Music production

Hit the right mark as AI streamlines music production and distribution

Deep learning and other AI algorithms are tailor-made for unstructured data and there is nothing less structured than digitized audio or video. What’s more, the world is inundated with it, with digital media being a major cause of the explosive growth of data created, captured and replicated. Indeed, computers are both the cause of massive amounts of new music and the solution to the problem of its management.

Since the digitization of music in the 1980s and the emergence of digital mixing software in the 1990s, computers have been essential in music production. Technology has led to an explosion in artistic production and consumption embodied in the rise of music streaming services serving more than two billion flows in the United States only every year. In addition, musicians and producers link the tracks at a frantic pace, with Spotify says it adds more than 40,000 tracks per day to its platform. Producing, organizing, conserving and promoting this prodigious production is a Herculean task that several companies are now automating using AI algorithms.

The digitization of music has fueled creative production, but facilitates computerized automation

Recommending and categorizing music based on actual sound, and not just specific room metadata, has been an area of ​​continuous technological development for two decades. The pioneers were Shazam’s audio search algorithm who identified each audio file using audio samples and the Pandora’s Music Genome project which used 400 characteristics to categorize each piece of music. In a 2006 interview, Pandora co-founder Tim Westergren said:

Essentially cover all the granular details of melody, harmony, rhythm, form, compositional qualities, and lyrics. I think of it as the primary colors, the distinct elements that make up a song]. For example, there are over 30 attributes that describe the voice alone; how much vibrato, range, ornamentation, tone, performance.

Both techniques begin with signal processing using FFT (Fast Fourier Transforms) or similar algorithms that were originally developed to process radar imagery to break down music into spectral components. Measurements of signal strength at various frequencies over time are then used to generate metadata that categorizes particular passages, instruments, pitches, and tempos. Increasing signal processing techniques with deep learning and other AI algorithms is the next step in improving the accuracy of music recommendations and predictive correlations used in music production.

CI / CD for music streaming

CI / CD (Continuous Integration / Continuous Delivery) is one of the most popular methods for DevOps teams to accelerate software development and release by automating the workflow from code submission to deployment of compiled applications. NEOS-AI provides a similar set of process automation tools for the preservation and distribution of music on hundreds of streaming platforms.

The rise of streaming services, which account for around 80% of the music industry’s total revenue, has allowed millions of titles from the old catalog and thousands of independent artists to be widely distributed and discovered by a large audience. Unfortunately for independent musicians and small publishers, the overhead costs of preparing a track or album for streaming are significant, costs thousands of dollars even for low budget outings. NEOS-AI automates the process by using machine learning (ML) to sort, organize and structure music libraries for distribution.

NEOS-AI begins by analyzing a music collection and its metadata for genre, mood and passages. From this data set, it automates the production and preservation of tracklists, song titles, and album art using five customizable components.

  • AI-Pilot: Analyzes and sorts metadata by genre, mood, and moments to create album track lists.
  • AI-Art: Designs artwork based on album title, using a library of over 50 million artist-selected images and designs.
  • AI Codes: Generate ISRC (an international standard to uniquely identify music recordings) and UPC codes using custom or default codes
  • AI-Agenda: Schedules automated tasks up to several months in advance.
  • AI-Release: Distributes albums via API to more than 300 digital streaming platforms around the world.

The whole process only takes a few seconds, a speed especially important for companies with large music catalogs looking to release custom collections for special events or projects. For example, NEOS-AI can create albums based on genre, mood, artist, tempo, or other musical characteristics and use behavioral analysis to select tracks with the greatest likelihood of commercial success. . AI-Art module can also create album art by matching music with best font, best color scheme, and best photos from its huge library.

AI and music – a young and evolving technology

NEOS-AI, a division of EMUQ Tech, is not alone in applying AI to music creation and production. For example,

  • Avia uses AI to create musical works and themes based on 11 predefined styles. His recurrent neural network is trained from a classical music database that finds patterns in selected tracks used to rank his style. The algorithm is refined by making time predictions based on different parts of a song about what’s to come. He then uses the difference between the expected and actual results to refine the rules for that style of music. It also has plagiarism detection to identify musical segments which are copies of music already present in its database. Avia software is famous for composing the haunting theme of NVIDIA GTC conferences, i am the ai, first used in 2017.
  • Music has three products that automate the classification of metadata (Tag), the creation of albums and playlists (Playlist) and the catalog search (Search). Its ranking and search engine helps identify trends that are useful to music producers looking for titles to promote. For example, by analyzing 5,200 songs from 104 weekly Spotify Viral charts in the United States, he found a pronounced decrease in the positive mood of popular songs as the year progressed. Its software can also identify the most popular genres and the correlations in popularity fluctuations between different genres.
  • Niland (acquired by Spotify in 2017) uses statistical signal processing and AI to classify tracks and identify musical correlations and similarities between different tracks and genres. Like NEOS-AI and Musiio, Niland’s software can automatically categorize songs and recommend tracks based on an individual’s preferences or listening history.
  • Synchronization tank is a music resource management system which manages copyright, multiple formats and versions, metadata, promotion and tracking of sales and usage. By consolidating multiple functions under a single user interface, Synchtank streamlines the sale and distribution of musical works and can create custom websites for the promotion of music. According to the CEO of the company, Using the synchro-tank ““AI-like techniques to help execute and manage B2B metadata based on audio waveforms, fueling advanced search capabilities for our clients. Catalogs on our platform are automatically tagged with semantic and descriptive metadata by default, making it possible to find the right track in a database of thousands or even millions of tracks in seconds.

My opinion

The application of AI to music production, catalog organization and management provides a compelling example of the power of technology to find and classify patterns in unstructured data. By extracting structure from previously insoluble data sources like digital music, video, speech, and text, deep and machine learning algorithms can identify correlations and make predictions that would be impossible or unfeasible using manual methods. This information reduces both overhead and time of managing unstructured data, for example, in streaming music production, and improves the quality of decisions.

Ultimately, AI can change the way executives make decisions, engineers develop products, and artists create works. Although software like Avia initially targeted less demanding needs like background tracks for movies, games, and live events, AI is already being used by composers and lyricists to explore and refine ideas. Indeed, an estimation that a quarter of all hit songs will be written in whole or in part using AI. Likewise, most business leaders will rely on AI-powered analytics and forecasting to make strategic decisions. Much like robots in factories, AI is a powerful tool for improving the quality and speed of intellectual and creative tasks.


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