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 …

We propose the use of Temporal Convolutional Networks for audio-based beat tracking. By
contrasting our convolutional approach with the current state-of-the-art recurrent approach …

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 …

Musical performance combines a wide range of pitches, nuances, and expressive
techniques. Audio-based classification of musical instruments thus requires to build signal …

Many researchers use convolutional neural networks with small rectangular filters for music
(spectrograms) classification. First, we discuss why there is no reason to use this filters setup …

We introduce the Harmonix set: a collection of annotations of beats, downbeats, and
functional segmentation for over 900 full tracks that covers a wide range of western popular …

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 …

Audio signal processing is the most challenging field in the current era for an analysis of an
audio signal. Audio signal classification (ASC) comprises of generating appropriate features …