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Instrument modeling for Automatic Music Transcription (Input Magnitude…
Instrument modeling for Automatic Music Transcription
Input Magnitude Spectrogram (weighted combination of basis spectra)
Spectrogram Factorisation
Short Time Fourier Transform STFT
Probabilistic Latent Component Analysis (PLCA)
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Non-Negative Matrix Factorisation (NMF)
Dynamic Bayesian Network
Sparse Decomposition
Deep Learning
Supervised
Convolutional Sequence To Sequence (Cseq2seq) model
Sequential Model/Recurrent Models/seq2seq model
Convolutional Neural Networks (CNNs)
End-To-End Neural Network (Dixon)/Hybrid
Probabilistic Graphical Model
Music Language Model/Recurrent NN
Generative RNN
Neural Autogressive Distribution Estimator: The
NADE
RNN-NADE
Acoustic Model NN
Two-State
Hidden Markov Models (HMMs)
Convolutional Neural Nets (ConvNets)
Spectrogram Factorisation Based Acoustic Models
Deep Neural Networks
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Recurrent Neural Networks
Hybrid RNN
Unsupervised Methods
Multi-Pitch Analysis
Analysis On The Frame
Analyses On The Note Level
Percussive Instrument Transcription
Clustering Harmonic Temporal Structures
Unsupervised NMF
Separating a Sound Sourse
Discriminative Approaches
Hidden Markov Model
Convolutional Neural Network