The song-level feature summarization is an essential building block for browsing, retrieval, and indexing of digital music. This paper proposes a local pooling method to aggregate the feature vectors of a song over the universal background model. Two types of local activation patterns of feature vectors are derived; one representation is derived in the form of histogram, and the other is given by a binary vector. Experiments over three publicly-available music datasets show that the proposed local aggregation of the auditory features is promising for music-similarity computation.