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

In this paper we present a novel method for jointly extracting beats and downbeats from
audio signals. A recurrent neural network operating directly on magnitude spectrograms is …

As an important format of multimedia, music has filled almost everyone's life. Automatic
analyzing of music is a significant step to satisfy people's need for music retrieval and music …

Existing systems for automatic transcription of drum tracks from polyphonic music focus on
detecting drum instrument onsets but lack consideration of additional meta information like …

ABSTRACT Dynamic Bayesian networks (eg, Hidden Markov Models) are popular
frameworks for meter tracking in music because they are able to incorporate prior …

The online estimation of rhythmic information, such as beat positions, downbeat positions,
and meter, is critical for many real-time music applications. Musical rhythm comprises …

In this paper, we propose a system that extracts the downbeat times from a beat-
synchronous audio feature stream of a music piece. Two recurrent neural networks are used …

In this paper, we present a novel state-of-the-art system for automatic downbeat tracking
from music signals. The audio signal is first segmented in frames which are synchronized at …

Motivic pattern classification from music audio recordings is a challenging task. More so in
the case of a cappella flamenco cantes, characterized by complex melodic variations, pitch …

The human ability to track musical downbeats is robust to changes in tempo, and it extends
to tempi never previously encountered. We propose a deterministic time-warping operation …