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One hour before sunrise, one can experience the dawn chorus where birds fromdifferent species sing together. In this scenario, high levels of polyphony, asin the number of overlapping sound sources, are prone to happen resulting in acomplex acoustic outcome. Sound Event Detection (SED) tasks analyze acousticscenarios in order to identify the occurring events and their respectivetemporal information. However, highly dense scenarios can be hard to processand have not been studied in depth. Here we show, using a ConvolutionalRecurrent Neural Network (CRNN), how birdsong polyphonic scenarios can bedetected when dealing with higher polyphony and how effectively this type ofmodel can face a very dense scene with up to 10 overlapping birds. We foundthat models trained with denser examples (i.e., higher polyphony) learn at asimilar rate as models that used simpler samples in their training set.Additionally, the model trained with the densest samples maintained aconsistent score for all polyphonies, while the model trained with the leastdense samples degraded as the polyphony increased. Our results demonstrate thathighly dense acoustic scenarios can be dealt with using CRNNs. We expect thatthis study serves as a starting point for working on highly populated birdscenarios such as dawn chorus or other dense acoustic problems.

Polyphonic sound event detection for highly dense birdsong scenes