Polyphonic music modeling with random fields

Abstract

Recent interest in the area of music information retrieval and related technologies is exploding. However, very few of the existing techniques take advantage of recent developments in statistical modeling. In this paper we discuss an application of Random Fields to the problem of creating accurate yet flexible statistical models of polyphonic music. With such models in hand, the challenges of developing effective searching, browsing and organization techniques for the growing bodies of music collections may be successfully met. We offer an evaluation of these models in terms of perplexity and prediction accuracy, and show that random fields not only outperform Markov chains, but are much more robust in terms of overfitting. 1

BassNet: A Variational Gated Autoencoder for Conditional Generation of Bass Guitar Tracks with Learned Interactive Control

“…A common approach for music creation using AI is to train a generative probabilistic model of the data [3][4][5] and create music by iterative sampling, possibly while using constraints [6,7]. More recently, deep learning applications in music creation have focused on generating waveforms directly [8], often using techniques that have been adopted from speech synthesis.…”