Open-Source Practices for Music Signal Processing Research: Recommendations for Transparent, Sustainable, and Reproducible Audio Research

By Brian McFee, Jong Wook Kim, Mark Brozier Cartwright, Justin Salamon, Rachel M. Bittner, Juan Pablo Bello. 2019

53 citations recorded by the source corpus.

In the early years of music information retrieval (MIR), research problems were often centered around conceptually simple tasks, and methods were evaluated on small, idealized data sets. A canonical example of this is genre recognition-i.e., Which one of n genres describes this song?-which was often evaluated on the GTZAN data set (1,000 musical excerpts balanced across ten genres) [1]. As task definitions were simple, so too were signal analysis pipelines, which often derived from methods for speech processing and recognition and typically consisted of simple methods for feature extraction, statistical modeling, and evaluation. When describing a research system, the expected level of detail was superficial: it was sufficient to state, e.g., the number of mel-frequency cepstral coefficients used, the statistical model (e.g., a Gaussian mixture model), the choice of data set, and the evaluation criteria, without stating the underlying software dependencies or implementation details. Because of an increased abundance of methods, the proliferation of software toolkits, the explosion of machine learning, and a focus shift toward more realistic problem settings, modern research systems are substantially more complex than their predecessors. Modern MIR researchers must pay careful attention to detail when processing metadata, implementing evaluation criteria, and disseminating results.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Robust sound event detection in bioacoustic sensor networks (2019, background): “In an effort of conducting transparent, sustainable, and reproducible audio research [64], BirdVoxDetect also comprises documentation, a test suite, a Python package indexation, and an interoperable application programming interface (API).”
Open-Unmix - A Reference Implementation for Music Source Separation (2019, background): “Releasing Open-Unmix is first and foremost an attempt to provide a reliable implementation sticking to established programming practice as were also proposed in (McFee et al., 2018).”
Expediting TTS Synthesis with Adversarial Vocoding (2019, background): “Precisely recreating this representation [14] is simple in our codebase.”