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 …

We explore frame-level audio feature learning for chord recognition using artificial neural
networks. We present the argument that chroma vectors potentially hold enough information …

This survey explores the symbiotic relationship between Machine Learning (ML) and music,
focusing on the transformative role of Artificial Intelligence (AI) in the musical sphere …

Reference annotation datasets containing harmony annotations are at the core of a wide
range of studies in music information retrieval (mir) and related fields. The majority of these …

Chord recognition systems depend on robust feature extraction pipelines. While these
pipelines are traditionally hand-crafted, recent advances in end-to-end machine learning …

In this paper, we present a novel architecture for audio chord estimation using a hybrid
recurrent neural network. The architecture replaces hidden Markov models (HMMs) with …

Music information retrieval (MIR) is “a multidisciplinary research endeavor that strives to
develop innovative content‐based searching schemes, novel interfaces, and evolving …

Imbalanced data is a typical problem in the supervised classification field, which occurs
when the different classes are not equally represented. This fact typically results in the …

To assess the performance of an automatic chord estimation system, reference annotations
are indispensable. However, owing to the complexity of music and the sometimes …

There is considerable debate over the benefits of recording and rendering high resolution
audio, ie, systems and formats that are capable of rendering beyond CD quality audio. We …