In music analysis, one of the most fundamental tasks is note onset detection – detecting the beginning of new note events. It is the basis for more high-level tasks, such as beat tracking or tempo detection. The main outline of all approaches for onset detection is roughly the same: The audio signal is transformed into an Onset Detection Function (ODF), which is zero for most of the time but has pronounced peaks in case of onsets. Applying peak picking algorithms on the ODF, the onset times can be extracted. Currently, Convolutional Neural Networks (CNNs) define the state of the art. In this paper, a first exploration of Echo State Networks (ESNs) to obtain an ODF is presented. ESNs have achieved comparable results to CNNs in several recognition tasks, such as speech and image recognition. Features were extracted using a bank of filters with a logarithmic frequency spacing. The feature vectors were fed into the ESN that computed the ODF. Applying a simple threshold-based peak picking algorithm on the ODF, the onsets were detected. For the hyperparameter optimization, a dataset with pre-defined splits for an 8-fold cross validation was used. With all hyperparameters optimized, we reached an F-Measure of 0.812 using a bidirectional ESN with 8000 neurons.