Sequences of feature vectors are a natural way of representing temporal data. Given a
database of sequences, a fundamental task is to find the database entry which is the most …

In this paper, we present madmom, an open-source audio processing and music information
retrieval (MIR) library written in Python. madmom features a concise, NumPy-compatible …

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 …

In this paper, we undertake a critical assessment of a stateof-the-art deep neural network
approach for computational rhythm analysis. Our methodology is to deconstruct this …

We explore frame-level audio feature learning for chord recognition using artificial neural
networks. We present the argument that chroma vectors potentially hold enough information …

We propose a multi-task learning approach for simultaneous tempo estimation and beat
tracking of musical audio. The system shows state-of-the-art performance for both tasks on a …

Transformer is a successful deep neural network (DNN) architecture that has shown its
versatility not only in natural language processing but also in music information retrieval …

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 …

In this paper we present a new tempo estimation algorithm which uses a bank of resonating
comb filters to determine the dominant periodicity of a musical excerpt. Unlike existing (comb …

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