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@conference{Mesaros2015, author = "Annamaria Mesaros and Toni Heittola and Onur Dikmen and Tuomas Virtanen", abstract = "Methods for detection of overlapping sound events in audio involve matrix factorization approaches, often assigning separated components to event classes. We present a method that bypasses the supervised construction of class models. The method learns the components as a non-negative dictionary in a coupled matrix factorization problem, where the spectral representation and the class activity annotation of the audio signal share the activation matrix. In testing, the dictionaries are used to estimate directly the class activations. For dealing with large amount of training data, two methods are proposed for reducing the size of the dictionary. The methods were tested on a database of real life recordings, and outperformed previous approaches by over 10%.", booktitle = "Proceedings of 40th IEEE International Conference on Audio, Speech and Signal Processing (ICASSP)", keywords = "coupled non-negative matrix factoriza- tion;non-negative dictionaries;sound event detection", pages = "151-155", publisher = "IEEE", title = "{S}ound event detection in real life recordings using coupled matrix factorization of spectral representations and class activity annotations", url = "https://www.cs.tut.fi/~mesaros/pubs/mesaros_icassp2015.pdf", year = "2015", }