In recent years, foundation models (FMs) such as large language models (LLMs) and latent
diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This …

Music is an integral part of human culture, embodying human intelligence and creativity, of
which songs compose an essential part. While various aspects of song generation have …

Building on the foundations of language modeling in natural language processing, Next
Token Prediction (NTP) has evolved into a versatile training objective for machine learning …

We introduce Self-Monitored Inference-Time INtervention (SMITIN), an approach for
controlling an autoregressive generative music transformer using classifier probes. These …

Current version identification (VI) datasets often lack sufficient size and musical diversity to
train robust neural networks (NNs). Additionally, their non-representative clique size …

We present a framework for learning to generate background music from video inputs.
Unlike existing works that rely on symbolic musical annotations, which are limited in quantity …

Challenges in managing linguistic diversity and integrating various musical modalities are
faced by current music information retrieval systems. These limitations reduce their …

Multimodal music generation aims to produce music from diverse input modalities, including
text, videos, and images. Existing methods use a common embedding space for multimodal …

Music scores are written representations of music and contain rich information about musical
components. The visual information on music scores includes notes, rests, staff lines, clefs …

Progress in the task of symbolic music generation may be lagging behind other tasks like
audio and text generation, in part because of the scarcity of symbolic training data. In this …