Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: Examples are disclosed herein that relate to depth imaging techniques using ultrasound One example provides an ultrasonic depth sensing system configured to, for an image frame, emit an ultrasonic pulse from each of a plurality of transducers, receive a reflection of each ultrasonic pulse at a microphone array, perform transmit beamforming and also receive beamforming computationally after receiving the reflections, form a depth image, and output the depth image for the image frame.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: Examples are disclosed herein that relate to depth imaging techniques using ultrasound One example provides an ultrasonic depth sensing system configured to, for an image frame, emit an ultrasonic pulse from each of a plurality of transducers, receive a reflection of each ultrasonic pulse at a microphone array, perform transmit beamforming and also receive beamforming computationally after receiving the reflections, form a depth image, and output the depth image for the image frame.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: This document describes word-dependent language models, as well as their creation and use. A word-dependent language model can permit a speech-recognition engine to accurately verify that a speech utterance matches a multi-word phrase. This is useful in many contexts, including those where one or more letters of the expected phrase are known to the speaker.

Abstract: Oftentimes individuals have a number of objectives to complete while traveling in a vehicle. The objectives can be arranged automatically and an associated route can be produced such that the objectives can be completed in an effective manner. Data related to the objectives can be collected such as a traffic pattern on paths near a location the objective is to take place. Locations for the objectives to be completed can be determined automatically as well as provided by user. Analysis of the collected data can take place and based on a result of the analysis, an efficient route is produced.

Abstract: A user can be compensated for taking detours from a projected route. Commonly, the reason for the compensation is that the user will be subjected to advertising, the user will pass by an establishment she is likely to visit, or to ease traffic congestion. Analysis of an area takes place and monetization opportunities are determined based upon the results of the analysis. A route between at least about two locations can be altered such that the user is provided a reward, commonly in an optimized manner.

Abstract: Speaker identification techniques are described. In one or more implementations, sample data is received at a computing device of one or more user utterances captured using a microphone. The sample data is processed by the computing device to identify a speaker of the one or more user utterances. The processing involving use of a feature set that includes features obtained using a filterbank having filters that space linearly at higher frequencies and logarithmically at lower frequencies, respectively, features that model the speaker's vocal tract transfer function, and features that indicate a vibration rate of vocal folds of the speaker of the sample data.

Abstract: A noise suppressor for altering a speech signal is trained based on a speech recognition system. An objective function can be utilized to adjust parameters of the noise suppressor. The noise suppressor can be used to alter speech signals for the speech recognition system.

Abstract: The perceptual sound quality of desired audio signals (e.g., human voice) captured by an electronic device (e.g., cell phone) are improved by reducing ambient noise according to an algorithm that acts upon audio signals captured from a front and rear direction. More particularly, audio signals captured by two directional microphones pointing in opposite directions (e.g., a front microphone which receives audio signals from a forward direction and a rear microphone which receives audio signals from a rear direction) are classified and subsequently enhanced (e.g., unwanted signals are suppressed) according to a probability of their source (e.g., front, rear, or noise) thereby providing an improved perceptual sound recording than each microphone individually. The resultant signals provide decreased noise since the contribution of the front and rear microphones are taken into consideration and the signal from the more relevant (e.g., in the direction from which sound is coming) microphone is utilized.

Abstract: When users travel to an intended destination, a plurality of information can be beneficial to assist their travel. If a person is traveling to a crowded event, then information can be provided such that congested traffic areas can be provided. There can be financial opportunities available in relation to providing information concerning an intended destination. An advertiser can pay money to have information played about the advertiser when it relates to the intended destination. Furthermore, a user can pay money for detailed data concerning an intended location, such as where cheapest parking is located.

Abstract: A route can be generated through utilization of a conventional manner, such as a portable electronic device accessing a database with roads, traffic information, weather data, and the like. As a user approaches a private area, the route can be augmented with travel information concerning the private area. Artificial intelligence techniques can be used to determine if a route should be augmented, to infer what augmentations to make, etc.

Abstract: Sound signals to be output from a loudspeaker array are modified by a plurality of filters designed according to an unconstrained optimization procedure to improve overall performance (e.g., power, directivity) of the loudspeaker array. More particularly, respective filters are configured to receive a signal to be output to a plurality of loudspeakers. Upon receiving the signal, the respective filters individually modify the received signal according to the results of the unconstrained optimization procedure and then output the individually modified signals to respective loudspeakers. The unconstrained optimization procedure takes into account manufacturing tolerances and individually enhances the signal output to each of a plurality of individual loudspeakers within an array to achieve an overall improvement in performance.

Abstract: A multimedia system configured to receive user input in the form of a spelled character sequence is provided. In one implementation, a spell mode is initiated, and a user spells a character sequence. The multimedia system performs spelling recognition and recognizes a sequence of character representations having a possible ambiguity resulting from any user and/or system errors. The sequence of character representations with the possible ambiguity yields multiple search keys. The multimedia system performs a fuzzy pattern search by scoring each target item from a finite dataset of target items based on the multiple search keys. One or more relevant items are ranked and presented to the user for selection, each relevant item being a target item that exceeds a relevancy threshold. The user selects the indented character sequence from the one or more relevant items.