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
There has been a considerable amount of work published on ADT in recent years, e.g., @cite_17 @cite_12 @cite_20 @cite_39 @cite_16 . In the past, different combinations of signal processing and information retrieval techniques haven been applied to ADT. For example: onset detection in combination with bandpass filtering @cite_8 @cite_27 , and instrument classification @cite_17 @cite_35 @cite_20 ; as well as probabilistic models @cite_39 @cite_24 . Another group of methods focus on extracting an onset-pseudo-probability function (activation function) for each instrument under observation. These methods utilize source separation techniques like Independent Subspace Analysis (ISA) @cite_7 , Prior Subspace Analysis (PSA) @cite_37 , and Non-Negative Independent Component Analysis (NNICA) @cite_38 . More recently, these approaches have been further developed using Non-Negative Matrix Factorization (NMF) variants as well as deep learning @cite_19 @cite_29 @cite_23 @cite_34 .
@cite_35 @cite_37 @cite_38 @cite_7 @cite_8 @cite_29 @cite_34 @cite_39 @cite_24 @cite_19 @cite_27 @cite_23 @cite_16 @cite_12 @cite_20 @cite_17
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In this work, we introduce a new discriminative training method for nonnegative dictionary learning. The new method can be used in single channel source separation (SCSS) applications. In SCSS, nonnegative matrix factorization (NMF) is used to learn a dictionary (a set of basis vectors) for each source in the magnitude spectrum domain. The trained dictionaries are then used in decomposing the mixed signal to find the estimate for each source. Learning discriminative dictionaries for the source signals can improve the separation performance. To achieve discriminative dictionaries, we try to avoid the bases set of one source dictionary from representing the other source signals. We propose to minimize cross-coherence between the dictionaries of all sources in the mixed signal. We incorporate a simplified cross-coherence penalty using a regularized NMF cost function to simultaneously learn discriminative and reconstructive dictionaries. The new regularized NMF update rules that are used to discriminatively train the dictionaries are introduced in this work. Experimental results show that using discriminative training gives better separation results than using conventional NMF. Copyright © 2013 ISCA. We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned attentional map. This substantially suppresses background interference in the convolutional features, which is the key to producing accurate inference of words, particularly at extremely small sizes. This results in a single model that essentially works in a coarse-to-fine manner. It departs from recent FCN-based text detectors which cascade multiple FCN models to achieve an accurate prediction. Furthermore, we develop a hierarchical inception module which efficiently aggregates multi-scale inception features. This enhances local details, and also encodes strong context information, allowing the detector to work reliably on multi-scale and multi-orientation text with single-scale images. Our text detector achieves an F-measure of 77 on the ICDAR 2015 benchmark, advancing the state-of-the-art results in [18, 28]. Demo is available at: http: sstd.whuang.org . We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined convolutive mixture of source signals. We work in the short-time Fourier transform (STFT) domain, where convolution is routinely approximated as linear instantaneous mixing in each frequency band. Each source STFT is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura-Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization (EM) algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms are applied to stereo audio source separation in various settings, covering blind and supervised separation, music and speech sources, synthetic instantaneous and convolutive mixtures, as well as professionally produced music recordings. Our EM method produces competitive results with respect to state-of-the-art as illustrated on two tasks from the international Signal Separation Evaluation Campaign (SiSEC 2008). In this paper, we evaluate various onset detection algorithms in terms of their online capabilities. Most methods use some kind of normalization over time, which renders them unusable for online tasks. We modified existing methods to enable online application and evaluated their performance on a large dataset consisting of 27,774 annotated onsets. We focus particularly on the incorporated preprocessing and peak detection methods. We show that, with the right choice of parameters, the maximum achievable performance is in the same range as that of offline algorithms, and that preprocessing can improve the results considerably. Furthermore, we propose a new onset detection method based on the common spectral flux and a new peak-picking method which outperforms traditional methods both online and offline and works with audio signals of various volume levels.