A method for real-time speaker recognition including obtaining speech data of a speaker, extracting, using a processor of a computer, a coarse feature of the speaker from the speech data, identifying the speaker as belonging to a pre-determined speaker cluster based on the coarse feature of the speaker, extracting, using the processor of the computer, a plurality of Mel-Frequency Cepstral Coefficients (MFCC) and a plurality of Gaussian Mixture Model (GMM) components from the speech data, determining a biometric signature of the speaker based on the plurality of MFCC and the plurality of GMM components, and determining in real time, using the processor of the computer, an identity of the speaker by comparing the biometric signature of the speaker to one of a plurality of biometric signature libraries associated with the pre-determined speaker cluster

Hierarchical real-time speaker recognition for biometric VoIP verification and targeting

Systems and methods for identifying which video segment is being displayed on a screen of a television system. The video segment is identified by deriving data from the television signals, the derived data being indicative of the video segment being displayed on the screen. This feature can be used to extract a viewer's reaction (such as changing the channel) to a specific video segment (such as an advertisement) and reporting the extracted information as metrics. The systems and methods may further provide contextually targeted content to the television system. The contextual targeting is based on not only identification of the video segment being displayed, but also a determination concerning the playing time or offset time of the particular portion of the video segment being currently displayed.

Method for identifying video segments and displaying contextually targeted content on a connected television

Provided herein is an electronic device and method of voice recognition, the method including analyzing an audio signal of a first frame when the audio signal is input and extracting a first feature value; determining a similarity between the first feature value extracted from the audio signal of the first frame and a first feature value extracted from an audio signal of a previous frame; analyzing the audio signal of the first frame and extracting a second feature value when the similarity is below a predetermined threshold value; and comparing the extracted first feature value and the second feature value and at least one feature value corresponding to a pre-defined voice signal and determining whether or not the audio signal of the first frame is a voice signal, and thus the electronic device may detect only a voice section from the audio signal while improving the processing speed.

Electronic device and method capable of voice recognition

Systems and processes for improved video editing, summarization and navigation based on generation and analysis of metadata are described. The metadata may be content-based (e.g., differences between neighboring frames, exposure data, key frame identification data, motion data, or face detection data) or non-content-based (e.g., exposure, focus, location, time) and used to prioritize and/or classify portions of video. The metadata may be generated at the time of image capture or during post-processing. Prioritization information, such as a score for various portions of the image data may be based on the metadata and/or image data. Classification information such as the type or quality of a scene may be determined based on the metadata and/or image data. The classification and prioritization information may be metadata and may be used to automatically remove undesirable portions of the video, generate suggestions during editing or automatically generate summary video.

Video analysis techniques for improved editing, navigation, and summarization

According to one embodiment, a video processing apparatus includes an acquisition unit, a first extraction unit, a generation unit, a second extraction unit, a computation unit and a selection unit The acquisition unit is configured to acquire video streams A first extraction unit is configured to analyze at least one of the moving pictures and the sounds for each video stream and to extract feature values A generation unit is configured to generate segments by dividing each video stream, and to generate associated segment groups A second extraction unit is configured to extract the associated segment groups that number of associated segments is greater than or equal to threshold as common video segment groups A computation unit is configured to compute summarization score A selection unit is configured to select segments used for a summarized video as summarization segments from the common video segment groups based on the summarization score

Video processing apparatus, method and system

An acquisition unit acquires feature vectors (the number is N). A first selection unit selects first neighbor features (k (1≦k≦N)) of each feature vector in order of higher similarity from the feature vectors. A second selection unit generates a plurality of groups each including similar feature vectors from the feature vectors, and selects second neighbor features (u (1≦k+u≦N−2)) of each feature vector in order of higher similarity. Each of the second neighbor features is differently included in a group. A determination unit calculates a density of each feature vector by using a threshold, the first neighbor features and the second neighbor features, and determines feature vectors to be classified into the same class as each feature vector by using the density and a threshold. A classification unit classifies the feature vectors into a plurality of classes by using the selection result. A control unit controls each threshold.

Signal classification apparatus

Acoustic models to provide features to a speech signal are created based on speech features included in regions where similarities of acoustic models created based on speech features in a certain time length are equal to or greater than a predetermined value. Feature vectors acquired by using the acoustic models of the regions and the speech features to provide features to speech signals of second segments are grouped by speaker.

Indexing apparatus, indexing method, and computer program product

Likelihood calculation means extracts audio features expressing features of a voice signal and a non-voice signal from an acquired audio signal, and calculates likelihood expressing probability that the voice signal is included in the audio signal using the audio features. Spectral feature extraction means performs a frequency analysis to the audio signal to extract a spectral feature. Using the spectral feature, first basis matrix producing means produces a first basis matrix expressing the feature of the non-voice signal. Second basis matrix producing means specifies a component having a high association with the voice signal in the first basis matrix using the likelihood, and excludes the component to produce a second basis matrix. Spectral feature estimation means estimates a spectral feature of the voice signal or a spectral feature of the non-voice signal by performing nonnegative matrix factorization to the spectral feature using the second basis matrix.

Audio signal processing apparatus and audio signal processing method

In an embodiment, an image processing device includes: a first calculating unit; a selecting unit; an extracting unit; a second calculating unit; a determining unit; and a generating unit. The first calculating unit calculates posture information of a subject from a first image including the subject. The selecting unit selects a second image associated with the posture information from a plurality of second images including clothing. The extracting unit extracts a first feature of the subject from a first contour in the first image and extracts a second feature from a second contour in the second image. The second calculating unit calculates a first difference between the first feature and the second feature. The determining unit determines a transparency that is higher as the first difference is larger. The generating unit generates a composite image combining the first image and the second image with the determined transparency.

Image processing device, method, and computer program product

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