In an attempt to mitigate the need for high quality strong annotations for Sound Event Detection (SED), an approach has been to resort to a mix of weaklylabelled, unlabelled and a small set of representative (isolated) examples. The common approach to integrate the set of representative examples into the training process is to use them for creating synthetic soundscapes. The process of synthesizing soundscapes however could come with its own artefacts and mismatch to real recordings and harm the overall performance. Alternatively, a rather direct way would be to use the isolated examples in a form of template matching. To this end in this paper we propose to train an isolated event classifier using the representative examples. By sliding the classifier across a recording, we use its output as an auxiliary feature vector concatenated with intermediate spectro-temporal representations extracted by the SED system. Experimental results on DESED dataset demonstrate improvements in segmentation performance when using auxiliary features and comparable results to the baseline when using them without synthetic soundscapes. Furthermore we show that this auxiliary feature vector block could act as a gateway to integrate external annotated datasets in order to further boost SED system's performance.