665 research outputs found
High-dimensional sequence transduction
We investigate the problem of transforming an input sequence into a
high-dimensional output sequence in order to transcribe polyphonic audio music
into symbolic notation. We introduce a probabilistic model based on a recurrent
neural network that is able to learn realistic output distributions given the
input and we devise an efficient algorithm to search for the global mode of
that distribution. The resulting method produces musically plausible
transcriptions even under high levels of noise and drastically outperforms
previous state-of-the-art approaches on five datasets of synthesized sounds and
real recordings, approximately halving the test error rate
VGM-RNN: Recurrent Neural Networks for Video Game Music Generation
The recent explosion of interest in deep neural networks has affected and in some cases reinvigorated work in fields as diverse as natural language processing, image recognition, speech recognition and many more. For sequence learning tasks, recurrent neural networks and in particular LSTM-based networks have shown promising results. Recently there has been interest – for example in the research by Google’s Magenta team – in applying so-called “language modeling” recurrent neural networks to musical tasks, including for the automatic generation of original music. In this work we demonstrate our own LSTM-based music language modeling recurrent network. We show that it is able to learn musical features from a MIDI dataset and generate output that is musically interesting while demonstrating features of melody, harmony and rhythm. We source our dataset from VGMusic.com, a collection of user-submitted MIDI transcriptions of video game songs, and attempt to generate output which emulates this kind of music
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