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Joint Beat and Downbeat Tracking with Recurrent Neural Networks
A recurrent neural network operating directly on magnitude spectrograms is used to model the metrical structure of the audio signals at multiple levels and provides an output feature that clearly distinguishes between beats and downbeats.
madmom: A New Python Audio and Music Signal Processing Library
- Sebastian Böck, Filip Korzeniowski, Jan Schlüter, Florian Krebs, G. Widmer
- Computer ScienceACM Multimedia
- 23 May 2016
Madmom is an open-source audio processing and music information retrieval (MIR) library written in Python that features a concise, NumPy-compatible, object oriented design with simple calling conventions and sensible default values for all parameters that facilitates fast prototyping of MIR applications.
Evaluating the Online Capabilities of Onset Detection Methods
A new onset detection method based on the common spectral flux and a new peak-picking method which outperforms traditional methods both online and offline and works with audio signals of various volume levels are proposed.
The k-Nearest Neighbour Join: Turbo Charging the KDD Process
This paper proposes an important, third similarity join operation called the k-nearest neighbour join, which combines each point of one point set with its k nearest neighbours in the other set, and proposes a new algorithm to compute this join using the multipage index (MuX), a specialised index structure for the similarity join.
Epsilon grid order: an algorithm for the similarity join on massive high-dimensional data
The Epsilon Grid Order is proposed, a new algorithm for determining the similarity join of very large data sets, based on a particular sort order of the data points, obtained by laying an equi-distant grid with cell length ε over the data space and comparing the grid cells lexicographically.
A Multi-model Approach to Beat Tracking Considering Heterogeneous Music Styles
A new beat tracking algorithm which extends an existing state-of-the-art system with a multi-model approach to represent different music styles and is able to match even human tapping performance.
Accurate Tempo Estimation Based on Recurrent Neural Networks and Resonating Comb Filters
A new tempo estimation algorithm which uses a bank of resonating comb filters to determine the dominant periodicity of a musical excerpt by simply reporting the highest resonator’s histogram peak.
Downbeat Tracking Using Beat Synchronous Features with Recurrent Neural Networks
A system that extracts the downbeat times from a beat-synchronous audio feature stream of a music piece and shows on seven commonly used datasets of Western music that the system is able to achieve state-of-the-art results.
An Efficient State-Space Model for Joint Tempo and Meter Tracking
A new state-space discretisation and tempo transition model for Dynamic Bayesian networks is proposed that can act as a drop-in replacement and not only increases the beat and downbeat tracking accuracy, but also reduces time and memory complexity drastically.
Tracking the "Odd": Meter Inference in a Culturally Diverse Music Corpus
This work approaches the tasks of beat tracking, downbeat recognition and rhythmic style classification in nonWestern music based on a Bayesian model, which infers tempo, dow and downbeat.