The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation

With recent breakthroughs in artificial neural networks, deep generative models have become one of the leading techniques for computational creativity. Despite very promising progress on image and short sequence generation, symbolic music generation remains a challenging problem since the structure of compositions are usually complicated. In this study, we attempt to solve the melody generation problem constrained by the given chord progression. This music meta-creation problem can also be incorporated into a plan recognition system with user inputs and predictive structural outputs. In particular, we explore the effect of explicit architectural encoding of musical structure via comparing two sequential generative models: LSTM (a type of RNN) and WaveNet (dilated temporal-CNN). As far as we know, this is the first study of applying WaveNet to symbolic music generation, as well as the first systematic comparison between temporal-CNN and RNN for music generation. We conduct a survey for evaluation in our generations and implemented Variable Markov Oracle in music pattern discovery. Experimental results show that to encode structure more explicitly using a stack of dilated convolution layers improved the performance significantly, and a global encoding of underlying chord progression into the generation procedure gains even more.Comment: 8 pages, 13 figure

Composing Music in Constrained Search Environments

Composing music with computers in constrained search environments adds complexities and problems not present in the traditional problem domain of generative music. The traditional and well researched mechanisms of Markov chains, genetic algorithms and data driven rule based systems do not directly map to a problem domain in which pitch choice and rhythm choice are likely to be highly limited. We therefore explore several possible solutions to generating rhythms in extremely constrained environments with the goal of generating music that adheres to user specified constraints and is aesthetically pleasing

A structured framework for representing time in a generative composition system

The representation of music structures is, from Musicology to Artificial Intelligence, a widely known research focus. It entails several generic Knowledge Representation problems like structured knowledge representation, time representation and causality. In this paper, we focus the problem of representing and reasoning about time in the framework of a structured music representation approach, intended to support the development of a Case-Based generative composition system. The basic idea of this system is to use Music Analysis as foundation for a generative process of composition, providing a structured and constrained way of composing novel pieces, although keeping the essential traits of the composer’s style. We propose a solution that combines a tree-like representation with a pseudo-dating scheme to provide an efficient and expressive means to deal with the problem

Interactive Music Generation with Positional Constraints using Anticipation-RNNs

Recurrent Neural Networks (RNNS) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary length. However, their left-to-right generation procedure only allows a limited control from a potential user which makes them unsuitable for interactive and creative usages such as interactive music generation. This paper introduces a novel architecture called Anticipation-RNN which possesses the assets of the RNN-based generative models while allowing to enforce user-defined positional constraints. We demonstrate its efficiency on the task of generating melodies satisfying positional constraints in the style of the soprano parts of the J.S. Bach chorale harmonizations. Sampling using the Anticipation-RNN is of the same order of complexity than sampling from the traditional RNN model. This fast and interactive generation of musical sequences opens ways to devise real-time systems that could be used for creative purposes.Comment: 9 pages, 7 figure

A CONSTRAINED MATCHING PURSUIT APPROACH TO AUDIO DECLIPPING

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A temporally-constrained convolutive probabilistic model for pitch detection

A method for pitch detection which models the temporal evolution of musical sounds is presented in this paper. The proposed model is based on shift-invariant probabilistic latent component analysis, constrained by a hidden Markov model. The time-frequency representation of a produced musical note can be expressed by the model as a temporal sequence of spectral templates which can also be shifted over log-frequency. Thus, this approach can be effectively used for pitch detection in music signals that contain amplitude and frequency modulations. Experiments were performed using extracted sequences of spectral templates on monophonic music excerpts, where the proposed model outperforms a non-temporally constrained convolutive model for pitch detection. Finally, future directions are given for multipitch extensions of the proposed model