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In this paper, the single-channel EEG based classification systems using simple extracted features are investigated. Each classification system contains the following stages: data acquisition, signal decomposition, feature extraction, and classification. In addition to using the filter bank and empirical mode decomposition (EMD) methods for signal decomposition, a sparse discrete wavelet packet transform...
Leveraging on the strength of energy-based processing for transient detection and pitch-based processing for softer onsets detection, we present a system that combines both energy and pitch cues for detecting onsets from different instrument categories. Given an audio input from an arbitrary instrument category, the system performs preliminary onset categorization based on the general note characteristics...
The development of effective music retrieval and recommendation applications requires meaningful features for characterizing music audio. The rhythm of music audio in particular, besides timbre and melody, is essential in describing the music piece. This paper illustrates how drum loop patterns characterize the salient rhythm structure of music and an approach for drum loop pattern extraction is described...
Although drum loops are widely present in many audio recordings of modern style music, there is little research that deals with automatic extraction of drum loops in polyphonic music audio. This paper presents an approach for drum loop pattern extraction, based on a technique of fusing the meter estimation information and onset clustering information. The extracted drum loop patterns are formed based...
Recent researches show that the benefits of image segmentation have been exploited in object categorization and recognition approaches. In most of these works, objects are segmented from the background around to increase recognition accuracy. However, it is generally hard to find a segmentation that captures all correct object boundaries in images of real world scene. So some researches begin to choose...
Recent work in visual retrieval shows that bag-of-features (BoF) has appeared promising for object recognition and categorization. Local descriptors such as SIFT have shown impressive results on objects. The main idea of BoF is to depict each image as an orderless collection of local keypoint features. However, not all the local keypoint features are important for retrieving objects and rather, the...
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