A Computational Model for Multi - Instrument Music Transcription CS 229 Final Project Report , Autumn 2013

CS 224D Final Project DeepRock

We create a canonical encoding for multi-instrument MIDI songs into natural language, then use deep NLP techniques such as character LSTM variants to compose rock music that surpasses the prior state of the art and is competitive with certain pieces of music composed by human rock bands. We further define a neural network architecture for learning multi-instrument music generation in concert, b...

Explicit Duration Hidden Markov Models for Multiple-Instrument Polyphonic Music Transcription

In this paper, a method for multiple-instrument automatic music transcription is proposed that models the temporal evolution and duration of tones. The proposed model supports the use of spectral templates per pitch and instrument which correspond to sound states such as attack, sustain, and decay. Pitch-wise explicit duration hidden Markov models (EDHMMs) are integrated into a convolutive prob...

CS 229 = = Final Project Report SPEECH & NOISE SEPARATION

In this course project I investigated machine learning approaches on separating speech signals from background noise. Keywords—MFCC, SVM, noise separation, source separation, spectrogram

An Efficient Temporally-Constrained Probabilistic Model for Multiple-Instrument Music Transcription

In this paper, an efficient, general-purpose model for multiple instrument polyphonic music transcription is proposed. The model is based on probabilistic latent component analysis and supports the use of sound state spectral templates, which represent the temporal evolution of each note (e.g. attack, sustain, decay). As input, a variable-Q transform (VQT) time-frequency representation is used....

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