected for transmission can signal a portion of the identifier, while a remainder can be signaled based upon tones communicated within the selected segment. Moreover, a subset of symbols within the resource can be reserved (e.g., unused) to enable identifying and/or correcting timing offset. Further, signaling can be effectuated over a plurality of peer discovery intervals such that partial identifiers communicated during each of the peer discovery intervals can be linked (e.g., based upon overlapping bits and/or bloom filter information).

Abstract: Systems and methodologies are described that facilitate identifying peers based upon encoded signals during peer discovery in a peer to peer network. For example, direct signaling that partitions a time-frequency resource into a number of segments can be utilized to communicate an identifier within a peer discovery interval; thus, a particular segment selected for transmission can signal a portion of the identifier, while a remainder can be signaled based upon tones communicated within the selected segment. Moreover, a subset of symbols within the resource can be reserved (e.g., unused) to enable identifying and/or correcting timing offset. Further, signaling can be effectuated over a plurality of peer discovery intervals such that partial identifiers communicated during each of the peer discovery intervals can be linked (e.g., based upon overlapping bits and/or bloom filter information).

Abstract: Various arrangements for detecting a type of sound, such as speech, are presented. A plurality of audio snippets may be sampled. A period of time may elapse between consecutive audio snippets. A hypothetical test may be performed using the sampled plurality of audio snippets. Such a hypothetical test may include weighting one or more hypothetical values greater than one or more other hypothetical values. Each hypothetical value may correspond to an audio snippet of the plurality of audio snippets. The hypothetical test may further include using at least the greater weighted one or more hypothetical values to determine whether at least one audio snippet of the plurality of audio snippets comprises the type of sound.

Abstract: Techniques are provided to infer a context associated with a mobile device based on aggregated data from a set of other mobile devices. The set of mobile devices can include mobile devices currently or previously near a location of the mobile device. Each mobile device in the set of other mobile devices can collect sensor data and infer a low-level context (e.g., “sitting” or “standing”). The low-level contexts can be aggregated. Based on the aggregated low-level contexts, a high-level context (e.g., “at a party” or “watching television”) associated with the mobile device can be inferred or a low-level context associated with the mobile device can be refined.

Abstract: Methods, systems, computer-readable media, and apparatuses for inferring context are provided. In one potential implementation, first context information associated with a first duration is identified, second context information is accessed to determine a context segmentation boundary; and the first context information and the second context information is then aggregated to generate an inferred segmented aggregated context. In a further implementation, the first context information is used to average inferred contexts, and the context segmentation boundary is used to reset a start time for averaging the first context information.

Abstract: A method of generating demographic statistics related to an event includes using sensor data acquired at a mobile device to estimate a value for a category of a demographic model of a user of the mobile device; using sensor data acquired at the mobile device to detect an occurrence of the event in which the user of the mobile device participates; and associating the estimated value for the category of the demographic model of the user of the mobile device with the event.

Abstract: Systems and methods herein enable a mobile device to detect that a user is traveling in association with a vehicle based at least on motion data. In some embodiments, accelerometer data is used. Motion data is leveraged in combination with various observations regarding vehicular movement to determine whether or not a mobile device is located in or on the vehicle. For instance, before entering the state of vehicular movement, it can be determined that the user is first in a walking state (e.g., walking to the car, bus, etc., and entering it). Likewise, after exiting the state of vehicular movement, the user re-enters the walking state (e.g., after stepping out of the car, bus, etc., the user again begins walking). Further, it can be determined that when the user is in the walking state, the accelerometer signals appear different to any accelerometer signals seen in the vehicular movement state.

Abstract: Systems and methods for speech detection in association with a mobile device are described herein. A method described herein for identifying presence of speech associated with a mobile device includes obtaining a plurality of audio samples from the mobile device while the mobile device operates in a mode distinct from a voice call operating mode, generating spectrogram data from the plurality of audio samples, and determining whether the plurality of audio samples include information indicative of speech by classifying the spectrogram data.

Abstract: Methods and apparatuses are provided that may be implemented in a mobile device to establish an orientation invariant reference frame based, at least in part, on measurement values from a three-dimensional accelerometer fixed to the mobile device; transform subsequent inertial sensor measurements to the reference frame; and classify a motion state of the mobile device relative to the reference frame based, at least in part, on the transformed inertial sensor measurements.

Abstract: Components, methods, and apparatuses are provided that may be used to obtain a plurality of time-referenced features extracted from signals received from one or more inertial sensors of a mobile device, wherein the time-referenced features are provided according to heterogeneous time scales. The time-referenced features are combined over a decision window to infer an activity of a user co-located with the mobile device.

Abstract: Components, methods, and apparatuses are provided that may be used to characterize a spectral envelope of at least one signal received from one or more inertial sensors of a mobile device co-located with a user engaged in an activity and to infer a position of the mobile device with respect to the user engaged in an activity based, at least in part, on the characterization of the spectral envelope.

Abstract: Techniques are provided to recognize a speaker's voice. In one embodiment, received audio data may be separated into a plurality of signals. For each signal, the signal may be associated with value/s for one or more features (e.g., Mel-Frequency Cepstral coefficients). The received data may be clustered (e.g., by clustering features associated with the signals). A predominate voice cluster may be identified and associated with a user. A speech model (e.g., a Gaussian Mixture Model or Hidden Markov Model) may be trained based on data associated with the predominate cluster. A received audio signal may then be processed using the speech model to, e.g.: determine who was speaking; determine whether the user was speaking; determining whether anyone was speaking; and/or determine what words were said. A context of the device or the user may then be inferred based at least partly on the processed signal.

Abstract: Techniques are provided to improve identification of a person using speaker recognition. In one embodiment, a unique social graph may be associated with each of a plurality of defined contexts. The social graph may indicate speakers likely to be present in a particular context. Thus, an audio signal including a speech signal may be collected and processed. A context may be inferred, and a corresponding social graph may be identified. A set of potential speakers may be determined based on the social graph. The processed signal may then be compared to a restricted set of speech models, each speech model being associated with a potential speaker. By limiting the set of potential speakers, speakers may be more accurately identified.

Abstract: A position estimate for a mobile device is generated using data from motion sensors, such as accelerometers, magnetometers, and/or gyroscopes, and data from light sensors, such as an ambient light sensor, proximity sensor and/or camera intensity sensor. A plurality of proposed positions with associated likelihoods is generated by analyzing information from the motion sensors and a list of candidate positions is produced based on information from the light sensors. At least one of the plurality of proposed positions is eliminated using the list of candidate positions and a position estimate for the mobile device is determined based on the remaining proposed positions and associated likelihoods. The proposed positions may be generated by extracting features from the information from the motion sensors and using models to generate likelihoods for the proposed positions. The likelihoods may be filtered over time. Additionally, a confidence metric may be generated for the estimated position.

Abstract: Components, methods, and apparatuses are provided for determining activity likelihood function values for an activity classification for two or more past epochs based, at least in part, on signals from one or more sensors of a mobile device. A method may comprise, for each of a plurality of activity classifications, determining activity likelihood function values for each of the plurality of activity classifications for two or more past epochs. The activity likelihood function values may be based on signals from one or more sensors of a mobile device. The method may also include combining the activity likelihood function values to determine a likelihood function for an activity classification at a present epoch. The method may also include inferring a present activity of a user co-located with the mobile device to be one of the activity classifications based on the determined likelihood functions for the activity classifications at the present epoch.

Abstract: Information about a context of a mobile device is inferred using measurements from light detectors of the mobile device. The light detectors may be located on multiple different sides of the mobile device. In some embodiments, measurements from one or more cameras may be used to infer context.