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In this paper, the basis functions are learned using a supervised process to obtain spectral patterns for different musical instruments.
After a set of overcomplete ICA basis functions are learned from the densely sampled 3D patches from training videos for each action, a test video is classified as the class whose basis functions can reconstruct the sampled 3D patches from the test video with the smallest reconstruction error.
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The supervised NMF methods with learnt instrument basis functions are described below: 1. Monophonic sources In the case of monophonic sources, we propose to use the real-time single-pitch constrained method proposed in [43].
In the self-taught learning framework, these basis functions are considered as the data building blocks or the higher level features.
Only five radial basis functions are required for this control system and their weightings can be established and updated continuously by on-line learning.
The matrix of basis functions is speaker-adaptive, learned in a training phase.
The number of basis functions is set to 100.
Recent studies of motor learning indeed suggest that bimanual movements are learned using nonlinear basis functions.
Two types of behaviour namely reactive and history-dependent are learned and are encoded by radial basis functions.
A radial basis function was used as the kernel function.
Non-orthogonal basis vectors can be learned by SR, and the basis vectors may be allowed to be redundant.
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