  A dictionary learning based audio source classification algorithm is proposed
to classify a sample audio signal as one amongst a finite set of different
audio sources. Cosine similarity measure is used to select the atoms during
dictionary learning. Based on three objective measures proposed, namely, signal
to distortion ratio (SDR), the number of non-zero weights and the sum of
weights, a frame-wise source classification accuracy of 98.2% is obtained for
twelve different sources. Cent percent accuracy has been obtained using moving
SDR accumulated over six successive frames for ten of the audio sources tested,
while the two other sources require accumulation of 10 and 14 frames.
