Abstract: A method and system for adapting automated interactions that allows the interactive behavior of an automated system, or the nature of the interaction elements implemented thereon, to be adapted to properties and/or behaviors of users of such systems in order to enhance operating convenience. Interaction adaptation is performed with reference to user groups to which the users are allocated.

Abstract: Gesture recognition system for remote controlling a TV is disclosed. The system comprises: (a) a webcam for capturing a video sequence of a user which is composed of video frames (b) a hand tracker for receiving the video sequence, and when a hand is determined within a frame of the video sequence, the hand tracker calculates the hand centroid; (c) a trajectory buffer for receiving plurality of the hand centroids as determined from plurality of frames respectively; (d) trajectory segmentation module for continuously inspecting the buffer for possible inclusion of relevant trajectories that relate to a hand gesture pattern, and whenever a relevant trajectory is detected, it extracts a respective trajectory segment, and conveys the segment into a gesture classifier; and (e) the gesture classifier for verifying whether the segment relates to a specific gesture command, and when affirmative, the gesture classifier transfers the respective command to the TV.

Abstract: The invention relates to a gesture recognition system for remote controlling a TV, which comprises: (a) video capturing device for capturing a video sequence of a user which is composed of video frames, and conveying the same into a hand tracker; (b) hand tracker module for receiving said video sequence, inspecting each of said video frames for possible inclusion of a user hand within it, and when a hand is determined to be found in a frame, calculating the hand centroid, and outputting the centroid coordinates into a trajectory buffer; (c) trajectory buffer for receiving from said hand tracker each of said hand centroid coordinates, and maintaining in storage at least several tens of recent hand centroids, that altogether define a trajectory; (d) trajectory segmentation module for continuously inspecting said buffer for possible inclusion of relevant trajectories that are suspected to relate to a hand gesture pattern, and whenever a relevant trajectory is detected, extracting a trajectory segment of the same

Abstract: A method and system for adapting automated interactions that allows the interactive behavior of an automated system, or the nature of the interaction elements implemented thereon, to be adapted to properties and/or behaviors of users of such systems in order to enhance operating convenience. Interaction adaptation is performed with reference to user groups to which the users are allocated.

Abstract: A method for signal controlled switching between audio coding schemes includes receiving input audio signals, classifying a first set of the input audio signals as speech or non-speech signals, coding the speech signals using a time domain coding scheme, and coding the nonspeech signals using a transform coding scheme. A multicode coder has an audio signal input and a switch for receiving the audio signal inputs, the switch having a time domain encoder, a transform encoder, and a signal classifier for classifying the audio signals generally as speech or non-speech, the signal classifier directing speech audio signals to the time domain encoder and non-speech audio signals to the transform encoder. A multicode decoder is also provided.

Abstract: A method and a circuit arrangement for automatic voice activity detection on the basis of the wavelet transformation. A voice activity detection circuit or module (5) is used to control a speech encoder (9) and a speech decoder (22), as well as a background noise encoder (10) and a background noise decoder (23) in order to perform source-controlled reduction of the mean transmission rate. After segmenting a speech signal, a wavelet transformation is computed for each frame, from which a set of parameters is determined, from which in turn a set of binary decision variables is calculated with the help of fixed thresholds in an arithmetic circuit (32). The decision variables control a decision logic circuit (42), whose result, after time smoothing in a time smoothing circuit (44), provides the statement “speech present/no speech” for each frame.

Abstract: A method and a circuit arrangement for automatic voice activity detection on the basic of the wavelet transformation. A voice activity detection circuit or module (5) is used to control a speech encoder (9) and a speech decoder (22), as well as a background noise encoder (10) and a background noise decoder (23) in order to perform source-controlled reduction of the mean transmission rate. After segmenting a speech signal, a wavelet transformation is computed for each frame from, which a set of parameters is determined, from which in turn a set of binary decision variables is calculated with the help of fixed thresholds in an arithmetic circuit (32). The decision variables control a decision logic circuit (42), whose result after time smoothing in a time smoothing circuit (44), provides the statement “speech present/no speech” for each frame.

Abstract: A method and a device are described for classifying speech on the basis of the wavelet transformation for low-bit-rate speech coding processes. The method and the device permit a more robust classifier of speech signals for signal-matched control of speech coding processes in order to reduce the bit rate without affecting the speech quality or to increase the quality at the same bit rate. The method provides that, after segmenting the speech signal, a wavelet transformation is calculated for each frame, from which a set of parameters is determined with the help of adaptive thresholds. The parameters control a finite-state model, which subdivides the frames into shorter subframes if required, and classifies each subframe into one of several classes typical for speech coding. The speech signal is classified on the basis of the wavelet transformation for each time frame. Thus both a high time resolution (location of pulses) and frequency resolution (good mean values) can be achieved.