QuiKo: A Quantum Beat Generation Application
원문 초록
In this chapter a quantum music generation application called QuiKo will be discussed. It combines existing quantum algorithms with data encoding methods from quantum machine learning to build drum and audio sample patterns from a database of audio tracks. QuiKo leverages the physical properties and characteristics of quantum computers to generate what can be referred to as Soft Rules proposed by Alexis Kirke. These rules take advantage of the noise produced by quantum devices to develop flexible rules and grammars for quantum music generation. These properties include qubit decoherence and phase kickback due controlled quantum gates within the quantum circuit. QuiKo builds upon the concept of soft rules in quantum music generation and takes it a step further. It attempts to mimic and react to an external musical inputs, similar to the way that human musicians play and compose with one another. Audio signals are used as inputs into the system. Feature extraction is then performed on the signal to identify the harmonic and percussive elements. This information is then encoded onto the quantum circuit. Measurements of the quantum circuit are then taken providing results in the form of probability distributions for external music applications to use to build the new drum patterns.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
WildFX: A DAW-Powered Pipeline for In-the-Wild Audio FX Graph Modeling
TOMI: Transforming and Organizing Music Ideas for Multi-Track Compositions with Full-Song Structure
From Generality to Mastery: Composer-Style Symbolic Music Generation via Large-Scale Pre-training
Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion
Personalizable Long-Context Symbolic Music Infilling with MIDI-RWKV
The interplay of robustness and generalization in quantum machine learning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.