Following their success in Computer Vision and other areas, deep learning techniques have
recently become widely adopted in Music Information Retrieval (MIR) research. However …

Given the recent surge in developments of deep learning, this paper provides a review of the
state-of-the-art deep learning techniques for audio signal processing. Speech, music, and …

The task of estimating the fundamental frequency of a monophonic sound recording, also
known as pitch tracking, is fundamental to audio processing with multiple applications in …

Music transcription is the process of transforming recorded sound of musical performances
into symbolic representations such as sheet music or MIDI files. Extensive research and …

Automatic music transcription (AMT) is the task of transcribing audio recordings into
symbolic representations. Recently, neural network-based methods have been applied to …

Convolutional Neural Networks (CNN) have been applied to diverse machine learning tasks
for different modalities of raw data in an end-to-end fashion. In the audio domain, a raw …

Recent advances in deep learning accelerated the development of content-based automatic
music tagging systems. Music information retrieval (MIR) researchers proposed various …

The guitar is a popular instrument for a variety of reasons, including its ability to produce
polyphonic sound and its musical versatility. The resulting variability of sounds, however …

We introduce a trainable front-end module for audio representation learning that exploits the
inherent harmonic structure of audio signals. The proposed architecture, composed of a set …

Multi-instrument automatic music transcription (AMT) is a critical but less investigated
problem in the field of music information retrieval (MIR). With all the difficulties faced by …