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
  This publication presents a new method for the detection and classification of un-pitched percussive instruments in real world musical signals. The derived information is an important pre-requisite for the creation of a musical score, i.e. automatic transcription, and for the automatic extraction of semantic meaningful meta-data, e.g. tempo and musical meter. The proposed method applies Independent Subspace Analysis using Non-Negative Independent Component Analysis and principles of Prior Subspace Analysis. An important extension of Prior Subspace Analysis is the identification of frequency subspaces of percussive instruments from the signal itself. The frequency subspaces serve as information for the detection of the percussive events and the subsequent classification of the occurring instruments. Results are reported on 40 manually transcribed test items. We discuss in this paper two post-processings for drum transcription systems, which aim to model typical properties of drum sequences. Both methods operate on a symbolic representation of the sequence, which is obtained by quantizing the onsets of drum strokes on an optimal tatum grid, and by fusing the posterior probabilities produced by the drum transcription system. The first proposed method is a generalization of the N -gram model. We discuss several training and recognition strategies (style-dependent models, local models) in order to maximize the reliability and the specificity of the trained models. Alternatively, we introduce a novel unsupervised algorithm based on a complexity criterion, which finds the most regular and wellstructured sequence compatible with the acoustic scores produced by the transcription system. Both approaches are evaluated on a subset of the ENST-drums corpus, and yield performance improvements. Content-based analysis of music can help manage the increasing amounts of music information available digitally and is becoming an important part of multimedia research. The use of drums and percussive sounds is pervasive to popular and world music. In this paper we describe an automatic system for detecting and transcribing low and medium-high frequency drum events from audio signals. Two different subband front-ends are utilized. The first is based on bandpass filters and the second is based on wavelet analysis. Experimental results utilizing music, drum loops and Indian tabla thekas as signals are provided. The proposed system can be used as a preprocessing step for rhythm-based music classification and retrieval. In addition it can be used for pedagogical purposes   This paper proposes a method for transcribing drums from polyphonic music using a network of connected hidden Markov models (HMMs). The task is to detect the temporal locations of unpitched percussive sounds (such as bass drum or hi-hat) and recognise the instruments played. Contrary to many earlier methods, a separate sound event segmentation is not done, but connected HMMs are used to perform the segmentation and recognition jointly. Two ways of using HMMs are studied: modelling combinations of the target drums and a detector-like modelling of each target drum. Acoustic feature parametrisation is done with mel-frequency cepstral coefficients and their first-order temporal derivatives. The effect of lowering the feature dimensionality with principal component analysis and linear discriminant analysis is evaluated. Unsupervised acoustic model parameter adaptation with maximum likelihood linear regression is evaluated for compensating the differences between the training and target signals. The performance of the proposed method is evaluated on a publicly available data set containing signals with and without accompaniment, and compared with two reference methods. The results suggest that the transcription is possible using connected HMMs, and that using detector-like models for each target drum provides a better performance than modelling drum combinations.      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.  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 djembe. Most studies of drum signal transcription focus on sounds taken in isolation. In this paper, we propose several methods for drum loop transcription where the drums signals dataset reflects the variability encountered in modern audio recordings (real and natural drum kits, audio effects, simultaneous instruments, etc.). 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.