Controlling Surprisal in Music Generation via Information Content Curve Matching

• URL: http://arxiv.org/abs/2408.06022v1
• Date: Mon, 12 Aug 2024 09:21:41 GMT
• Title: Controlling Surprisal in Music Generation via Information Content Curve Matching
• Authors: Mathias Rose Bjare, Stefan Lattner, Gerhard Widmer,
• Abstract summary: We propose a novel method for controlling surprisal in music generation using sequence models. We define a metric called Instantaneous Information Content (IIC) The IIC serves as a proxy function for the perceived musical surprisal.
• Score: 3.5570874721859016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, the quality and public interest in music generation systems have grown, encouraging research into various ways to control these systems. We propose a novel method for controlling surprisal in music generation using sequence models. To achieve this goal, we define a metric called Instantaneous Information Content (IIC). The IIC serves as a proxy function for the perceived musical surprisal (as estimated from a probabilistic model) and can be calculated at any point within a music piece. This enables the comparison of surprisal across different musical content even if the musical events occur in irregular time intervals. We use beam search to generate musical material whose IIC curve closely approximates a given target IIC. We experimentally show that the IIC correlates with harmonic and rhythmic complexity and note density. The correlation decreases with the length of the musical context used for estimating the IIC. Finally, we conduct a qualitative user study to test if human listeners can identify the IIC curves that have been used as targets when generating the respective musical material. We provide code for creating IIC interpolations and IIC visualizations on https://github.com/muthissar/iic.

Related papers

• Estimating Musical Surprisal in Audio [4.056099795258358]
  Information content (IC) of one-step predictions from an autoregressive model as a proxy for surprisal in symbolic music. We train an autoregressive Transformer model to predict compressed latent audio representations of a pretrained autoencoder network. We investigate the IC's relation to audio and musical features and find it correlated with timbral variations and loudness and, to a lesser extent, dissonance, rhythmic complexity, and onset density related to audio and musical features.
  arXiv  Detail & Related papers  (2025-01-13T16:46:45Z)
• Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures [3.463789345862036]
  We introduce a new method based on Joint-Embedding Predictive Architectures, where an encoder and a predictor are jointly trained to produce latent representations of a context. In particular, we design our predictor to be conditioned on arbitrary instruments, enabling our model to perform zero-shot stem retrieval. We validate the retrieval performances of our model using the MUSDB18 and MoisesDB datasets.
  arXiv  Detail & Related papers  (2024-11-29T16:11:47Z)
• Generating Sample-Based Musical Instruments Using Neural Audio Codec Language Models [2.3749120526936465]
  We propose and investigate the use of neural audio language models for the automatic generation of sample-based musical instruments. Our approach extends a generative audio framework to condition on pitch across an 88-key spectrum, velocity, and a combined text/audio embedding.
  arXiv  Detail & Related papers  (2024-07-22T13:59:58Z)
• MuPT: A Generative Symbolic Music Pretrained Transformer [56.09299510129221]
  We explore the application of Large Language Models (LLMs) to the pre-training of music. To address the challenges associated with misaligned measures from different tracks during generation, we propose a Synchronized Multi-Track ABC Notation (SMT-ABC Notation) Our contributions include a series of models capable of handling up to 8192 tokens, covering 90% of the symbolic music data in our training set.
  arXiv  Detail & Related papers  (2024-04-09T15:35:52Z)
• Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task [86.72661027591394]
  We generate complete and semantically consistent symbolic music scores from text descriptions. We explore the efficacy of using publicly available checkpoints for natural language processing in the task of text-to-music generation. Our experimental results show that the improvement from using pre-trained checkpoints is statistically significant in terms of BLEU score and edit distance similarity.
  arXiv  Detail & Related papers  (2022-11-21T07:19:17Z)
• EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters [72.19032452642728]
  We propose a novel differentiable EEG decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. We demonstrate the utility of our model towards emotion recognition from EEG signals on the SEED dataset and on a new EEG dataset of unprecedented size. The discovered features align with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening.
  arXiv  Detail & Related papers  (2021-10-19T14:22:04Z)
• Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music [69.2737664640826]
  This paper evaluates different types of memory mechanisms (memory cells) and analyses their performance in the field of music composition. A set of quantitative metrics is presented to evaluate the performance of the proposed architecture automatically.
  arXiv  Detail & Related papers  (2020-12-02T14:19:19Z)
• Score-informed Networks for Music Performance Assessment [64.12728872707446]
  Deep neural network-based methods incorporating score information into MPA models have not yet been investigated. We introduce three different models capable of score-informed performance assessment.
  arXiv  Detail & Related papers  (2020-08-01T07:46:24Z)
• Music Generation with Temporal Structure Augmentation [0.0]
  The proposed method augments a connectionist generation model with count-down to song conclusion and meter markers as extra input features. An RNN architecture with LSTM cells is trained on the Nottingham folk music dataset in a supervised sequence learning setup. Experiments show an improved prediction performance for both types of annotation.
  arXiv  Detail & Related papers  (2020-04-21T19:19:58Z)
• Multi-Modal Music Information Retrieval: Augmenting Audio-Analysis with Visual Computing for Improved Music Video Analysis [91.3755431537592]
  This thesis combines audio-analysis with computer vision to approach Music Information Retrieval (MIR) tasks from a multi-modal perspective. The main hypothesis of this work is based on the observation that certain expressive categories such as genre or theme can be recognized on the basis of the visual content alone. The experiments are conducted for three MIR tasks Artist Identification, Music Genre Classification and Cross-Genre Classification.
  arXiv  Detail & Related papers  (2020-02-01T17:57:14Z)
• Modeling Musical Structure with Artificial Neural Networks [0.0]
  I explore the application of artificial neural networks to different aspects of musical structure modeling. I show how a connectionist model, the Gated Autoencoder (GAE), can be employed to learn transformations between musical fragments. I propose a special predictive training of the GAE, which yields a representation of polyphonic music as a sequence of intervals.
  arXiv  Detail & Related papers  (2020-01-06T18:35:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.