Musicians Experiment With Google’s Music AI Sandbox

Wyclef Jean, Justin Tranter and Marc Rebillet demonstrate ways to explore sounds and develop musical ideas.

WJWyclef Jean登壇者JTJustin Tranter登壇者
チャンネル
Google DeepMind
公開日
2024年5月14日
動画
3:01
読了時間
1 分
原文
英語
YouTube で開く
スクロールすると動画は右下に小さくなって再生を続けます。字幕は閲覧言語に合わせて切り替わります。
閲覧言語2 言語で読めます
この記事はまだ日本語に翻訳されていないため、英語の原文を表示しています。
要約

30 秒でわかる

In this 2024 demonstration, musicians use Music AI Sandbox to generate and select musical material, experiment with descriptive prompts and add texture to loops.

  1. 1A virtual crateJean selects generated fragments as he would search records for samples.
  2. 2Describing a moodTranter and Blush use descriptive prompts to explore sounds.
  3. 3Filling out a loopRebillet tries new instrumental textures and chooses what to keep.

ポイント

  1. Generated material can serve as a starting point for sampling and selection.
  2. Descriptive prompts can help explore a desired mood.
  3. Musicians retain creative choices about which outputs to use.
全文

チャプターごとに整えた全文を読む

文字起こしのみ
01

Sampling and experimentation

Wyclef Jean experiments with Music AI Sandbox, a set of tools developed by Google DeepMind and YouTube with musicians. He tries combining musical influences and listens for unexpected results.

Wyclef Jean experiments with Music AI Sandbox, a set of tools developed by Google DeepMind and YouTube with musicians. He tries combining musical influences and listens for unexpected results.

Jean compares the process to a hip-hop producer digging through records for a sample. Instead of searching physical vinyl, he listens to generated material, selects a fragment and incorporates it into a song.

02

Songwriting with prompts

Songwriter Justin Tranter experiments with chords and describes the appeal of expressing ideas in a familiar musical language, even without being a specialist in modern production software.

Songwriter Justin Tranter experiments with chords and describes the appeal of expressing ideas in a familiar musical language, even without being a specialist in modern production software.

Tranter and collaborator Blush try atmospheric prompts, using places and eras to suggest a mood. They listen to the outputs and adjust their direction as they work.

03

Adding texture to loops

Marc Rebillet uses generated elements to fill gaps in a loop, experimenting with percussion, viola and clapping. He describes the tool as a collaborator offering ideas he can accept, reject or reshape.

Marc Rebillet uses generated elements to fill gaps in a loop, experimenting with percussion, viola and clapping. He describes the tool as a collaborator offering ideas he can accept, reject or reshape.

Jean says the tools can speed up moving ideas from his head into a track. The video shows demonstrations by participating artists; their enthusiasm is their experience, not a guarantee of a particular result for every musician.

同じチャンネルの記事

スライド 5 枚38:10EN
Google DeepMind· 2026年10月1日From deepfakes to DNA: how SynthID watermarks AIHannah Fry, Pushmeet Kohli, Jeremy Ratcliffe · Google DeepMindHannah Fry sits down with Pushmeet Kohli and Jeremy Ratcliffe to explain SynthID: a mathematical watermark now used on text, images and video, and being extended to protein sequences so DNA synthesis companies can tell AI-designed molecules from nature.BiosecurityComputational BiologyGenerative media+2
44:25EN
Google DeepMind· 2026年9月9日How AI Is Changing Weather ForecastingHannah Fry, Peter Battaglia · Google DeepMindHannah Fry and Peter Battaglia discuss hurricane forecasts, uncertainty and WeatherNext 3.AI researchモデルと学習
44:41EN
Google DeepMind· 2026年8月26日The Mathematics of AI UncertaintyZoubin GhahramaniZoubin Ghahramani explains Bayesian updating, calibration and their computational challenges.AI researchMachine Learning
10:52PT
Google DeepMind· 2026年7月14日Reconstruindo o gol perdido de PeléPesquisa histórica, atores e IA recriam um lance de 1959 sem filmagem original.HistoryGenerative media
53:05EN
Google DeepMind· 2026年7月10日Inside AI Models: Neel Nanda on Interpretability and SafetyHannah Fry, Neel Nanda · Google DeepMindHannah Fry explores reasoning traces, probes and sparse autoencoders.Model InterpretabilityAI SafetyAI research
42:38EN
Google DeepMind· 2026年6月23日When AI Agents Delegate: Security and a Possible Agent EconomyHannah Fry, Nenad Tomašev · Google DeepMindNenad Tomašev discusses specialists, oversight and correlated failures with Hannah Fry.Multi-agent systemsエージェント工学AI security