Secl-Umons Database For Sound Event Classification And Localization

We introduce the SECL-UMons dataset for sound event classification and localization in the context of office environments. The multichannel dataset is composed of 11 event classes recorded at several realistic positions in two different rooms. The dataset comprises two types of sequences according to the number of events in the sequence. 2662 unilabel sequences and 2724 multilabel sequences are recorded corresponding to a total of 5.24 hours. The database will be publicly released to provide support for algorithm development and common ground for comparison of different techniques. The DCASE 2019 challenge baseline (SELDnet) employing a convolutional recurrent neural network is used to generate benchmark scores for the new dataset. We also slightly modify the model to introduce a benchmark score for real-time classification and localization for the new dataset.