What is the last thing you learned?
The latest thing I learnt is about ai music covers.
You:
Can you say a bit about advances ai is making in musical cover creation. Frank sinatra covers of rock songs is an example. Can you write something that is a comprehensive summary I can use for a blog post pleas3?
Copilot:
Certainly! Advances in AI-generated music have been making waves in recent years, transforming the landscape of musical creation and cover production. Let’s delve into some exciting developments:
- Generative AI for Music Creation and Editing:
- Adobe Research has been at the forefront of this revolution. Their project, Music GenAI Control, combines generative AI with fine-grained editing capabilities.
- How it works:
- Creators input text prompts like “powerful rock,” “happy dance,” or “sad jazz.”
- The AI generates music based on these prompts.
- Users can then edit the generated audio with pixel-level control, adjusting tempo, structure, intensity, and more.
• Impact:
• Workflow efficiency: Instead of manually cutting existing music, creators can tailor pieces to their precise needs.
• Deep control: It’s like Photoshop for music, allowing artists to shape and tweak audio1.
• Google DeepMind’s Lyria:
• Developed in partnership with YouTube, Lyria is an advanced AI music generation model.
• Dream Track:
• An experiment in YouTube Shorts, Dream Track fosters connections between artists, creators, and fans through music creation2.
• AI Tools for Creativity:
• Google Magenta and Amper:
• These AI systems can generate original compositions.
• RealTalk Studio and Voctro Labs:
• They create AI covers of existing songs with remarkable accuracy3.
• Bridging Ideas and Tangible Creations:
• ChatGPT for lyrics and Google’s MusicLM for melody generation:
• These tools bridge the gap between creative concepts and actual music.
• Stability AI’s Stable Audio:
• Empowers creators by facilitating musical creativity4.
In summary, AI is revolutionizing music by becoming a co-creator, offering deep control, and enabling novel ways to express artistic vision. Whether it’s Frank Sinatra covers of rock songs or entirely new compositions, AI is harmonizing creativity and technology in exciting ways! 烙
https://blog.adobe.com/en/publish/2024/02/28/adobe-research-audio-creation-editing
https://deepmind.google/discover/blog/transforming-the-future-of-music-creation/
The Rise of AI-Generated Music: Exploring the Best AI Covers
AI’s Impact on the Music Industry: Creativity and Controversies
https://link.springer.com/book/10.1007/978-3-030-72116-9
You:
What are some other examples of AI-generated music?
Copilot:
Certainly! AI-generated music has been a fascinating area of exploration, and several notable examples showcase the capabilities of artificial intelligence in the realm of music composition. Here are some intriguing instances:
• Jukebox by OpenAI:
• Description: Jukebox is a neural network that generates music, including rudimentary singing, as raw audio across various genres and artist styles.
• How it Works:
• Users input prompts like genre, artist, and lyrics.
• Jukebox produces new music samples from scratch.
• Impact:
• It pushes the boundaries of generative models by directly modeling music as raw audio, handling long-range dependencies, and capturing diverse styles1.
• AIVA’s “Hello World!”:
• Description: AIVA (Artificial Intelligence Virtual Artist) composed the piece “Hello World!” using its AI system.
• Significance:
• Demonstrates AI’s ability to create original compositions with emotional depth and stylistic variation2.
• DeepJazz’s “The Entertainer”:
• Description: DeepJazz, an AI program, generated a ragtime-style piano piece called “The Entertainer.”
• Noteworthy:
• It showcases AI’s capacity to emulate specific musical genres and historical styles2.
• Savages by AllttA:
• Description: An AI-generated song titled “Savages.”
• Context:
• The song exemplifies how AI can create original music across different genres3.
• Not Mine by Miquela:
• Description: Another AI-generated track, “Not Mine.”
• Implication:
• It highlights the potential for AI to produce music that resonates with listeners3.
• World is Mine by Supercell feat. Hatsune Miku:
• Description: An AI-generated song featuring the virtual singer Hatsune Miku.
• Significance:
• Merges AI creativity with vocal synthesis technology3.
• Meta’s AudioCraft:
• Description: Meta’s text-to-audio tool allows users to generate music based on input text.
• Features:
• Trained on licensed music and public sound effects.
• Enables creative exploration in music composition4.
These examples illustrate the exciting possibilities of AI-generated music, from raw audio compositions to genre-specific tracks. As AI continues to evolve, we can expect even more innovative and expressive musical creations! 烙
https://openai.com/research/jukebox
https://aicontentfy.com/en/blog/ai-generated-music-new-soundscape
Check Out These 5 Crazy Examples of AI Generated Music (Scary Good!)
https://builtin.com/artificial-intelligence/ai-music-examples
https://www.jeffbullas.com/ai-generated-music/jeffbullas.com
Can you explain how generative adversarial networks (GANs) work in relation to creating new songs?
Certainly! Generative Adversarial Networks (GANs) have revolutionized various domains, including music composition. Let’s explore how GANs work in the context of creating new songs:
• Overview of GANs:
• GANs consist of two neural networks:
• Generator: Creates content (e.g., music, images) from random noise.
• Discriminator: Determines whether the generated content is real or fake.
• The generator aims to produce authentic-looking data, fooling the discriminator.
• The discriminator learns to distinguish real data from generated data.
• This adversarial process leads to improved content generation over time.
• Time-Frequency Data Representation:
• Music differs from images; it’s not a simple 2D array.
• To apply GANs to music, we need an appropriate data representation.
• Piano Roll Representation:
• Each song is divided into segments (e.g., 16 beats).
• Each beat is further divided into time slices (e.g., 24 slices).
• Each slice contains a vector representing note volumes (128 possible notes).
• This results in a matrix (384 x 128) for the discriminator and generator.
• By maintaining translational invariance, we ensure effective convolution within the GAN.
• Training Process:
• We use a dataset of MIDI files (e.g., classical music).
• The generator produces music similar to piano rolls.
• The discriminator evaluates whether the generated music is authentic.
• The generator improves by learning from the discriminator’s feedback.
• Overlapping segments from songs create additional training samples.
• Data filtering reduces the input dataset to a manageable size.
• Challenges and Successes:
• Convolutional models in GANs can be tricky to train.
• Researchers have successfully applied GANs to generate music.
• Examples include piano compositions, orchestral pieces, and more.
• GANs capture patterns, harmonies, and structures, producing novel music1.
In summary, GANs offer a powerful approach to music generation by learning from existing compositions and creating new, expressive pieces. Whether it’s classical, jazz, or experimental music, GANs continue to push the boundaries of creativity in the musical realm! 烙
Click to access 1804.09399v1.pdf
https://arxiv.org/pdf/1709.06298v2.pdfhttps://link.springer.com/chapter/10.1007/978-3-031-18444-4_2
https://link.springer.com/chapter/10.1007/978-981-16-9885-9_9
https://link.springer.com/content/pdf/10.1007/978-981-16-9885-9_9.pdflink.springer.com9ganart.dev
Certainly! Here’s a heartfelt song celebrating the beauty of our planet:
# **”Earth’s Melody”** (Verse 1) In the whispering forests, where leaves dance with glee, The ancient oaks and maples hum their sweet decree. Mountains rise like guardians, their peaks touching the sky, And rivers weave their stories, flowing ceaselessly by. (Pre-Chorus) Oh, Earth, your canvas painted with hues divine, From azure oceans to golden fields that shine. Each sunrise, a symphony; each sunset, a prayer, We’re but stardust travelers, breathing your rarefied air. (Chorus) **Earth, our cradle and our muse,** **Your valleys and your peaks infuse,** **A timeless song that never wanes,** **In every leaf, in every grain.** (Verse 2) Beneath the vast expanse, where constellations play, Deserts bloom with resilience, under the sun’s fierce sway. Coral reefs, kaleidoscopic, in the ocean’s tender care, Harbor secrets of Atlantis, whispered tales we share. (Pre-Chorus) Oh, Earth, your heartbeat syncs with ours, In forests’ rustling whispers and rain-kissed flowers. Snow-capped summits touch eternity’s embrace, As we spin through seasons, tracing orbits in space. (Chorus) **Earth, our cradle and our muse,** **Your valleys and your peaks infuse,** **A timeless song that never wanes,** **In every leaf, in every grain.** (Bridge) From humming bees to soaring eagles, From coral reefs to ancient sequoias, Every creature, every stone, a note in your grand score, We sing your praises, Earth, forevermore. (Chorus) **Earth, our cradle and our muse,** **Your valleys and your peaks infuse,** **A timeless song that never wanes,** **In every leaf, in every grain.** (Outro) As twilight paints the sky, and stars ignite their flame, We’ll dance upon your shores, and whisper your name. For you, dear Earth, are our home, our celestial art, A symphony of existence, etched into every heart.
Feel free to sing along to “Earth’s Melody” and celebrate the wonder that surrounds us!
can you create a set of images to illustrate today’s conversation?
That’s lovely!
I’m glad you enjoyed it! If you have any more requests or need assistance, feel free to ask. Have a wonderful day!
Gipsi Art Blog 