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 …

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 …

In this paper, we introduce a novel method for the automatic estimation of downbeat
positions from music signals. Our system relies on the computation of musically inspired …

We define a novel system for the automatic estimation of downbeat positions from audio
music signals. New rhythm and melodic features are introduced and feature adapted …

This letter presents a novel system architecture that integrates blind source separation with
joint beat and downbeat tracking in musical audio signals. The source separation module …

Putting forward an extensive new argument for a humanities-based approach to big-data
analysis, The Music in the Data shows how large datasets of music, or music corpora, can …

The computational approach of autocorrelation relies on recurrent patterns within a musical
signal to identify and analyze the meter of musical passages. This paper suggests that the …

This paper presents a feedback framework that can improve chord recognition for music
audio signals by performing approximate note transcription with Bayesian non-negative …