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Audio encoding rate selection is mostly based on the extent of channel congestion in these days. This paper presents an analysis of complexity of audio based Mean Opinion Score (MOS). The different styles of music exhibit various requirement of encoding rates in the view of MOS. A preliminary encoding rate selection system is set up. Experiment results indicate that our system can reduce encoding...
In our previous work, a speech/music classifier is proposed on the basis of the feature subset selection (FSS) tool and oblique decision tree induced by the algorithm OC1. In this paper, we endeavor to improve it by state transfer (ST) strategy whose aim is to refine the classification results, according to the fact that adjacent segments in one audio file have strong relevance to each other. The...
Social tags are becoming more and more popular in Web2.0 recently. Tags defined by users are of high-level semantic for music. In this paper, we present a similarity calculation and genre classification measure for music artists with the use-defined tags from Last.fm. Similarities between artists are calculated based on tag co-occurrence. The k-nearest neighbor algorithm (k-NN) has been used to classify...
Although technologies of both low-level and high-level descriptors for music information retrieval (MIR) are advancing, there are some essential deficiencies while utilizing them separately. In this paper we propose a model where the low-level and high-level descriptors collaborate to support semantics-based MIR. The ontology of ldquomusic scenerdquo domain is constructed as a demonstration, and a...
In the problem of classification of audio signals, the requirements of low-complexity, high-accuracy and short delay are crucial for some practical scenarios. This paper proposes a method of real-time speech/music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected together with a proposed simple harmonic structure stability feature,...
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