INTELLIGENT MUSICAL INSTRUMENT SOUND CLASSIFICATION

Abstract

This chapter is devoted to intelligent classification of the sound of musical instruments. Although it is possible, and in some applications sufficient to process musical data based on statistical methods, clearly such an approach does not provide either computational or cognitive insight. The principal constituents of intelligent computation techniques are data mining, machine learning, knowledge discovery algorithms, decision-systems, learning algorithms, soft computing techniques, artificial intelligence – some of these notions have become independent areas, and some of them are nearly synonymous. Data mining, also referred to as Knowledge Discovery in Databases – KDD, has been defined by Frawley et al. as “the nontrivial extraction of implicit, previously unknown, and potentially useful information from data”. Soft computing aims at using machine learning to discover and to present knowledge in a form, which is easily comprehensible to humans. Physical systems described by multiple variables and parameter models having non-linear coupling, frequently occur in the fields of physics, engineering, technical applications, economy, etc. The conventional approaches for understanding and predicting the behavior of such systems based on analytical techniques can prove to be very difficult, even at initial stages of establishing an appropriate mathematical model. The computational environment used in such an analytical approach is perhaps too categorical and inflexible to cope with the complexity of physical systems of the real world. It turns out that when dealing with such systems, one has to face a high degree of uncertainty and to tolerate imprecision. Trying to increase precision can be very costly.