29 papers with code • 1 benchmarks • 7 datasets
Music transcription is the task of converting an acoustic musical signal into some form of music notation.
( Image credit: ISMIR 2015 Tutorial - Automatic Music Transcription )
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We apply deep learning methods, specifically long short-term memory (LSTM) networks, to music transcription modelling and composition.
Attention is a commonly used mechanism in sequence processing, but it is of O(n^2) complexity which prevents its application to long sequences.
We train our network from fixed-length music excerpts with a single-labeled predominant instrument and estimate an arbitrary number of predominant instruments from an audio signal with a variable length.
Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates.
Automatic music transcription (AMT) aims to infer a latent symbolic representation of a piece of music (piano-roll), given a corresponding observed audio recording.
We advance the state of the art in polyphonic piano music transcription by using a deep convolutional and recurrent neural network which is trained to jointly predict onsets and frames.