ProgGP: From GuitarPro Tablature Neural Generation To Progressive Metal Production
Recent work in the field of symbolic music generation has shown value in using a tokenization based on the GuitarPro format, a symbolic representation supporting guitar expressive attributes, as an input and output representation. We extend this work by fine-tuning a pre-trained Transformer model on ProgGP, a custom dataset of 173 progressive metal songs, for the purposes of creating compositions from that genre through a human-AI partnership. Our model is able to generate multiple guitar, bass guitar, drums, piano and orchestral parts. We examine the validity of the generated music using a mixed methods approach by combining quantitative analyses following a computational musicology paradigm and qualitative analyses following a practice-based research paradigm. Finally, we demonstrate the value of the model by using it as a tool to create a progressive metal song, fully produced and mixed by a human metal producer based on AI-generated music.
Code (0)
등록된 구현이 없습니다.
Tasks
Music GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models
Originating in the Renaissance and burgeoning in the digital era, tablatures are a commonly used music notation system which provides explicit representations of instrument fingerings rather than pitches. GuitarPro has e…
DecoderGenre classificationMusic GenerationMusic Style Transfer+1Between the AI and Me: Analysing Listeners' Perspectives on AI- and Human-Composed Progressive Metal Music
Generative AI models have recently blossomed, significantly impacting artistic and musical traditions. Research investigating how humans interact with and deem these models is therefore crucial. Through a listening and r…
SCORE-SET: A dataset of GuitarPro files for Music Phrase Generation and Sequence Learning
A curated dataset of Guitar Pro tablature files (.gp5 format), tailored for tasks involving guitar music generation, sequence modeling, and performance-aware learning is provided. The dataset is derived from MIDI notes i…
Music GenerationRock Guitar Tablature Generation via Natural Language Processing
Deep learning has recently empowered and democratized generative modeling of images and text, with additional concurrent works exploring the possibility of generating more complex forms of data, such as audio. However, t…
GTR-CTRL: Instrument and Genre Conditioning for Guitar-Focused Music Generation with Transformers
Recently, symbolic music generation with deep learning techniques has witnessed steady improvements. Most works on this topic focus on MIDI representations, but less attention has been paid to symbolic music generation u…
Genre classificationMusic GenerationRhythm