Recognising Guitar Effects - Which Acoustic Features Really Matter?

Abstract

The recognition of audio effects employed in recordings of electric guitar or bass has a wide range of applications in music information retrieval. It is meaningful in holistic automatic music transcription and annotation approaches for, e. g., music education, intelligent music search, or musicology. In this contribution, we investigate the relevance of a large variety of state-of-the-art acoustic features for the task of automatic guitar effect recognition. The usage of functionals, i. e., statistics such as moments and percentiles, is hereby compared to the bag-of-audio-words approach to obtain an acoustic representation of a recording on instance level. Our results are based on a database of more than 50 000 monophonic and polyphonic samples of electric guitars and bass guitars, processed with 10 different digital audio effects.