On the Relative Importance of Individual Components of Chord Recognition Systems

Most chord recognition systems share a common architecture comprising two main stages: feature extraction and pattern matching, and two optional sub stages: pre-filtering and post-filtering. Understanding the interaction between these basic components is very important not only for achieving optimal performance, but also for assessing the potential and limitations of the system. Unfortunately, there are no studies that sufficiently evaluate the effects of the different approaches to each processing step and the interactions between these steps. In this paper we attempt to remedy this deficiency by performing a systematic evaluation encompassing a wide variety of techniques used for each processing step. In our study we find that filtering has a significant impact on performance, but providing musical context information in the transition matrix is rendered moot by the need to enforce continuity in the estimations. We discovered that the benefits of using complex chord models can be largely offset by an appropriate choice of features. In addition, the initial performance gap between different features were not fully compensated by any subsequent processing stages.