Self-supervised learning (SSL) has recently emerged as a promising paradigm for training
generalisable models on large-scale data in the fields of vision, text, and speech. Although …

We demonstrate that language models pre-trained on codified (discretely-encoded) music
audio learn representations that are useful for downstream MIR tasks. Specifically, we …

DB), a shared database containing original street dance videos with copyright-cleared
dance music. Although dancing is highly related to dance music and dance information can …

An Introduction to Audio Content Analysis Enables readers to understand the algorithmic
analysis of musical audio signals with AI-driven approaches An Introduction to Audio …

In this paper, we undertake a critical assessment of a stateof-the-art deep neural network
approach for computational rhythm analysis. Our methodology is to deconstruct this …

In this work, we provide a broad comparative analysis of strategies for pre-training audio
understanding models for several tasks in the music domain, including labelling of genre …

We propose a multi-task learning approach for simultaneous tempo estimation and beat
tracking of musical audio. The system shows state-of-the-art performance for both tasks on a …

In the era of extensive intersection between art and Artificial Intelligence (AI), such as image
generation and fiction co-creation, AI for music remains relatively nascent, particularly in …

We present a single-step musical tempo estimation system based solely on a convolutional
neural network (CNN). Contrary to existing systems, which typically first identify onsets or …

Self-supervised learning (SSL) has shown promising results in various speech and natural
language processing applications. However, its efficacy in music information retrieval (MIR) …