Tracking music from media using reduced pitch class profile features

In this paper, a system that robustly searches and matches a music input signal to a music collection database using a hash table that is constructed from n-grams of reduced tonal profile. Since the problem that is being studied requires high performance, efficiency and scalability, not only the retrieval accuracy should be high, but also the systems workload on the processing unit and the memory should be in acceptable ranges. With respect to other conventional features, the tonal profile features extracted in this work requires much less space. From the tonal features n-gram blocks are constructed and used in a look up table. Whenever the input signal satisfies some constraints, matching and retrieval are performed The results show that the computation performance and the retrieval accuracy is at promising levels.