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We present Essentia 2.0, an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set(More)
In this paper, we propose a method that can identify challenging music samples for beat tracking without ground truth. Our method, motivated by the machine learning method “selective sampling,” is based on the measurement of mutual agreement between beat sequences. In calculating this mutual agreement we show the critical influence of(More)
In this paper we establish a threshold for perceptually acceptable beat tracking based on the mutual agreement of a committee of beat trackers. In the first step we use an existing annotated dataset to show that mutual agreement can be used to select one committee member as the most reliable beat tracker for a song. Then we conduct a listening test using a(More)
We present Essentia 2.0, an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set(More)
In this paper, an approach is presented that identifies music samples which are difficult for current state-of-the-art beat trackers. In order to estimate this difficulty even for examples without ground truth, a method motivated by selective sampling is applied. This method assigns a degree of difficulty to a sample based on the mutual disagreement between(More)
A recent trend in the field of beat tracking for musical audio signals has been to explore techniques for measuring the level of agreement and disagreement between a committee of beat tracking algorithms. By using beat tracking evaluation methods to compare all pairwise combinations of beat tracker outputs, it has been shown that selecting the beat tracker(More)
Beat tracking estimation from music signals becomes difficult in the presence of highly predominant vocals. We compare the performance of five state-of-the-art algorithms on two datasets, a generic annotated collection and a dataset comprised of song excerpts with highly predominant vocals. Then, we use seven state-of-the-art audio voice suppression(More)
The Multi-feature Beat tracker uses 5 different onsets detection function to estimates the beats of a musical audio signal using only one beat tracker algorithm, finally the beat tracker output is selected using a committee technique presented in previous works. The algorithm ZDG2 get the higher value in five of the ten measures in the Mckinney Dataset in(More)
The automatic beat tracking from audio is still an open research task in the Music Information Retrieval (MIR) community. The goal of this paper is to show and discuss a work-in-progress of how audio source separation can be used for improving beat tracking estimations in difficult cases of music audio signal with highly predominant vocals. The audio source(More)