{"id":2564,"date":"2024-05-15T09:11:22","date_gmt":"2024-05-15T07:11:22","guid":{"rendered":"https:\/\/phonicscore.com\/a-2024-overview-of-audio-related-ai-tasks\/"},"modified":"2024-06-20T13:37:12","modified_gmt":"2024-06-20T11:37:12","slug":"a-2024-overview-of-audio-related-ai-tasks","status":"publish","type":"post","link":"https:\/\/phonicscore.com\/de\/a-2024-overview-of-audio-related-ai-tasks\/","title":{"rendered":"Wof\u00fcr Sie im Jahr 2024 KI im Musik- und Audiobereich einsetzen k\u00f6nnen"},"content":{"rendered":"\n

Um Ideen f\u00fcr unser n\u00e4chstes innovatives Tool im Musikbereich zu sammeln, haben wir den State-of-the-Art der Forschung zum Einsatz von KI im Bereich Musik und Audio recherchiert.<\/p>\n\n

Im Vergleich zu NLP oder Computer Vision scheint die Integration von KI und ML in der Musik- und Audiobearbeitung weniger fortgeschritten zu sein. Nichtsdestotrotz deuten die j\u00fcngsten Entwicklungen auf ein gro\u00dfes Interesse insbesondere an generativer KI hin. Dies l\u00e4sst sich an mehreren erfolgreichen Markteinf\u00fchrungen in der letzten Zeit beobachten, wie z.B. stable audio<\/a>, soundry AI<\/a>, udio<\/a> oder suno<\/a>. Es gibt jedoch noch viele weitere Anwendungen von KI in diesem Bereich. In diesem Blogbeitrag haben wir eine Liste von Anwendungsbereichen zusammengestellt, wo KI bei der Audioverarbeitung eingesetzt werden k\u00f6nnte oder bereits eingesetzt wird.<\/p>\n\n

Einige der folgenden KI-Anwendungsf\u00e4lle sind bereits vergleichsweise bekannt oder sogar bereits in der Wahrnehmung von der breiten \u00d6ffentlichkeit angekommen. Einige davon k\u00f6nnten f\u00fcr Sie neu sein. Wir haben auch nach verf\u00fcgbarer Software gesucht, die die entsprechenden Aufgaben implementiert, um zu zeigen, welche Bereiche bereits ausgereift sind. Nat\u00fcrlich ist es in vielen F\u00e4llen, besonders bei propriet\u00e4rer Software, nicht klar, ob diese Softwareanwendungen intern KI, manuelle Algorithmen oder eine Mischung aus beidem verwenden.<\/p>\n\n

Hier ist unsere Liste der KI-Anwendungsbereiche, die mit Musik oder Audio zu tun haben:<\/p>\n\n

    \n
  1. Automatische Musik-Transkription \/ Erkennung:<\/strong> Bei dieser Technologie wird ein Musik-Audiosignal in eine entsprechende symbolische Notation, z.B. MIDI, umgewandelt. Sie verwendet Algorithmen, um Tonh\u00f6hen, Rhythmen und manchmal sogar Instrumentarten aus Musikaufnahmen zu identifizieren. Die automatische Musiktranskription ist f\u00fcr verschiedene Anwendungen wie Musikunterricht, Datenbankablage und das Finden von Musik von entscheidender Bedeutung.Vorhandene Software:<\/strong> Klangio AI<\/a>, AnthemScore<\/a><\/li>\n\n\n\n
  2. Sheet Music Difficulty Estimation:<\/strong> Bei diesem Thema geht es um die Entwicklung von Methoden zur Einsch\u00e4tzung des Schwierigkeitsgrads von Notenbl\u00e4ttern. Algorithmen analysieren den musikalischen Inhalt, wie z.B. die Notendichte, die rhythmische Komplexit\u00e4t und die technischen Anforderungen, um P\u00e4dagogen und Musikern ein objektives Ma\u00df f\u00fcr die Schwierigkeit eines St\u00fccks zu geben. Dies hilft bei der Auswahl der richtigen Musikst\u00fccke je nach Kenntnisstand.<\/li>\n\n\n\n
  3. Music Structure Analysis \/ Formal Analysis<\/strong>: Die Musikstrukturanalyse untersucht den Aufbau eines Musikst\u00fccks, um seine Form und Struktur zu verstehen und abzubilden. Dazu geh\u00f6rt das Erkennen von wiederholten Abschnitten, Variationen und der Gesamtanordnung der Themen. Es ist f\u00fcr Musikwissenschaftler, Komponisten und Interpreten unerl\u00e4sslich, um Kompositionen zu interpretieren und Auff\u00fchrungsstrategien zu verbessern.<\/li>\n\n\n\n
  4. Optische Notenerkennung (OMR)<\/strong>: Scannt gedruckte oder handgeschriebene Noten und konvertiert sie in bearbeitbare oder abspielbare Formate wie MIDI oder MusicXML. Dies erleichtert die digitale Aufbewahrung von Partituren, eine einfachere Transposition und die Integration in unterschiedliche Softwareanwendungen im Musikbereich.Vorhandene Software:<\/strong> z.B. Maestria<\/a>, Soundslice<\/a><\/li>\n\n\n\n
  5. (Symbolische) Musikgenerierung \/ Text-to-Music<\/strong>: Bei der symbolischen Musikgenerierung wird neue Musik in symbolischer Form (wie MIDI) mithilfe von Algorithmen erstellt, die oft auf bestimmten Regeln oder erlernten Musikstilen basieren. Text-to-music erweitert dies, indem es einen beschreibenden Text in Musik umwandelt, die die Stimmung, das Tempo und die Instrumentierung widerspiegelt, die der Text vorgibt.Vorhandene Software:<\/strong> AIVA<\/a>, MuseNet<\/a><\/li>\n\n\n\n
  6. Erzeugung von Chorgesang<\/strong>: Konzentriert sich auf die Synthese von realistischen Chorkl\u00e4ngen mit Hilfe digitaler Audiotechnologie. Es kombiniert Elemente der Stimmsynthese und der Akustikmodellierung und erzeugt reichhaltige, mehrschichtige Choreffekte aus einzelnen oder mehreren Vokalinputs.<\/li>\n\n\n\n
  7. Performance Analysis<\/strong>: Die Analyse musikalischer Darbietungen beinhaltet das Analysieren von Audioaufnahmen oder Live-Auftritten, um Timing, Dynamik und Ausdruck zu bewerten. Dies hilft beim Verst\u00e4ndnis der Leistungspraxis. Im Bildungsbereich verwenden Entwickler sie, um musikalische Trainings- und Feedbacksysteme zu verbessern.Vorhandene Software:<\/strong> Practice Birds IPM<\/a>, Yousician<\/a><\/li>\n\n\n\n
  8. Music Source Separation<\/strong>: Bei diesem Prozess werden einzelne Kl\u00e4nge oder Instrumente aus einer gemischten Audiospur extrahiert. Sie ist von grundlegender Bedeutung bei der Musikbearbeitung, beim Remixen und auch im Bildungsbereich, wo Sch\u00fcler mit einzelnen Komponenten einer Komposition \u00fcben k\u00f6nnen.Vorhandene Software:<\/strong> iZotope RX<\/a>, lalal.ai<\/a><\/li>\n\n\n\n
  9. Speaker Diarization<\/strong> erm\u00f6glicht es, einen Dialog in Segmente zu unterteilen, je nachdem, wann die einzelnen Sprecher sprechen. Es hat wesentliche Anwendungen in der automatischen Transkription von Dialogen.Vorhandene Software:<\/strong> z.B. Google Cloud Speech-to-Text<\/a><\/li>\n\n\n\n
  10. Audio-Klassifizierung \/ Musik-Klassifizierung \/ Auto-Tagging von Musik \/ Erkennung von Musikgenres<\/strong>: Diese Prozesse umfassen die Analyse von Audiotracks, um sie nach Genre, Stimmung, Instrumentierung oder anderen Metadaten zu kategorisieren. Dies ist entscheidend f\u00fcr Musikempfehlungssysteme, digitale Bibliotheken und f\u00fcr die Organisation gro\u00dfer Musiksammlungen.<\/li>\n\n\n\n
  11. Mechanical Fault Diagnosis Based on Audio Signal Analysis (MFDA<\/strong> ) beinhaltet die Erkennung mechanischer Fehler in Ger\u00e4ten durch die Analyse von Audiosignalen. Indem sie ungew\u00f6hnliche Ger\u00e4usche oder Vibrationen erkennt, hilft sie bei der vorbeugenden Wartung und Fehlererkennung ohne Demontage. Daher ist diese Aufgabe ein typisches Problem bei der Erkennung von Anomalien, das mit Hilfe von Autoencoder-basierten Architekturen angegangen werden kann.<\/li>\n\n\n\n
  12. Content-based Retrieval<\/strong>: Diese Technik erm\u00f6glicht es Benutzern, Multimedia-Inhalte (wie Audio oder Video) anhand des Inhalts selbst und nicht anhand von Metadaten zu finden. Sie nutzt Merkmale, die aus dem Inhalt extrahiert wurden, wie Tempo, Melodie oder Harmonie in der Musik, um die Suche zu erm\u00f6glichen.Vorhandene Software:<\/strong> SoundHound<\/a><\/li>\n\n\n\n
  13. Voice Cloning:<\/strong> Mit der Technologie des Stimmenklonens werden digitale Nachbildungen der Stimme einer Person anhand von Audio-Samples erstellt. AI Engineers k\u00f6nnen diese Technik f\u00fcr personalisierte virtuelle Assistenten, barrierefreie Anwendungen und Unterhaltung nutzen.Vorhandene Software:<\/strong> Respeecher<\/a>, Descript<\/a><\/li>\n\n\n\n
  14. Audio DeepFake-Erkennung:<\/strong> Ziel ist die Erkennung des Stimmenklonens, d.h. die Identifizierung und Entsch\u00e4rfung von k\u00fcnstlich erzeugten oder manipulierten Audioclips, die echte Aufnahmen imitieren sollen. Sie ist entscheidend, um Fehlinformation zu bek\u00e4mpfen und die Authentizit\u00e4t der Kommunikation sicherzustellen.Vorhandene Software: <\/strong>Pindrop<\/a><\/li>\n\n\n\n
  15. Audio-Inpainting:<\/strong> \u00c4hnlich des Wiederherstellens fehlender Teile eines Bildes werden beim Audio Inpainting fehlende oder besch\u00e4digte Teile eines Audiosignals wiederhergestellt oder rekonstruiert. Es erm\u00f6glicht daher, das H\u00f6rerlebnis zu verbessern oder bei Projekten zur Audiorestauration zu helfen.Vorhandene Software:<\/strong> Udio<\/a><\/li>\n\n\n\n
  16. Audio Super Resolution<\/strong> verbessert die Aufl\u00f6sung eines Audiosignals. Sie verbessert somit die Klarheit und Detailgenauigkeit von Aufnahmen niedriger Qualit\u00e4t auf h\u00f6here Aufl\u00f6sungen.<\/li>\n\n\n\n
  17. Audio-Denoising \/ Audio-Declipping:<\/strong> Diese Techniken werden verwendet, um Audioaufnahmen zu bereinigen, indem Rauschen entfernt und verzerrte Kl\u00e4nge repariert werden. Sie sind f\u00fcr die Nachbearbeitung von Musik und Film sowie f\u00fcr die forensische Audioanalyse von entscheidender Bedeutung.<\/li>\n\n\n\n
  18. Bei der polyphonen Audiobearbeitung<\/strong> werden Audiodaten bearbeitet, die mehrere T\u00f6ne oder Stimmen gleichzeitig enthalten. Dies erm\u00f6glicht komplexe Anpassungen und Manipulationen, die das gesamte Audioerlebnis verbessern k\u00f6nnen.Vorhandene Software:<\/strong> Melodyne Studio<\/a><\/li>\n\n\n\n
  19. Sprachsynthese & Speech-to-Text:<\/strong> Sprachsynthese, auch bekannt als Text-to-Speech (TTS), wandelt Text in gesprochene Sprachausgabe um, w\u00e4hrend Speech-to-Text den umgekehrten Weg geht und gesprochene Sprache in geschriebenen Text transkribiert. Diese Technologien sind von grundlegender Bedeutung f\u00fcr Hilfsmittel zur Barrierefreiheit, virtuelle Assistenten und automatische Transkriptionsdienste.Vorhandene Software:<\/strong> z.B. Google Text-to-Speech<\/a><\/li>\n\n\n\n
  20. Die Sprachidentifikation<\/strong> identifiziert die in einem Audioclip gesprochene Sprache. Diese Aufgabe ist f\u00fcr mehrsprachige Anwendungen, automatische \u00dcbersetzungsdienste und globale Kommunikationsplattformen unerl\u00e4sslich.<\/li>\n\n\n\n
  21. Emotion Recognition:<\/strong> In diesem Bereich geht es um die Analyse von Stimmausdr\u00fccken, um emotionale Zust\u00e4nde zu erkennen. Sie wird im Kundenservice, bei Sicherheitssystemen und in der Gesundheitsdiagnostik eingesetzt, um menschliche Emotionen zu bewerten und effektiv darauf zu reagieren.Vorhandene Software:<\/strong> Beyond Verbal<\/li>\n\n\n\n
  22. Sound Event Localization and Detection<\/strong> lokalisiert und identifiziert bestimmte Ger\u00e4usche in einer Audioumgebung. Sie sind n\u00fctzlich f\u00fcr die \u00dcberwachung, die Beobachtung von Wildtieren und f\u00fcr Smart-Home-Systeme.Vorhandene Software:<\/strong> Audio Analytic<\/a><\/li>\n<\/ol>\n\n

    Auf der technologischen Seite dominiert, \u00e4hnlich wie in anderen Bereichen des maschinellen Lernens, das Deep Learning eindeutig diese Bereiche. Die architekturellen Prinzipien der eingesetzten Modelle \u00e4hneln denen, die aus der Computer Vision bekannt sind. VAEs, Transfomer und Diffusions- und Flow-Matching-Modelle werden insbesondere f\u00fcr generative Aufgaben verwendet. Im Gegensatz zu Computer-Vision-Aufgaben verwenden wir im Audiobereich jedoch in der Regel Spektrogrammdarstellungen als Eingaben. Bei Bedarf wird oft eine Phasenrekonstruktion beim Postprocessing genutzt, um ein realistisches Audiosignal zu erzeugen.<\/p>\n\n

    Die oben genannten Anwendungsf\u00e4lle unterscheiden sich betr\u00e4chtlich hinsichtlich der Menge an Forschungs- und Entwicklungsarbeit, die bereits in sie investiert wurde. So haben zum Beispiel Forscher von Spotify und Magenta (Google AI) k\u00fcrzlich leistungsstarke Modelle im Bereich AMT entwickelt, um Audioaufnahmen f\u00fcr verschiedenste Instrumente zuverl\u00e4ssig zu transkribieren. Auch in den Bereichen TTS und STT hat es in letzter Zeit bedeutende Fortschritte gegeben. Die Synthese von nat\u00fcrlich klingender Stimme und die zuverl\u00e4ssige Transkription von Sprachaufnahmen (z.B. mit OpenAIs Whisper) werden dadurch m\u00f6glich.<\/p>\n\n

    Wie sieht die Zukunft im Bereich Musik und Audio aus? Einerseits sehen wir, dass die obige Liste eine ganze Reihe von Aufgaben enth\u00e4lt, die generative Techniken verwenden. Generative Musik k\u00f6nnte zu einem wichtigen Anwendungsbereich in Branchen wie der Filmindustrie werden. Wir sehen au\u00dferdem eine Konvergenz verschiedener Arten von Modalit\u00e4ten. Das neueste Modell von OpenAI, GPT-4o, akzeptiert zum Beispiel textuelle, visuelle und akustische Eingaben. Der zunehmende Einsatz von Foundation Models in verschiedenen Bereichen des Deep Learning deutet darauf hin, dass Transfer Learning eine entscheidende Rolle bei der Verbesserung von Prediction Tasks in der Audiotechnik spielen k\u00f6nnte. Selbst bei weniger popul\u00e4ren Aufgaben ist es daher wahrscheinlich, dass wir die Modellgenauigkeit verbessern und gleichzeitig den Bedarf an Trainingsdaten minimieren k\u00f6nnen.<\/p>\n","protected":false},"excerpt":{"rendered":"

    Um Ideen f\u00fcr unser n\u00e4chstes innovatives Tool im Musikbereich zu sammeln, haben wir den State-of-the-Art der Forschung zum Einsatz von KI im Bereich Musik und Audio recherchiert. Im Vergleich zu NLP oder Computer Vision scheint die Integration von KI und ML in der Musik- und Audiobearbeitung weniger fortgeschritten zu sein. Nichtsdestotrotz deuten die j\u00fcngsten Entwicklungen […]<\/p>\n","protected":false},"author":507,"featured_media":2316,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[13],"tags":[],"class_list":["post-2564","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-unkategorisiert"],"yoast_head":"\nWof\u00fcr Sie im Jahr 2024 KI im Musik- und Audiobereich einsetzen k\u00f6nnen - PhonicScore<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/phonicscore.com\/de\/a-2024-overview-of-audio-related-ai-tasks\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Wof\u00fcr Sie im Jahr 2024 KI im Musik- und Audiobereich einsetzen k\u00f6nnen - PhonicScore\" \/>\n<meta property=\"og:description\" content=\"Um Ideen f\u00fcr unser n\u00e4chstes innovatives Tool im Musikbereich zu sammeln, haben wir den State-of-the-Art der Forschung zum Einsatz von KI im Bereich Musik und Audio recherchiert. Im Vergleich zu NLP oder Computer Vision scheint die Integration von KI und ML in der Musik- und Audiobearbeitung weniger fortgeschritten zu sein. 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Hartl","author_link":"https:\/\/phonicscore.com\/de\/author\/ahartl\/"},"uagb_comment_info":0,"uagb_excerpt":"Um Ideen f\u00fcr unser n\u00e4chstes innovatives Tool im Musikbereich zu sammeln, haben wir den State-of-the-Art der Forschung zum Einsatz von KI im Bereich Musik und Audio recherchiert. Im Vergleich zu NLP oder Computer Vision scheint die Integration von KI und ML in der Musik- und Audiobearbeitung weniger fortgeschritten zu sein. Nichtsdestotrotz deuten die j\u00fcngsten Entwicklungen…","_links":{"self":[{"href":"https:\/\/phonicscore.com\/de\/wp-json\/wp\/v2\/posts\/2564","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/phonicscore.com\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/phonicscore.com\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/phonicscore.com\/de\/wp-json\/wp\/v2\/users\/507"}],"replies":[{"embeddable":true,"href":"https:\/\/phonicscore.com\/de\/wp-json\/wp\/v2\/comments?post=2564"}],"version-history":[{"count":5,"href":"https:\/\/phonicscore.com\/de\/wp-json\/wp\/v2\/posts\/2564\/revisions"}],"predecessor-version":[{"id":2572,"href":"https:\/\/phonicscore.com\/de\/wp-json\/wp\/v2\/posts\/2564\/revisions\/2572"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/phonicscore.com\/de\/wp-json\/wp\/v2\/media\/2316"}],"wp:attachment":[{"href":"https:\/\/phonicscore.com\/de\/wp-json\/wp\/v2\/media?parent=2564"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/phonicscore.com\/de\/wp-json\/wp\/v2\/categories?post=2564"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/phonicscore.com\/de\/wp-json\/wp\/v2\/tags?post=2564"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}