In the majority of audio classification tasks that involve supervised machine learning, ground truth samples are regularly required as training inputs. Most researchers in this field usually annotate audio content by hand and for their individual requirements. This practice resulted in the absence of solid datasets and consequently research conducted by different researchers on the same topic cannot be effectively pulled together and elaborated on. A collaborative audio annotation platform is proposed for both scientific and application oriented audio-semantic tasks. Innovation points include easy operation and interoperability, on the fly annotation while playing audio content online, efficient collaboration with feature engines and machine learning algorithms, enhanced interaction, and personalization via state of the art Web 2.0 /3.0 services.
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