RT Journal Article
T1 MetrikaBox: An open framework for experimenting with audio classification
A1 Perianez-Pascual, Jorge
A1 Gutiérrez, Juan D.
A1 Delgado, Emilio
A1 Sánchez-Figueroa, Fernando
A1 Rodriguez-Echeverria, Roberto
K1 Artificial Intelligence
K1 Deep Learning
K1 Audio classification
K1 Neural networks
K1 Digital signal processing
K1 Software engineering
AB This paper presents MetrikaBox, a general-purpose, open-source, and extensible audio classification package designed to facilitate the development of Deep Learning (DL) models for a wide range of audio processing tasks. The software manages all necessary preprocessing steps to build classification models capable of distinguishing between user-defined classes using advanced Artificial Intelligence (AI) techniques. MetrikaBox is well suited for tasks such as musical genre classification, voice-versus-music discrimination, and other audio classification or segmentation applications. Users can either employ the package as provided or extend it by integrating their own datasets, classification models, data loading systems, augmentation techniques, and more. The package has been tested in both commercial and academic settings, where it has produced models for industrial audio processing and served as a platform for proof-of-concept applications. Comprehensive documentation and practical examples included in the repository support users in integrating the system into their audio analysis projects. MetrikaBox is openly available and provides a user interface for convenient testing.
PB Elsevier
SN 2352-7110
YR 2025
FD 2025-08-28
LK https://hdl.handle.net/10347/42739
UL https://hdl.handle.net/10347/42739
LA eng
NO SoftwareX Volume 31, September 2025, 102306
NO This work was supported by Grant CPP2021-008491 funded by MICIU/AEI/10.13039/50100011033 and by the European Union NextGeneration EU/PRTR.
DS Minerva
RD 24 abr 2026