With the advent of deep learning, global tempo estimation accuracy has reached a new peak, which presents a great opportunity to evaluate our evaluation practices. In this article, we discuss presumed and actual applications, the pros and cons of commonly used metrics, and the suitability of popular datasets. To guide future research, we present results of a survey among domain experts that investigates today's applications, their requirements, and the usefulness of currently employed metrics. To aid future evaluations, we present a public repository containing evaluation code as well as estimates by many different systems and different ground truths for popular datasets.
Tempo estimation is a fundamental problem in music information retrieval. Most approaches attempt to solve two problems: first finding a dominant pulse and second correcting the metrical level of this pulse. The latter has also been dubbed fixing the octave error. We propose an algorithm for tempo estimation that addresses both problems mostly independently. While using a standard pulse detection technique, for octave error correction, we exploit a simple relationship between a single global feature, average spectral novelty, and listener perception of musical tempo. The proposed method is extremely simple. Nevertheless, it outperforms most existing tempo estimation methods and is on par with the best-performing ones. It thus exemplifies that a global feature-based approach can significantly improve tempo estimation.
In this article we explore how the different semantics of spectrograms' time and frequency axes can be exploited for musical tempo and key estimation using Convolutional Neural Networks (CNN). By addressing both tasks with the same network architectures ranging from shallow, domain-specific approaches to deep variants with directional filters, we show that axis-aligned architectures perform similarly well as common VGG-style networks developed for computer vision, while being less vulnerable to confounding factors and requiring fewer model parameters.
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