#MusicGen

[andronicmusicblog]

Stable Audio 2.0: Generating Complete Musical Tracks

Music is an integral part of human expression, and with the advent of artificial intelligence, it is now possible to create music with the help of machine learning algorithms. Stable Audio 2.0 is the latest AI-powered tool that allows you to generate complete musical tracks with coherent musical structure.

Stable Audio 2.0 is a software that uses deep learning algorithms to analyze, understand, and then reproduce various musical genres. With this tool, you can create compositions that are up to 3 minutes long and contain distinct intro, development, and outro sections. The generated tracks are not only coherent but also sound like they were produced by a human composer.

The tool is user-friendly and easy to use. All you need to do is select the genre you want the music to be in, and then specify the length of the composition. You can also customize the tempo, key, and other parameters to get the desired output. Once you have set the parameters, the algorithm takes over and generates a complete musical track that you can use as a standalone composition or as a base for further refinement.

Stable Audio 2.0 is not just a tool for music production, but it is also a tool for music education. With its ability to generate different musical genres, it can help students learn more about music theory and composition. The tool can also help experienced composers break out of their creative rut and explore new genres and styles.

To get started with Stable Audio 2.0, visit their website, and sign up for a free trial. The tool is available for both Windows and Mac users. Once you have tried the tool, be sure to share your experience with us in the comments below.

In conclusion, Stable Audio 2.0 is a revolutionary tool that is changing the way we create music. It is a testament to the power of AI and its ability to augment human creativity. With this tool, anyone can create a complete musical track with ease, regardless of their musical background. So, go ahead and give it a try, and who knows, you might just create the next big hit!

[smmconsultant]

Branding can reach new heights with meta's new release of an open-source AI-powered music generator called MusicGen that can create new pieces of music from text input and can optionally be based on existing melodies.

[aralap]

[mysocial8onetech]

Do you love music and AI? Then you will love MusicGen, a new AI model by Meta Research that can generate music based on text and melody inputs. It can produce high-quality music samples with various instruments, genres, and styles, while matching the style and melody of the input. Read our blog post to learn more about this revolutionary model and how to access it. Continue Reading

[tumnikkeimatome]

オープンソースの音楽生成AI「Audiocraft (MusicGen)」のローカル環境導入と、無料で高品質な楽曲を制作する手順を解説

Meta社のAudiocraft音楽生成AIの優位性 Meta社が開発したAudiocraftは、音楽業界に革新的な変化をもたらす音楽生成AIです。 一般的な音楽生成AIと異なり、テキストプロンプトから驚くほど高品質な楽曲を生成できます。 オープンソースで提供されており、商用利用も視野に入れた開発が進められています。 MusicGenモデルの高度な音楽生成能力 MusicGenは、Audiocraftの主力モデルとして位置づけられています。 2万時間以上の音楽データを学習基盤とし、多様なジャンルの楽曲生成に対応します。 クラシック、ジャズ、ポップスなど、ジャンルを問わず高品質な楽曲を生成可能です。 AudioGenとEnCodecの補完的機能 AudioGenは環境音や効果音の生成に特化し、EnCodecは音声圧縮と復元を担当します。 この3つのモデルが連携することで、プロフェッショナル…

[jranimator]

Music Licensing Simplified – Key Insights for Artists

Music licensing serves as the backbone of the music industry, ensuring creators and rights holders are compensated for their work. As the music landscape becomes more digital, licensing plays an even more crucial role. For artists, understanding the basics of licensing can mean the difference between profiting from their creations and losing out on revenue. Whether you're an independent musician or part of a larger production company, navigating the complexities of licensing is essential for success.

The Basics of Music Licensing

Music licensing is essentially the process of granting permissions for music to be used in various contexts, such as films, advertisements, streaming services, or live performances. When someone wants to use a song, they need the rights holder’s approval, which often involves paying royalties. Licensing agreements ensure the original creator is recognized and compensated for their work.

Technological advancements like Musicgen Meta are simplifying this process, especially for independent artists. These tools not only aid in music creation but also help artists manage their rights effectively, making licensing less daunting.

Why Licensing Matters

Licensing protects intellectual property (IP), which is a crucial asset for creators. Without proper licensing, artists risk losing control over how their work is used and may not receive fair compensation. Moreover, licensing ensures that music used in commercial or public settings adheres to copyright laws.

For instance, companies like Universal Music Publishing Group have been instrumental in managing licensing for some of the biggest names in the industry. They handle everything from mechanical licenses for streaming to synchronization licenses for movies, ensuring artists are fairly compensated across the board.

Types of Music Licenses

There are several types of music licenses, each tailored to different usage scenarios. Understanding these can help artists better protect their work:

Mechanical Licenses – For reproductions of music, such as CDs or digital downloads.

Synchronization Licenses – For music used in audiovisual projects like movies or advertisements.

Public Performance Licenses – For live performances or radio airplay.

Master Licenses – For using a specific recording of a song.

Each license serves a unique purpose, and knowing which one applies to your music is critical for monetization.

Simplifying Licensing with Technology

The rise of digital tools like Musicgen Meta has been a game-changer for artists navigating licensing. These platforms not only streamline the creative process but also integrate features to manage copyrights, making it easier for artists to track where and how their music is being used.

For example, if a musician wants to distribute their work across multiple platforms or license it for a film, Musicgen Meta can provide templates and tools to facilitate these agreements. This minimizes the chances of licensing disputes and ensures transparency in revenue sharing.

Challenges in Licensing for Different Genres

Genres often come with their own set of licensing challenges. For instance, Pop Music vs. Rock Music have different licensing demands based on their usage and audience reach. Pop music, often featured in commercials and mainstream media, requires synchronization and public performance licenses in large volumes. Rock music, with its strong live performance culture, leans heavily on public performance and mechanical licenses.

These differences highlight why artists must tailor their licensing strategies to their genre. Understanding the specific requirements of their musical style can help them maximize their revenue and exposure.

Iconic Brands and Licensing

The involvement of major brands in the music industry has further complicated the licensing landscape. For example, exploring Who Owns Beats by Dre sheds light on how music and technology companies collaborate to redefine licensing. Beats by Dre, known for its high-quality headphones, also works closely with artists and producers to license music for promotional campaigns.

This intersection of music and branding demonstrates how licensing extends beyond traditional boundaries. Brands use music to create an emotional connection with their audience, and proper licensing ensures that artists benefit from these partnerships.

Music Publishing and Licensing

At the heart of music licensing lies the process of music publishing. Companies like Universal Music Publishing Group play a pivotal role in ensuring that songwriters and composers receive their due share. Music publishing involves managing the rights of a song, from copyright registration to royalty collection.

For independent artists, understanding the basics of Music Publishing can be a stepping stone toward effective licensing. Many publishing companies now offer online tools and resources, making it easier for artists to navigate this complex field.

Licensing in the Digital Age

The digital era has transformed the way music is licensed. Streaming platforms like Spotify and Apple Music rely heavily on licensing agreements to pay artists. However, this shift has also introduced challenges, such as ensuring fair royalty distribution.

Artists who embrace tools like Musicgen Meta and partner with reliable publishing groups can stay ahead in this evolving landscape. Licensing agreements in the digital age must be more comprehensive, covering everything from digital downloads to micro-licensing for social media platforms.

Practical Tips for Artists

For artists looking to simplify the licensing process, here are some practical tips:

Educate Yourself: Understand the different types of licenses and their applications.

Leverage Technology: Use platforms like Musicgen Meta to manage copyrights and licensing.

Partner Wisely: Collaborate with established publishing groups like Universal Music Publishing Group for guidance.

Know Your Genre: Tailor your licensing strategy to your genre’s specific needs, whether it’s Pop Music vs. Rock Music or another style.

Seek Professional Help: Consult with music attorneys or licensing experts for complex agreements.

Conclusion

Music licensing may seem daunting, but it is a vital aspect of an artist’s career. With the right tools and knowledge, navigating this landscape can be simplified. From leveraging AI-driven tools like Musicgen Meta to collaborating with industry leaders such as Universal Music Publishing Group, artists can protect their rights and maximize their revenue.

As the industry continues to evolve, staying informed and adaptable is key. By mastering the art of licensing, artists can ensure their work reaches wider audiences while receiving fair compensation.

What’s your take on music licensing? Share your experiences or questions in the comments below!

[thenextaitool]

Best Suno Alternatives for Music Creation in 2024

Discover creative tools that simplify music creation and spark inspiration.

The music industry is going through a significant change with the rise of AI music generators. These innovative tools allow for the creation of unique and engaging musical pieces without needing extensive technical skills.

AI as a creative partner is now a reality, reshaping how we produce and enjoy music. One notable tool in this field is Suno, which is recognized for turning user inputs into beautiful compositions, complete with catchy lyrics and melodies. However, some users have raised concerns about the similarities in the music produced, wishing for more variety and originality. If you find these issues relatable or are simply curious about other options, this blog post will introduce you to some top alternatives.

Udio

Udio allows you to create personalized music tailored to specific moments and experiences in your life.

MusicGen

MusicGen by Meta AI generates versatile high-quality music using transformers, processing text or melody, with chromagrams.

Remusic

Remusic is a revolutionary AI tool transforming music creation and enjoyment, signaling the future of AI-generated music.

Tad AI

Tad AI provides fast custom royalty-free music generation for musicians, creators, and businesses using AI-generated lyrics and prompts.

Donna AI

Donna AI transforms ideas into songs, offers royalty-free creations, community sharing, with vocal/instrument isolation.

AI music generators have transformed the way we approach music production, making it more accessible and creative. Whether you're looking to enhance your social media with engaging background music or add a touch of drama to your videos, these tools provide a variety of features to suit your needs. They make music creation more inclusive, allowing for personal expression, with some even available without a subscription, while also helping you avoid potential copyright issues.

As you consider these options, keep in mind that the key is to find a tool that aligns with your creative vision and supports your musical projects.

For more blogs like this: thenextaitool.com/blog

[rthidden]

Generative AI's New Sound Waves

Generative AI isn't just for text anymore; it's making some serious noise in the audio world.

1. Stability AI's StableAudioOpen

Text-to-Audio Magic: Converts text prompts into recordings up to 47 seconds.

Royalty-Free Assurance: Trained on royalty-free recordings, with a transparent process to ensure compliance.

Open Source with a Catch: Open source but non-commercial; commercial use requires a fee. You can fine-tune it with your custom data.

2. Eleven Labs' Sound Effects

Short and Sweet: Generates audio snippets up to 22 seconds.

Licensed Data: Uses Shutterstock's licensed data for training.

Limited Free Tier: Offers a couple of free snippets, but it's more closed-sourced compared to StableAudioOpen.

3. Existing Sound Generators

Meta's AudioCraft Library: Includes MusicGen for music and AudioGen for sound effects, both open source under MIT license.

Other Players: Google’s MusicFX and OpenAI’s Shootbox exist but aren't as popular.

Generative AI is revolutionizing audio creation. Dive in, experiment, and let us know how these tools amplify your projects!

[education30and40blog]

AI music editor developed by Sony and researchers can modify songs with text prompts

Researchers at Queen Mary University of London, Sony AI, and MBZUAI's Music X Lab have developed an AI system called Instruct-MusicGen that can modify existing music based on text prompts.

[jcmarchi]

Meta unveils five AI models for multi-modal processing, music generation, and more

New Post has been published on https://thedigitalinsider.com/meta-unveils-five-ai-models-for-multi-modal-processing-music-generation-and-more/

Meta unveils five AI models for multi-modal processing, music generation, and more

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Meta has unveiled five major new AI models and research, including multi-modal systems that can process both text and images, next-gen language models, music generation, AI speech detection, and efforts to improve diversity in AI systems.

The releases come from Meta’s Fundamental AI Research (FAIR) team which has focused on advancing AI through open research and collaboration for over a decade. As AI rapidly innovates, Meta believes working with the global community is crucial.

“By publicly sharing this research, we hope to inspire iterations and ultimately help advance AI in a responsible way,” said Meta.

Chameleon: Multi-modal text and image processing

Among the releases are key components of Meta’s ‘Chameleon’ models under a research license. Chameleon is a family of multi-modal models that can understand and generate both text and images simultaneously—unlike most large language models which are typically unimodal.

“Just as humans can process the words and images simultaneously, Chameleon can process and deliver both image and text at the same time,” explained Meta. “Chameleon can take any combination of text and images as input and also output any combination of text and images.”

Potential use cases are virtually limitless from generating creative captions to prompting new scenes with text and images.

Multi-token prediction for faster language model training

Meta has also released pretrained models for code completion that use ‘multi-token prediction’ under a non-commercial research license. Traditional language model training is inefficient by predicting just the next word. Multi-token models can predict multiple future words simultaneously to train faster.

“While [the one-word] approach is simple and scalable, it’s also inefficient. It requires several orders of magnitude more text than what children need to learn the same degree of language fluency,” said Meta.

JASCO: Enhanced text-to-music model

On the creative side, Meta’s JASCO allows generating music clips from text while affording more control by accepting inputs like chords and beats.

“While existing text-to-music models like MusicGen rely mainly on text inputs for music generation, our new model, JASCO, is capable of accepting various inputs, such as chords or beat, to improve control over generated music outputs,” explained Meta.

AudioSeal: Detecting AI-generated speech

Meta claims AudioSeal is the first audio watermarking system designed to detect AI-generated speech. It can pinpoint the specific segments generated by AI within larger audio clips up to 485x faster than previous methods.

“AudioSeal is being released under a commercial license. It’s just one of several lines of responsible research we have shared to help prevent the misuse of generative AI tools,” said Meta.

Improving text-to-image diversity

Another important release aims to improve the diversity of text-to-image models which can often exhibit geographical and cultural biases.

Meta developed automatic indicators to evaluate potential geographical disparities and conducted a large 65,000+ annotation study to understand how people globally perceive geographic representation.

“This enables more diversity and better representation in AI-generated images,” said Meta. The relevant code and annotations have been released to help improve diversity across generative models.

By publicly sharing these groundbreaking models, Meta says it hopes to foster collaboration and drive innovation within the AI community.

(Photo by Dima Solomin)

See also: NVIDIA presents latest advancements in visual AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, artificial intelligence, audioseal, chameleon, fair, jasco, meta, meta ai, models, music generation, open source, text-to-image

[ksgproject2024]

第１７回活動

2024/06/14

今日も暑いですね〜 なのに大学の冷房が効きすぎていて、体調崩しそうです... プロジェクトも折り返しなのは早いですね

今日も主にポスターとWebサイトに載せる文章と内容を考えていました！ また、マイルストーンを決めるにあたって外部発表はどこでやるか？どのくらいの仕事量なのかが徐々に明確になってきました🤗 ↓以下本日の活動です！

本日の予定

(1)今日やる事の確認(2分) (2)プロジェクトの概要(11:15まで) (3)ポスターやサイトに載せる文章(12:05まで) (4)各グループの共有や報告あれば何か(2〜5分) (5)次回以降の予定など話し合い(残り時間)

プロジェクトの概要

ポスター用

一般的に、音楽は「聴くもの」として楽しまれています。しかし、音楽にはもっと多様な可能性があると思います。そこで私たちは、音楽の表現の可能性を広げ、新しい楽しみ方を見つけようと考えました。

このプロジェクトでは、性格診断の結果を基に音楽を作り、その音楽をARアートと結びつけることで、「聴くもの」という枠を超えた新しい音楽表現を創り出すことを目指しています。私たちは、音楽と性格の融合、そして音楽とARアートの融合という二つの側面から、音楽の楽しみ方と新しい表現方法の発見に挑戦します。

↑概要では「音楽と性格の融合、そして音楽とARアートの融合という二つの側面から、音楽の楽しみ方と新しい表現方法の発見に挑戦します。」の部分をなくし、各チームの活動の詳細を書く

目標→マイルストーン　目的を達成するための具体的な活動のことで目的の欄に書かなくてもよい

テーマ

音楽の新たな表現を見つける

　〜性格を聴こう、音楽を視よう〜

・音楽は「聴くもの」という固定観念がある→音楽を聴覚以外で捉え新しい表現の仕方を見つけようと考えた 　→ここに論理の飛躍があると感じる。間に目的がある

・音楽は「聴くもの」 　→人によっては「作るもの」「使うもの」という人もいるがこの部分をどうするか

プロジェクト概要���改善案）

一般的に、音楽は「聴くもの」として楽しまれています。しかし、音楽にはもっと多様な可能性があると思います。そこで私たちは、音楽の表現の可能性を広げ、新しい楽しみ方を見つけようと考えました。

このプロジェクトでは、性格診断の結果を基に音楽を作り、その音楽をARアートと結びつけることで、「聴くもの」という枠を超えた新しい音楽表現を創り出すことを目指しています。具体的には、性格診断を元に音楽生成AIを用いて、それぞれの性格に合ったオリジナルの音楽を生成します。そして、生成された音楽をアートに変換し、ARとして体験できるようにします。

私たちは、音楽と性格の融合、そして音楽とARアートの融合という二つの側面から、音楽の楽しみ方と新しい表現方法の発見に挑戦します。

ポスター・サイトに載せる文章

サイト

・プロフィールに一言コメントと、各自が生成した性格を表した曲を載せる ・音楽に関するプロジェクトなので好きな曲も載せたい

各グループの具体案

音楽生成グループ ・性格と音楽を結びつける ・性格診断を元に、音楽生成AIを用いてオリジナルの音楽を生成する。 ・そのシステムをソフトウェアまたはサイトとして利用できるようにする。

ARアートグループ ・音楽とARアートを結びつける。 ・生成された音楽をもとに、アートを作る。 ・作成したアートを、ARとして体験できるようにする。 ・予想される成果物

音楽生成グループの成果物

　自らの性格を知るために最も確実な手段として性格診断を選択。その結果を音楽に変換するために音楽生成AIを使うことにした。 　「ビッグファイブ性格診断」という10問からなる性格診断と独自に用意した設問の結果を音楽要素に変換し、それをプロンプト(AIへの指示文)に落とし込む。 　そのプロンプトを音楽生成AI「SunoAI」に入力し、ユーザーを表現したオリジナルの音楽を生成する。 　現状はソフトウェアの完成を目標としている。Pythonを使い、GU(not洋服屋)Iのあるソフトで性格診断の情報を入力し、その情報を下にプロンプトを作成する。作成したプロンプトをSunoAIに送信し、生成された音楽を受け取って再生する機能を実装する。

現在の達成状況

音楽生成グループ

　AIの評価(Suno,Mubert,MusicGen)の3つの生成AIを比較。調査の結果から音楽生成AIとしてSunoを選択。 　性格診断は当初は独自のものを作ろうと考えていたが、より信頼性の高いビッグファイブ性格診断を使うことにした。 　本来質問数が120問あるが、より手軽に作れるようにするため10問の簡易版を使うことにした。（結果に大きな差異はない） 　論文などから性格要素と音楽要素の関連を調べ、プロンプトのver1を作成 　性格診断の結果によってプロンプトが同じようなものが生成されてしまうため、現在はプロンプトを随時更新しより良いプロンプトの模索を進めている状況 ・性格診断からプロンプトを作るという流れは完成 ・プロンプトのブラッシュアップを続ける ・APIを使用して生成AIへの接続を行えるプログラムの制作を始めたところ

予想される成果物

ARアートの成果物 ・音楽を、印象空間を用いてアートに変換する 印象空間：音楽を聴いた印象語の程度を表にプロットしたもの 　性格診断結果から、印象空間を介して、アートにプロットする要素、(季節、天気、時間帯に分解し、各要素の組み合わせをオブジェクトとしてARで投影する。 ・季節に対応する木、天気に対応する空模様、時間帯に対応する明るさ（暗さ）のアートを作成 ・性格診断結果から各印象語の程度に変換・印象空間にプロット ・各属性の印象座標と距離が近いアートを出力

1.季節に対応する木、天気に対応する空模様、時間帯に対応する明るさ（暗さ）のアートを作成 →季節、天気、時間の属性に対応するアートを作成しておく （Blenderの画面（オブジェクト）のスクショ）

2.性格診断の結果から各印象語の程度に変換・印象空間にプロット →性格診断の結果からその人に合ったイメージを散布図の座標に当てはめる （印象語が印象空間にプロットされている感じのイラスト）

3.各属性の印象座標と距離が近いアートを出力 （印象空間からアートが飛び出している感じのイラスト） 　変換されたアートをUnityでARにして、UnityライブラリのZapparでWeb上に公開する。また、カード型のマーカーを媒介として、ARを手元で体験できるようにする。←横にイラストか写真を載せる

次回の予定案

(1)今日やる事の確認(2分) (2)本日の雑学(5分)根本さん (3)代表者会議の報告 (4)ポスターなどについて、小杉先生に助言をもらう(10分) (5)各グループ作業(12:05まで) (6)各グループの共有や報告あれば何か(3分) (7)次回以降の予定など話し合い(7分) ※教員に相談に行くことがあれば変更するので、予定が前後することあり

[ai-news]

Researchers from C4DM, Queen Mary University of London, Sony AI, and Music X Lab, MBZUAI, have introduced Instruct-MusicGen to address the challenge of text-to-music editing, where textual queries are used to modify music, such as changing its style #AI #ML #Automation

[jhavelikes]

Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.

facebookresearch/audiocraft: Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.

[aiartresources]

GitHub - rsxdalv/tts-generation-webui: TTS Generation Web UI (Bark, MusicGen + AudioGen, Tortoise, RVC, Vocos, Demucs)