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Sound Board: Food for Thought, Aesthetics in Orchestra Recording - April 2015
Reflecting on Reflections - June 2014
AES Convention Papers Forum
Collaborative Annotation Platform for Audio Semantics
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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