One major approach to music retrieval is to model music as a sequence of features, after which traditional information retrieval techniques are applied on the sequence. Because of the temporal nature of music and the inexactness of user queries, most effort on music retrieval systems focus on issues such as indexing and approximation match. In contrast, the processing of music before feature extraction, such as the identijication of melody track, were often considered easy or done. This may be the case in a controlled environment, such as one for musicology research, where the pieces are carefulljl analyzed by human beings before being submitted to the database. However; in an environment where large volumes of music is obtained from the Web, manual music analysis is impractical. Since many well-known musical features often pertain to the melody of musical pieces, and users often remember the melody of a song, algorithms that select the melody tracks of a piece are important for Web-based content-based retrieval systems. In this paper; we describe a number of algorithms for automatic melody track selection in a music retrieval context. We will also study the pelformance of the algorithms by comparing their answers to those judged by human beings.
Since only simple symbol-based manipulations are needed, n-gram indexing is used for natural languages where syntactic or semantic analyses are often difficult. Music, whose automatic analysis for patterns such as motifs and phrases are difficult, inaccurate or computationally expensive, is thus similar to natural languages. The use of n-gram in music retrieval systems is thus a natural choice.In this paper, we study a number of issues regarding ngram indexing of musical features using simulated queries. They are: whether combinatorial explosion is a problem in n-gram indexing of musical features, the relative discrimination power of six different musical features, the value of n needed for them, and the average amount of false positives returned when n-grams are used to index music.
Weather systems such as tropical cyclones, fronts, troughs and ridges affect our daily lives. Yet, they are often manually located and drawn on weather charts based on forecasters' experience. To identify them, multiple atmospheric elements need to be considered, and the results may vary among forecasters. In this paper, we contribute to the fields of pattern recognition and meteorological computing by designing a generic model of weather systems, along with a genetic algorithm-based framework for finding them from multidimensional numerical weather prediction data. It was found that our method not only can locate weather systems with 80% to 100% precision, but also discover features that could indicate the genesis or dissipation of such systems that could be ignored by forecasters.
Rotational motion can often be seen in video. However, comparatively little research has been done to investigate rotational motions in video, whose analysis could be useful. For example, if we can efficiently identify the rotation center of a spinning object, extraction and tracking of it can be made easier by grouping points moving at the same radial speed. It could also improve compression by synthesizing analyzed spin transitions, and help tracking of rotating objects. In this paper, we introduce a set of rotation center location methods using only the motion field constructed during video encoding, along with a few methods for improving their performances. These methods can be implemented using integer operations only. They are up to 1.81 times faster than the traditional circulation analysis method with little sacrifice in accuracy, and are not affected by asymmetric fields caused by translational motions.
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