Automatic transcription of drum loops

Recent efforts in audio indexing and retrieval in music databases mostly focus on melody. If this is appropriate for polyphonic music signals, specific approaches are needed for systems dealing with percussive audio signals such as those produced by drums, tabla or djembé. Most studies of drum signals transcription focus on sounds taken in isolation. In this paper, we propose several methods for drum loops transcription where the drums signals dataset reflects the variability encountered in modem audio recordings (real and natural drum kits, audio effects, simultaneous instruments,... ). The approaches described are based on Hidden Markov Models (HMM) and Support Vector Machines (SVM). Promising results are obtained with a 83.9% correct recognition rate for a simplified taxonomy.