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Tempo-Invariant Processing of Rhythm with Convolutional Neural Networks [article]

Anders Elowsson
2018 arXiv   pre-print
This paper describes how a log-frequency representation of rhythm-related activations instead can promote tempo invariance when processed with convolutional neural networks.  ...  Rhythm patterns can be performed with a wide variation of tempi.  ...  Convolutional neural network model Building blocks Outlined in this Section are basic building blocks for tempo-invariant processing using magnitude spectra.  ... 
arXiv:1804.08167v2 fatcat:22zbyym6aveoxf77uczarsdnu4

Downbeat tracking with tempo invariant convolutional neural networks

Bruno Di Giorgi, Matthias Mauch, Mark Levy
2020 Zenodo  
We propose a deterministic time-warping operation that enables this skill in a convolutional neural network (CNN) by allowing the network to learn rhythmic patterns independently of tempo.  ...  Unlike conventional deep learning approaches, which learn rhythmic patterns at the tempi present in the training dataset, the patterns learned in our model are tempo-invariant, leading to better tempo  ...  Attribution: Bruno Di Giorgi, Matthias Mauch, Mark Levy, "Downbeat Tracking with Tempo-Invariant Convolutional Neural Networks", in Proc. of the 21st Int.  ... 
doi:10.5281/zenodo.4245408 fatcat:s6itqkqgerac7lmh7gzyqo2h3a

Experimenting with musically motivated convolutional neural networks

Jordi Pons, Thomas Lidy, Xavier Serra
2016 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)  
A common criticism of deep learning relates to the difficulty in understanding the underlying relationships that the neural networks are learning, thus behaving like a blackbox.  ...  The classes in this dataset have a strong correlation with tempo, what allows assessing if the proposed architectures are learning frequency and/or time dependencies.  ...  This work is partly supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).  ... 
doi:10.1109/cbmi.2016.7500246 dblp:conf/cbmi/PonsLS16 fatcat:yfnqfa6lpnefnp2fr7ad7ektkm

Beat Tracking With A Cepstroid Invariant Neural Network

Anders Elowsson
2016 Zenodo  
SUMMARY & CONCLUSIONS We have presented a novel beat tracking and tempo estimation system that uses a cepstroid invariant neural network.  ...  "Beat Tracking with a Cepstroid Invariant Neural Network", 17th International Society for Music Information Retrieval Conference, 2016.  ... 
doi:10.5281/zenodo.1416054 fatcat:2wvpkk3evnf5jdumbtb5upzwga

Designing efficient architectures for modeling temporal features with convolutional neural networks

Jordi Pons, Xavier Serra
2017 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
Many researchers use convolutional neural networks with small rectangular filters for music (spectrograms) classification.  ...  considered during the design process.  ...  INTRODUCTION Due to the convolutional neural networks (CNNs) success in image classification, its literature significantly influenced the music informatics research (MIR) community that adopted standard  ... 
doi:10.1109/icassp.2017.7952601 dblp:conf/icassp/PonsS17 fatcat:mhznxaqxwzhsnlod7uxowkmxee

Special Issue on Deep Learning for Applications in Acoustics: Modeling, Synthesis, and Listening

Leonardo Gabrielli, György Fazekas, Juhan Nam
2021 Applied Sciences  
The recent introduction of Deep Learning has led to a vast array of breakthroughs in many fields of science and engineering [...]  ...  Conflicts of Interest: The authors declare no conflict of interest.  ...  The relative spacing and scaling of part relations are modelled by Gaussians, leading to a tempo-invariant rhythmic pattern representation.  ... 
doi:10.3390/app11020473 fatcat:obsm7zqddrbrhfjmlkaxdcjjsi

Convolutional Neural Networks For Real-Time Beat Tracking: A Dancing Robot Application

Aggelos Gkiokas, Vassilios Katsouros
2017 Zenodo  
In [17] , under the assumption that humans perceive the rhythm in a relative manner with respect to the salient periodicities of a music excerpt, a cepstroid invariant neural network is proposed to estimate  ...  These features along with the Beat Activation Function derived from the ground truth data are used to train a Convolutional Neural Network (CNN).  ... 
doi:10.5281/zenodo.1417736 fatcat:f2cqtllbgzhhxnkfzcw5lp6v2m

Music Genre Classification Using Masked Conditional Neural Networks [chapter]

Fady Medhat, David Chesmore, John Robinson
2017 Lecture Notes in Computer Science  
MCLNN has achieved accuracies that are competitive to state-of-the-art handcrafted attempts in addition to models based on Convolutional Neural Networks.  ...  The ConditionaL Neural Networks (CLNN) and the Masked ConditionaL Neural Networks (MCLNN) exploit the nature of multi-dimensional temporal signals.  ...  Fig. 1 . 1 Conditional Fig. 2 . 2 Convolutional Neural Network Fig. 3 . 3 Two CLNN layers with n = 1.  ... 
doi:10.1007/978-3-319-70096-0_49 fatcat:oqvalo65cjfpfmpug2zjsedcvi

Deconstruct, analyse, reconstruct: How to improve tempo, beat, and downbeat estimation

Sebastian Böck, Matthew Davies
2020 Zenodo  
In this paper, we undertake a critical assessment of a state-of-the-art deep neural network approach for computational rhythm analysis.  ...  To this end, we devise a novel multi-task approach for the simultaneous estimation of tempo, beat, and downbeat.  ...  The core component, which is common to both, is a deep neural network (DNN) architecture based on dilated convolutions, most well-known from WaveNet [23] .  ... 
doi:10.5281/zenodo.4245497 fatcat:562a4tnkmzgdvfsplrqngasvv4

Artificial Neural Network for Folk Music Style Classification

Qinliang Ning, Junyan Shi, Hasan Ali Khattak
2022 Mobile Information Systems  
In this paper, we use artificial neural networks to classify folk music styles and transform audio signals into a sound spectrum to avoid the problem of manually selecting features.  ...  In this paper, we use artificial neural networks to classify folk music styles and transform audio signals into a sound spectrum.  ...  Acknowledgments is project was supported by the Key Scientific Research Project of the Hunan Provincial Department of Education, Project no. 19a104.  ... 
doi:10.1155/2022/9203420 fatcat:arxti5o43vf33he4h4pllcw6nm

Dance Dance Convolution [article]

Chris Donahue, Zachary C. Lipton, Julian McAuley
2017 arXiv   pre-print
For the step placement task, we combine recurrent and convolutional neural networks to ingest spectrograms of low-level audio features to predict steps, conditioned on chart difficulty.  ...  Dance Dance Revolution (DDR) is a popular rhythm-based video game. Players perform steps on a dance platform in synchronization with music as directed by on-screen step charts.  ...  Following state-of-the-art work on onset detection (Schlüter & Böck, 2014) , we adopt a convolutional neural network (CNN) architecture.  ... 
arXiv:1703.06891v3 fatcat:wznptvo5avdlljttqsybbgirty

DLR : Toward a deep learned rhythmic representation for music content analysis [article]

Yeonwoo Jeong, Keunwoo Choi, Hosan Jeong
2017 arXiv   pre-print
In the use of deep neural networks, it is crucial to provide appropriate input representations for the network to learn from.  ...  A 1-dimensional convolutional network is utilised in the learning of DLR. In the experiment, we present the results from the source task and the target task as well as visualisations of DLRs.  ...  In the experiment, DLR was first trained for a rhythm-and tempo-related task and transferred as an input representation to a convolutional neural network for music tagging.  ... 
arXiv:1712.05119v1 fatcat:omazzlfw5nedtb3tqjdsw3zsom

Automatic Classification of Music Genre Using Masked Conditional Neural Networks

Fady Medhat, David Chesmore, John Robinson
2017 2017 IEEE International Conference on Data Mining (ICDM)  
MCLNN have achieved competitive performance on the Ballroom music dataset compared to several hand-crafted attempts and outperformed models based on state-of-the-art Convolutional Neural Networks.  ...  The ConditionaL Neural Networks (CLNN) and its extension the Masked ConditionaL Neural Networks (MCLNN) are designed for multidimensional temporal signal recognition.  ...  Fully connected Feed-forward Neural Networks do not scale well with inputs of large dimensions such as images. Convolutional Neural Networks (CNN) [3] use weight sharing to tackle this problem.  ... 
doi:10.1109/icdm.2017.125 dblp:conf/icdm/MedhatCR17 fatcat:uyy7b5rp5zd4vpr66t7ehtsiae

BeatNet: CRNN and Particle Filtering for Online Joint Beat Downbeat and Meter Tracking [article]

Mojtaba Heydari, Frank Cwitkowitz, Zhiyao Duan
2021 arXiv   pre-print
In this work, we introduce an online system for joint beat, downbeat, and meter tracking, which utilizes causal convolutional and recurrent layers, followed by a pair of sequential Monte Carlo particle  ...  Musical rhythm comprises complex hierarchical relationships across time, rendering its analysis intrinsically challenging and at times subjective.  ...  [18] proposed tempo-invariant convolutional filters for downbeat tracking.  ... 
arXiv:2108.03576v1 fatcat:jhijnjnm2fcntjqsrdx73eioee

BeatNet: CRNN and Particle Filtering for Online Joint Beat, Downbeat and Meter Tracking

Mojtaba Heydari, Frank Cwitkowitz, Zhiyao Duan
2021 Zenodo  
In this work, we introduce an online system for joint beat, downbeat, and meter tracking, which utilizes causal convolutional and recurrent layers, followed by a pair of sequential Monte Carlo particle  ...  Musical rhythm comprises complex hierarchical relationships across time, rendering its analysis intrinsically challenging and at times subjective.  ...  [18] proposed tempo-invariant convolutional filters for downbeat tracking.  ... 
doi:10.5281/zenodo.5624577 fatcat:5oo5lflvezesbgf2vf5abtwsre
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