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Environmental sound extraction and incremental learning approach for real time concepts identification

机译:实时概念识别的环境声音提取和增量学习方法

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Audio classification has been becoming very important in the field of multimedia researches dealing with audio processing and pattern recognition. Although major of them are focusing in “how”? Audio classifications should be semantic; the majority of them have neglected the importance of preprocessing step of environmental sound recognition, or using simply very classic sound classifiers. The originality of this paper is to construct a complete three modules process, acting dependently, with well defined functions. New method of acoustic sources separation are offered by our process, as well as, a sophisticated encapsulation of binary classifiers is used to promote environmental sound classification, leading to a real time audio concepts identifier. The main finding is that our system was able to recognize accuracy more than 90% of the introduced audio concepts.
机译:在处理音频处理和模式识别的多媒体研究领域,音频分类一直非常重要。 虽然他们的专业是关注“如何&#x201d ;? 音频分类应该是语义; 其中大多数人忽略了预处理环境声音识别的重要性,或使用简单的经典声学分类。 本文的原创性是构造一个完整的三个模块过程,依赖性起作用,具有明确的功能。 我们的过程提供了新的声学来源分离方法,以及二进制分类器的复杂封装用于促进环境声音分类,导致实时音频概念标识符。 主要发现是我们的系统能够识别超过90%引入的音频概念的准确性。

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