Analysis of time-frequency representations for musical onset detection with convolutional neural network.

Stasiak, Bartłomiej; Mońko, Jędrzej

doi:10.15439/2016f558

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…CNNs are used for note onset detection in audio recordings in the early work [83] for sound event recognition. The use of a spectrogram as an input to the network instead of the enhanced auto-correlation yields better detection performance.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

et al. 2023

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The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music Signal Processing has received an increasing amount of attention over the last decade. This work reviews the most recent developments in Deep Learning in Music signal processing. Two main applications that are discussed are Music Information Retrieval, which spans a plethora of applications, and Music Generation, which can fit a range of musical styles. After a review of both topics, several emerging directions are identified for future research.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

et al. 2023

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“…The CNN is one of the most popular architectures in many music-related machine learning tasks [16,17,20,25,[44][45][46][47][48][49][50][51][52][53][54][55]. Many of these works adopt an architecture having cascading blocks of 2-dimensional filters and max-pooling, derived from well-known works in image recognition [21,56].…”

Section: Base Architecturementioning

confidence: 99%

One deep music representation to rule them all? A comparative analysis of different representation learning strategies

Kim

Urbano

Liem

et al. 2019

Neural Comput & Applic

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Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach. In this approach, it is possible to reuse the output of a pre-trained neural network as the basis for a new learning task. The underlying hypothesis is that if the initial and new learning tasks show commonalities and are applied to the same type of input data (e.g. music audio), the generated deep representation of the data is also informative for the new task. Since, however, most of the networks used to generate deep representations are trained using a single initial learning source, their representation is unlikely to be informative for all possible future tasks. In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain. We conducted this investigation via an extensive empirical study that involves multiple learning sources, as well as multiple deep learning architectures with varying levels of information sharing between sources, in order to learn music representations. We then validate these representations considering multiple target datasets for evaluation. The results of our experiments yield several insights on how to approach the design of methods for learning widely deployable deep data representations in the music domain.

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Note Onset Detection with a Convolutional Neural Network in Recordings of Bowed String Instruments

Mońko

Stasiak

2017

Communications in Computer and Information Science

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Analysis of time-frequency representations for musical onset detection with convolutional neural network.

Cited by 7 publications

References 18 publications

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

One deep music representation to rule them all? A comparative analysis of different representation learning strategies

Note Onset Detection with a Convolutional Neural Network in Recordings of Bowed String Instruments

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