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 made using non-negative matrix factorization as well as deep learning methods. However, these works primarily focus on transcribing three drum instruments only: snare drum, bass drum, and hi-hat. Yet, for many applications, the ability to transcribe more drum instruments which make up standard drum kits used in western popular music would be desirable. In this work, convolutional and convolutional recurrent neural networks are trained to transcribe a wider range of drum instruments. First, the shortcomings of publicly available datasets in this context are discussed. To overcome these limitations, a larger synthetic dataset is introduced. Then, methods to train models using the new dataset focusing on generalization to real world data are investigated. Finally, the trained models are evaluated on publicly available datasets and results are discussed. The contributions of this work comprise: (i.) a large-scale synthetic dataset for drum transcription, (ii.) first steps towards an automatic drum transcription system that supports a larger range of instruments by evaluating and discussing training setups and the impact of datasets in this context, and (iii.) a publicly available set of trained models for drum transcription. Additional materials are available at this http URL
In @cite_12 , a set of MIDI drum loops rendered with different drum samples are used to create synthetic data in the context of ADT. Using synthetic data was a necessity in the early years of music information retrieval (MIR), but due to the continuous efforts of creating datasets, this has declined in recent years. However, machine learning methods like deep learning, often requirer large amounts of data, and manual annotation in large volumes is unfeasible for many MIR tasks. In other fields like speech recognition or image processing, creating annotations is easier, and large amounts of data are commonly available. Using data augmentation can, to a certain degree, be used to overcome lack of data, as has been demonstrated in the context of ADT @cite_11 . @cite_28 an approach to resynthesizes solo tracks using automatically annotated f0 trajectories, to create perfect annotations, is introduced. This approach could be applicable for ADT, once a satisfactory model for the full range of drum instruments is available. At the moment such annotations would be limited to the three drum instrument classes used in state-of-the-art methods.
@cite_28 @cite_12 @cite_11
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This paper presents a drum transcription algorithm adjusted to the constraints of real-time audio. We introduce an instance filtering (IF) method using sub-band onset detection, which improves the performance of a system having at its core a feature-based K-nearest neighbor classifier (KNN). The architecture proposed allows for adapting different parts of the algorithm for either bass drum, snare drum or hi-hat cymbals. The open-source system is implemented in the graphic programming languages Pure Data (PD) and Max MSP, and aims to work with a large variety of drum sets. We evaluated its performance on a database of audio samples generated from a well known collection of midi drum loops randomly matched with a diverse collection of drum sets. Both of the evaluation stages, testing and validation, show an improvement in the performance when using the instance filtering algorithm. Automatic drum transcription methods aim at extracting a symbolic representation of notes played by a drum kit in audio recordings. For automatic music analysis, this task is of particular interest as such a transcript can be used to extract high level information about the piece, e.g., tempo, downbeat positions, meter, and genre cues. In this work, an approach to transcribe drums from polyphonic audio signals based on a recurrent neural network is presented. Deep learning techniques like dropout and data augmentation are applied to improve the generalization capabilities of the system. The method is evaluated using established reference datasets consisting of solo drum tracks as well as drums mixed with accompaniment. The results are compared to state-of-the-art approaches on the same datasets. The evaluation reveals that F-measure values higher than state of the art can be achieved using the proposed method.