TI - Generative Modeling of Metadata for Machine Learning Based Audio Content Classification The resulting classifier is then able to classify real-world content (e.g., YouTube) with an accuracy ˜ 90% with very low latency (viz., ˜ on an average 7 ms) based on real-world sunil g.}, Subsequently, synthetic metadata are generated from these statistical models, and the synthetic metadata is input to the ML classifier as feature vectors. Towards this end, statistical models of the various metadata are created since a large metadata dataset is not available. In this paper we present a neural network to classify between the movie (cinematic, TV shows), music, and voice using metadata contained in either the audio/video stream. Present research for audio-based classifiers look at short- and long-term analysis of signals, using both temporal and spectral features. doi:Ībstract: Automatic content classification technique is an essential tool in multimedia applications. Bharitkar, "Generative Modeling of Metadata for Machine Learning Based Audio Content Classification," Engineering Brief 564, (2019 October.). Music Production for Emerging Audio Formats.Audio for Virtual and Augmented Reality.Archiving, Restoration and Digital Libraries.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |