Daniel Stoller, Mi Tian, Sebastian Ewert, and Simon Dixon
Open-Source Practices for Music Signal Processing Research: Recommendations for Transparent, Sustainable, and Reproducible Audio Research
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) . 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.
Using a Neural Network Codec Approximation Loss to Improve Source Separation Performance in Limited Capacity Networks
Ishwarya Ananthabhotla, Sebastian Ewert, Joseph A. Paradiso
Rachel M Bittner, Magdalena Fuentes, David Rubinstein, Andreas Jansson, Keunwoo Choi, Thor Kell