Impact Sounds Classification for Interactive Applications via Discriminative Dictionary Learning

Classification of impulsive events produced from the acoustic stimulation of everyday objects opens the door to exciting interactive applications, as for example, gestural control of sound synthesis. Such events may exhibit significant variability, which makes their recognition a very challenging task. Furthermore, the fact that interactive systems require an immediate response to achieve low latency in real-time scenarios, poses major constraints to be overcome. This paper focuses on the design of a novel method for identifying the sound-producing objects, as well as the location of impact of each event, under a low-latency assumption. To this end, a sparse representation coding framework is adopted based on learned discriminative dictionaries from short training and testing data. The performance of the proposed method is evaluated on a set of real impact sounds and compared against a nearest neighbour classifier. The experimental results demonstrate the high performance improvements of our proposed method, both in terms of classification accuracy and low latency.