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Audio classification is an important issue in current audio processing and content analysis researches. In this paper we present a high-accuracy audio classification algorithm based on SVM-UBM using MFCCs as classification features. Firstly MFCCs are extracted in frame level, then a Universal Background Gaussian Mixture Model (UBM) is employed to integrate these sequences of frame-level MFCCs within...
Detection of key sounds, such as applause, laugh, music, environmental noise, etc., is one of the challenges in intelligent management of multimedia information and content understanding. In this paper, we report progress in development of a reference content-based audio classification algorithm that is based on a conventional and widely accepted approach, namely signal parameterization by MFCC followed...
Audio classification is an important issue in current audio processing and content analysis researches. Speech/music classification is one of the most interesting branches of audio signal classification. In this paper we present an unsupervised clustering method, based on one-class support vector machines (OCSVM) and inspired by the classical K-means algorithm, which effectively classifies speech/music...
Automatic generation of music thumbnails based on content analysis is important for efficient management and better consumption of digital music. In this paper, a unique method for extracting thumbnails from a song based on structure analysis of the music piece is presented. First, paragraphs in the song with repeated melody are detected; next, vocal portions in the song are identified. With such...
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