arXiv subjects

Bob L. T. Sturm

Publications and source records attributed to Bob L. T. Sturm.

12 recordsLinked to original sources

arXiv2025

Data-Driven Analysis of Text-Conditioned AI-Generated Music: A Case Study with Suno and Udio

Online AI platforms for creating music from text prompts (AI music), such as Suno and Udio, are now being used by hundreds of thousands of users. Some AI music is appearing in advertising, and even charting, in multiple countries. How are these platforms being used? What subjects are inspiring their users? This article answers these questions for Suno and Udio using a large collection of songs generated by users of these platforms from May to October 2024. Using a combination of state-of-the-art text embedding models, dimensionality reduction and clustering methods, we analyze the prompts, tags and lyrics, and automatically annotate and display the processed data in interactive plots. Our results reveal prominent themes in lyrics, language preference, prompting strategies, as well as peculiar attempts at steering models through the use of metatags. To promote the musicological study of the developing cultural practice of AI-generated music we share our code and resources.

cs.IR↗

arXiv2025

"I made this (sort of)": Negotiating authorship, confronting fraudulence, and exploring new musical spaces with prompt-based AI music generation

I reflect on my experience creating two music albums centered on state-of-the-art prompt-based AI music generation platforms. The first album explicitly poses the question: What happens when I collide my junk mail with these platforms? The second album is a direct response to the first, and toys with the inability of state-of-the-art prompt-based AI music generation platforms to generate music that is not ``practiced'', ``polished'', and ``produced''. I seed a large language model (LLM) with information about these albums and have it interview me, which results in the exploration of several deeper questions: To what extent am I the author? Where am I in the resulting music? How is my musical identity changing as I am faced with machines that are in some ways far more talented than I? What new musical spaces does my work open, for me or anyone/thing else? I conclude by reflecting on my reflections, as well as LLM-mediated self-reflection as method.

cs.SD↗

arXiv2025

SMART: Tuning a symbolic music generation system with an audio domain aesthetic reward

Recent work has proposed training machine learning models to predict aesthetic ratings for music audio. Our work explores whether such models can be used to finetune a symbolic music generation system with reinforcement learning, and what effect this has on the system outputs. To test this, we use group relative policy optimization to finetune a piano MIDI model with Meta Audiobox Aesthetics ratings of audio-rendered outputs as the reward. We find that this optimization has effects on multiple low-level features of the generated outputs, and improves the average subjective ratings in a preliminary listening study with $14$ participants. We also find that over-optimization dramatically reduces diversity of model outputs.

cs.SD↗

arXiv2024

Steer-by-prior Editing of Symbolic Music Loops

With the goal of building a system capable of controllable symbolic music loop generation and editing, this paper explores a generalisation of Masked Language Modelling we call Superposed Language Modelling. Rather than input tokens being known or unknown, a Superposed Language Model takes priors over the sequence as input, enabling us to apply various constraints to the generation at inference time. After detailing our approach, we demonstrate our model across various editing tasks in the domain of multi-instrument MIDI loops. We end by highlighting some limitations of the approach and avenues for future work. We provides examples from the SLM across multiple generation and editing tasks at https://erl-j.github.io/slm-mml-demo/.

cs.SD↗

arXiv2024

SYMPLEX: Controllable Symbolic Music Generation using Simplex Diffusion with Vocabulary Priors

We present a new approach for fast and controllable generation of symbolic music based on the simplex diffusion, which is essentially a diffusion process operating on probabilities rather than the signal space. This objective has been applied in domains such as natural language processing but here we apply it to generating 4-bar multi-instrument music loops using an orderless representation. We show that our model can be steered with vocabulary priors, which affords a considerable level control over the music generation process, for instance, infilling in time and pitch and choice of instrumentation -- all without task-specific model adaptation or applying extrinsic control.

cs.SD↗

arXiv2023

Retrieval Augmented Generation of Symbolic Music with LLMs

We explore the use of large language models (LLMs) for music generation using a retrieval system to select relevant examples. We find promising initial results for music generation in a dialogue with the user, especially considering the ease with which such a system can be implemented. The code is available online.

cs.SD↗

arXiv2023

DDSP-based Neural Waveform Synthesis of Polyphonic Guitar Performance from String-wise MIDI Input

We explore the use of neural synthesis for acoustic guitar from string-wise MIDI input. We propose four different systems and compare them with both objective metrics and subjective evaluation against natural audio and a sample-based baseline. We iteratively develop these four systems by making various considerations on the architecture and intermediate tasks, such as predicting pitch and loudness control features. We find that formulating the control feature prediction task as a classification task rather than a regression task yields better results. Furthermore, we find that our simplest proposed system, which directly predicts synthesis parameters from MIDI input performs the best out of the four proposed systems. Audio examples are available at https://erl-j.github.io/neural-guitar-web-supplement.

cs.SD↗

arXiv2023

Exploring Softly Masked Language Modelling for Controllable Symbolic Music Generation

This document presents some early explorations of applying Softly Masked Language Modelling (SMLM) to symbolic music generation. SMLM can be seen as a generalisation of masked language modelling (MLM), where instead of each element of the input set being either known or unknown, each element can be known, unknown or partly known. We demonstrate some results of applying SMLM to constrained symbolic music generation using a transformer encoder architecture. Several audio examples are available at https://erl-j.github.io/smlm-web-supplement/

cs.SD↗

arXiv2023

Validity in Music Information Research Experiments

Validity is the truth of an inference made from evidence, such as data collected in an experiment, and is central to working scientifically. Given the maturity of the domain of music information research (MIR), validity in our opinion should be discussed and considered much more than it has been so far. Considering validity in one's work can improve its scientific and engineering value. Puzzling MIR phenomena like adversarial attacks and performance glass ceilings become less mysterious through the lens of validity. In this article, we review the subject of validity in general, considering the four major types of validity from a key reference: Shadish et al. 2002. We ground our discussion of these types with a prototypical MIR experiment: music classification using machine learning. Through this MIR experimentalists can be guided to make valid inferences from data collected from their experiments.

cs.SD↗

arXiv2022

Audio Latent Space Cartography

We explore the generation of visualisations of audio latent spaces using an audio-to-image generation pipeline. We believe this can help with the interpretability of audio latent spaces. We demonstrate a variety of results on the NSynth dataset. A web demo is available.

cs.SD↗

arXiv2022

TimbreCLIP: Connecting Timbre to Text and Images

We present work in progress on TimbreCLIP, an audio-text cross modal embedding trained on single instrument notes. We evaluate the models with a cross-modal retrieval task on synth patches. Finally, we demonstrate the application of TimbreCLIP on two tasks: text-driven audio equalization and timbre to image generation.

cs.SD↗

arXiv2020

Dataset artefacts in anti-spoofing systems: a case study on the ASVspoof 2017 benchmark

The Automatic Speaker Verification Spoofing and Countermeasures Challenges motivate research in protecting speech biometric systems against a variety of different access attacks. The 2017 edition focused on replay spoofing attacks, and involved participants building and training systems on a provided dataset (ASVspoof 2017). More than 60 research papers have so far been published with this dataset, but none have sought to answer why countermeasures appear successful in detecting spoofing attacks. This article shows how artefacts inherent to the dataset may be contributing to the apparent success of published systems. We first inspect the ASVspoof 2017 dataset and summarize various artefacts present in the dataset. Second, we demonstrate how countermeasure models can exploit these artefacts to appear successful in this dataset. Third, for reliable and robust performance estimates on this dataset we propose discarding nonspeech segments and silence before and after the speech utterance during training and inference. We create speech start and endpoint annotations in the dataset and demonstrate how using them helps countermeasure models become less vulnerable from being manipulated using artefacts found in the dataset. Finally, we provide several new benchmark results for both frame-level and utterance-level models that can serve as new baselines on this dataset.

eess.AS↗