Visualising Singing Style under Common Musical Events Using Pitch-Dynamics Trajectories and Modified TRACLUS Clustering

Lin, Kin Wah Edward; Anderson, Hans; Agus, Natalie; So, Clifford; Lui, Simon

doi:10.1109/icmla.2014.44

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…TRACLUS, introduced by Lee, Han, and Whang in 2007 [4], is a trajectory clustering algorithm that identifies common sub-trajectories across sets of trajectories. The algorithm's applications span hurricane tracking, wildlife tracking [4], visualizing singing styles [12], and transportation [13].…”

Section: A Traclusmentioning

confidence: 99%

Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning

Benjamin

Strümke

Lekkas

2022

2022 American Control Conference (ACC)

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Understanding the behavior of deep reinforcement learning (DRL) agents is crucial for improving their performance and reliability. However, the complexity of their policies often makes them challenging to understand. In this paper, we introduce a new approach for investigating the behavior modes of DRL policies, which involves utilizing dimensionality reduction and trajectory clustering in the latent space of neural networks. Specifically, we use Pairwise Controlled Manifold Approximation Projection (PaCMAP) for dimensionality reduction and TRACLUS for trajectory clustering to analyze the latent space of a DRL policy trained on the Mountain Car control task. Our methodology helps identify diverse behavior patterns and suboptimal choices by the policy, thus allowing for targeted improvements. We demonstrate how our approach, combined with domain knowledge, can enhance a policy's performance in specific regions of the state space.

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Section: A Traclusmentioning

confidence: 99%

Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning

Benjamin

Strümke

Lekkas

2022

2022 American Control Conference (ACC)

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“…These systems, once developed adequately, will have applications in bilateral cochlear implants [15], the ability to calculate fundamental frequency [7], beat monitoring (despite dominant voices) [49], and karaoke music production, as well as any other system that relies on lyric, instrument and chord recognition. Other potential applications include melody extraction/annotation [5,34], assessment of singing ability [18], automatic lyrics recognition/matching [23,44], singing visualization [19], and singer identification [20].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Representation Learning and Recurrent Neural Network for Singing Voice Separation

Bhattarai

Pandeya

Jie

et al. 2022

Circuits Syst Signal Process

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Music source separation has traditionally followed the encoder-decoder paradigm (e.g., hourglass, U-Net, DeconvNet, SegNet) to isolate individual music components from mixtures. Such networks, however, result in a loss of location-sensitivity, as low-resolution representation drops the useful harmonic patterns over the temporal dimension. We overcame this problem by performing singing voice separation using a high-resolution representation learning (HRNet) system coupled with a long short-term memory (LSTM) module to retain high-resolution feature map and capture the temporal behavior of the acoustic signal. We called this joint combination of HRNet and LSTM as HR-LSTM. The predicted spectrograms produced by this system are close to ground truth and successfully separate music sources, achieving results superior to those realized by past methods. The proposed network was tested using four datasets (DSD100, MIR-1K, Korean Pansori, and Nepal Idol singing voice). Our experiments confirmed that the proposed HR-LSTM outperforms state-of-the-art networks at singing voice separation when the DSD100 dataset is used, performs comparably to alternative methods when the MIR-1K dataset is used, and separates the voice and accompaniment components well when the Pansori and NISVS datasets are used. In addition to proposing and validating our network, we also developed and shared our Nepal Idol dataset.

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“…Singing voice separation attempts to isolate singing voice (also called vocal line) from a song. In recent years, this problem has attracted increasing attention with the demand for singer identification [1][2][3], automatic lyrics recognition [4,5] and alignment [6], singing pitch estimation [7], singing style visualization [8], and so on. Meanwhile, isolating pure accompaniment from a song also has great applications such as leading instrument detection [9] and drum source separation [10].…”

Section: Introductionmentioning

confidence: 99%

Monaural Singing Voice and Accompaniment Separation Based on Gated Nested U-Net Architecture

Geng

Huang

2020

Symmetry

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This paper proposes a separation model adopting gated nested U-Net (GNU-Net) architecture, which is essentially a deeply supervised symmetric encoder–decoder network that can generate full-resolution feature maps. Through a series of nested skip pathways, it can reduce the semantic gap between the feature maps of encoder and decoder subnetworks. In the GNU-Net architecture, only the backbone not including nested part is applied with gated linear units (GLUs) instead of conventional convolutional networks. The outputs of GNU-Net are further fed into a time-frequency (T-F) mask layer to generate two masks of singing voice and accompaniment. Then, those two estimated masks along with the magnitude and phase spectra of mixture can be transformed into time-domain signals. We explored two types of T-F mask layer, discriminative training network and difference mask layer. The experiment results show the latter to be better. We evaluated our proposed model by comparing with three models, and also with ideal T-F masks. The results demonstrate that our proposed model outperforms compared models, and it’s performance comes near to ideal ratio mask (IRM). More importantly, our proposed model can output separated singing voice and accompaniment simultaneously, while the three compared models can only separate one source with trained model.

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Visualising Singing Style under Common Musical Events Using Pitch-Dynamics Trajectories and Modified TRACLUS Clustering

Cited by 7 publications

References 8 publications

Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning

Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning

High-Resolution Representation Learning and Recurrent Neural Network for Singing Voice Separation

Monaural Singing Voice and Accompaniment Separation Based on Gated Nested U-Net Architecture

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