A Review of Automatic Drum Transcription
In Western popular music, drums and percussion are an important means to emphasize and shape the rhythm, often defining the musical style. If computers were able to analyze the drum part in recorded music, it would enable a variety of rhythm-related music processing tasks. Especially the detection and classification of drum sound events by computational methods is considered to be an important and challenging research problem in the broader field of music information retrieval. Over the last two decades, several authors have attempted to tackle this problem under the umbrella term automatic drum transcription (ADT). This paper presents a comprehensive review of ADT research, including a thorough discussion of the task-specific challenges, categorization of existing techniques, and evaluation of several state-of-the-art systems. To provide more insights on the practice of ADT systems, we focus on two families of ADT techniques, namely methods based on non-negative matrix factorization and recurrent neural networks. We explain the methods’ technical details and drum-specific variations and evaluate these approaches on publicly available data sets with a consistent experimental setup. Finally, the open issues and underexplored areas in ADT research are identified and discussed, providing future directions in this field
Code (0)
등록된 구현이 없습니다.
Tasks
Drum TranscriptionDrum Transcription in Music (DTM)Information RetrievalMusic Information RetrievalRhythmSimilar Papers 제목 키워드 기반
Towards multi-instrument drum transcription
Automatic drum transcription, a subtask of the more general automatic music transcription, deals with extracting drum instrument note onsets from an audio source. Recently, progress in transcription performance has been …
Drum TranscriptionMusic TranscriptionThe Inverse Drum Machine: Source Separation Through Joint Transcription and Analysis-by-Synthesis
We introduce the Inverse Drum Machine (IDM), a novel approach to drum source separation that combines analysis-by-synthesis with deep learning. Unlike recent supervised methods that rely on isolated stems, IDM requires o…
Drum TranscriptionAIoT-Based Drum Transcription Robot using Convolutional Neural Networks
With the development of information technology, robot technology has made great progress in various fields. These new technologies enable robots to be used in industry, agriculture, education and other aspects. In this p…
Drum TranscriptionMusic TranscriptionGlobal Structure-Aware Drum Transcription Based on Self-Attention Mechanisms
This paper describes an automatic drum transcription (ADT) method that directly estimates a tatum-level drum score from a music signal, in contrast to most conventional ADT methods that estimate the frame-level onset pro…
DecoderDrum TranscriptionNoise-to-Notes: Diffusion-based Generation and Refinement for Automatic Drum Transcription
Automatic drum transcription (ADT) is traditionally formulated as a discriminative task to predict drum events from audio spectrograms. In this work, we redefine ADT as a conditional generative task and introduce Noise-t…