We present G.A.D.A. (Guitar Audio Dataset for AI), a novel open-source dataset designed for advancing research in guitar audio analysis, signal processing, and machine learning applications. This comprehensive corpus comprises recordings from three main guitar categories: electric, acoustic, and bass guitars, featuring multiple instruments within each category to ensure dataset diversity and robustness.
The recording methodology employs two distinct approaches based on instrument type. Electric and bass guitars were recorded using direct recording techniques via DI boxes, providing clean, unprocessed signals ideal for further digital processing and manipulation. For acoustic guitars, where direct recording was not feasible, we utilized multiple microphone configurations at various positions to capture the complete acoustic properties of the instruments. Both recording approaches prioritize signal quality while maintaining maximum flexibility for subsequent processing and analysis.
The dataset includes standardized recordings of major and minor chords played in multiple positions and voicings across all instruments. Each recording is accompanied by detailed metadata, including instrument specifications, recording equipment details, microphone configurations (for acoustic guitars), and chord information. The clean signals from electric instruments enable various post-processing applications, including virtual amplifier modeling, effects processing, impulse response convolution, and room acoustics simulation.
To evaluate G.A.D.A.'s effectiveness in machine learning applications, we propose a comprehensive testing framework using established algorithms including k-Nearest Neighbors, Support Vector Machines, Convolutional Neural Networks, and Feed-Forward Neural Networks. These experiments will focus on instrument classification tasks using both traditional audio features and deep learning approaches.
G.A.D.A. will be freely available for academic and research purposes, complete with documentation, preprocessing scripts, example code, and usage guidelines. This resource aims to facilitate research in musical instrument classification, audio signal processing, deep learning applications in music technology, computer-aided music education, and automated music transcription systems.
The combination of standardized recording methodologies, comprehensive metadata, and the inclusion of both direct-recorded and multi-microphone captured audio makes G.A.D.A. a valuable resource for comparative studies and reproducible research in music information retrieval and audio processing.
