Abstract: A system that analyzes music to detect musical beats and to rectify beats that are out of sync with the actual beat phase of the music. The music analysis includes onset detection, tempo/meter estimation, and beat analysis, which includes the rectification of out-of-sync beats.

Abstract: A method is provided for real-time speaker change detection and speaker tracking in a speech signal. The method is a “coarse-to-refine” process, which consists of two stages: pre-segmentation and refinement. In the pre-segmentation process, the covariance of a feature vector of each segment of speech is built initially. A distance is determined based on the covariance of the current segment and a previous segment; and the distance is used to determine if there is a potential speaker change between these two segments. If there is no speaker change, the model of current identified speaker model is updated by incorporating data of the current segment. Otherwise, if there is a speaker change, a refinement process is utilized to confirm the potential speaker change point.

Abstract: Systems and methods for learning-based automatic commercial content detection are described. In one aspect, program data is divided into multiple segments. The segments are analyzed to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content. The context-based features are a function of single-side left and/or right neighborhoods of segments of the multiple segments.

Abstract: A system that analyzes music to detect musical beats and to rectify beats that are out of sync with the actual beat phase of the music. The music analysis includes onset detection, tempo/meter estimation, and beat analysis, which includes the rectification of out-of-sync beats.

Abstract: Systems and methods to automatically edit a video to generate a video summary are described. In one aspect, sub-shots are extracted from the video. Importance measures are calculated for at least a portion of the extracted sub-shots. Respective relative distributions for sub-shots having relatively higher importance measures as compared to importance measures of other sub-shots are determined. Based on the determined relative distributions, sub-shots that do not exhibit a uniform distribution with respect to other sub-shots in the particular ones are dropped. The remaining sub-shots are connected with respective transitions to generate the video summary.

Abstract: A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.

Abstract: A system and methods analyze music to detect musical beats and to rectify beats that are out of sync with the actual beat phase of the music. The music analysis includes onset detection, tempo/meter estimation, and beat analysis, which includes the rectification of out-of-sync beats.

Abstract: A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.

Abstract: A system that analyzes music to detect musical beats and to rectify beats that are out of sync with the actual beat phase of the music. The music analysis includes onset detection, tempo/meter estimation, and beat analysis, which includes the rectification of out-of-sync beats.

Abstract: A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.

Abstract: A system that analyzes music to detect musical beats and to rectify beats that are out of sync with the actual beat phase of the music. The music analysis includes onset detection, tempo/meter estimation, and beat analysis, which includes the rectification of out-of-sync beats.

Abstract: A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.

Abstract: A system and methods analyze music to detect musical beats and to rectify beats that are out of sync with the actual beat phase of the music. The music analysis includes onset detection, tempo/meter estimation, and beat analysis, which includes the rectification of out-of-sync beats.

Abstract: Systems and methods are described that implement personalized karaoke, wherein a user's personal home video and photographs are used to form a background for the lyrics during a karaoke performance. An exemplary karaoke apparatus is configured to segment visual content to produce a plurality of sub-shots and to segment music to produce a plurality of music sub-clips. Having produced the visual content sub-shots and music sub-clips, the exemplary karaoke apparatus shortens some of the plurality of sub-shots to a length of a corresponding music sub-clip from within the plurality of music sub-clips. The plurality of sub-shots is then displayed as a background to lyrics associated with the music, thereby adding interest to a karaoke performance.

Abstract: Systems and methods for extracting a music snippet from a music stream are described. In one aspect, one or more music sentences are extracted from the music stream. The one or more sentences are extracted as a function of peaks and valleys of acoustic energy across sequential music stream portions. The music snippet is selected based on the one or more music sentences.

Abstract: Methods and apparatuses are provided for automatically generating video data based on still image data. Certain aspects of the video may also be configured to correspond to audio features identified within associated audio data.

Abstract: Systems and methods for extracting a music snippet from a music stream are described. In one aspect, one or more music sentences are extracted from the music stream. The one or more sentences are extracted as a function of peaks and valleys of acoustic energy across sequential music stream portions. The music snippet is selected based on the one or more music sentences.

Abstract: A “music video parser” automatically detects and segments music videos in a combined audio-video media stream. Automatic detection and segmentation is achieved by integrating shot boundary detection, video text detection and audio analysis to automatically detect temporal boundaries of each music video in the media stream. In one embodiment, song identification information, such as, for example, a song name, artist name, album name, etc., is automatically extracted from the media stream using video optical character recognition (OCR). This information is then used in alternate embodiments for cataloging, indexing and selecting particular music videos, and in maintaining statistics such as the times particular music videos were played, and the number of times each music video was played.

Abstract: Systems and methods for extracting a music snippet from a music stream are described. In one aspect, the music stream is divided into multiple frames of fixed length. The most-salient frame of the multiple frames is then identified. One or more music sentences are then extracted from the music stream as a function of peaks and valleys of acoustic energy across sequential music stream portions. The music snippet is the sentence that includes the most-salient frame.

Abstract: Systems and methods for learning-based automatic commercial content detection are described. In one aspect, program data is divided into multiple segments. The segments are analyzed to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content. The context-based features are a function of single-side left and/or right neighborhoods of segments of the multiple segments.