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In this paper, a new feature set is presented and evaluated based on sinusoidal modeling of audio signals. Duration of the longest sinusoidal model frequency track, as a measure of the harmony, is used and compared to typical features as input into an audio classifier. The performance of this sinusoidal model feature is evaluated through classification of audio to speech and music using both the GMM...
This paper deals with the Music/Speech classification problem, starting from a set of features extracted directly from compressed audio data. The proposed classification system is able to label audio sequences stored as compressed MPEG layer III files. Decoding and analyzing in a unique stage is a fundamental tool for audio streaming applications, such as real time classification. Moreover, the techniques...
In this paper, we propose a new system for classifying audio segments as speech or music. The proposed system improves classification accuracy, particularly in low signal-to-noise ratio (SNR) environments. The system selects the features with the highest classification accuracy that corresponds to the SNR value. The value of this features are compared to certain thresholds, which are also adapted...
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