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 …

Singing melody extraction essentially involves two tasks: one is detecting the activity of a
singing voice in polyphonic music, and the other is estimating the pitch of a singing voice in …

We present a single deep learning architecture that can both separate an audio recording of
a musical mixture into constituent single-instrument recordings and transcribe these …

Melody extraction in polyphonic musical audio is important for music signal processing. In
this paper, we propose a novel streamlined encoder/decoder network that is designed for …

In music source separation, the number of sources may vary for each piece and some of the
sources may belong to the same family of instruments, thus sharing timbral characteristics …

Separating a singing voice from its music accompaniment remains an important challenge in
the field of music information retrieval. We present a unique neural network approach …

Prior information about the target source can improve audio source separation quality but is
usually not available with the necessary level of audio alignment. This has limited its …

Training a robust and reliable deep learning model requires a large amount of data. In the
crisis domain, building deep learning models to identify actionable information from the …

A music mashup combines audio elements from two or more songs to create a new work. To
reduce the time and effort required to make them, researchers have developed algorithms …

This paper describes a multi-task learning approach to joint extraction (fundamental
frequency (F0) estimation) and separation of singing voices from music signals. While deep …