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AI Search Tool for Music Publishing: Best 3 Ways

AI Search Tool for Music Publishing: Best 3 Ways

In the ever-evolving landscape of sync and music publishing, leveraging advanced technology is essential for staying competitive. Cyanite offers an AI search tool for music publishing – enhancing workflows and maximizing your catalog’s potential. 

Here are three of the best ways to utilize Cyanite as a music publisher.

1. Using Cyanite’s Web App as an Internal AI Search Tool for Sync

Cyanite’s web app can serve as an AI search tool for music publishing, allowing publishers to quickly locate the right tracks for sync briefs. This streamlines the entire creative sync process:

 

    • Leverage reference tracks: Use reference tracks through Cyanite’s Similarity Search to swiftly scan your catalog for songs with similar sounds and vibes.

    • Utilize Free Text Search: Enter full briefs, scene descriptions, and other prompts (find examples here) to discover suitable music.

    • Enhance Your Pitches with Visualizations: Enrich your presentations with objective data visualizations to persuade even the most data-driven clients.

All of this not only saves time but lets anyone from your team quickly work with your entire repertoire. It also enhances the likelihood of securing sync placements, and your company’s profile to be able to find surprising, yet appropriate songs.

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With the help of Cyanite’s AI tags and the outstanding search results, we were able to find forgotten gems and give them a new life in movie productions. Without Cyanite, this might never have happened.

Miriam Rech

Sync Manager, Meisel Music

2. Enriching Your DISCO Library or Source Audio with Cyanite Tags

Integrating Cyanite’s tagging capabilities into your DISCO or Source Audio library can significantly enhance your catalog’s discoverability. By automatically tagging tracks with detailed descriptors such as mood, tempo, genre, and lyrical themes, Cyanite enriches your library with objective and consistent language. This ensures you, your team, and your clients find the right music.

This enriched tagging not only improves the user experience but also increases the chances of placements by ensuring that the right tracks are easily searchable. Furthermore, providing your team with tools that deliver meaningful insights contributes to improved employee satisfaction, making their work more efficient and enjoyable.

Read more on how to upload Cyanite tags to your DISCO and Source Audio library.

When integrating catalogs from new signings, acquisitions, or sub-publishing deals, using Cyanite ensures we have consistent & unified tagging across all of our repertoire, regardless of its origin. Both on and off DISCO.

Aaron Mendelsohn

Digital Asset Manager , Reservoir Media

3. Leveraging Music CMS with Cyanite

Cyanite seamlessly integrates with various music content management systems (CMS) such as Reprtoire, Synchtank, Cadenzabox, and Harvest Media, providing music publishers with an AI search tool within their preferred platforms. This integration streamlines catalog management and enhances search functionalities, allowing publishers to efficiently find and manage their music assets.

Cyanite also offers an API for publishers who have developed their own software solutions. This enables direct access to our powerful AI music search features, allowing for customized integration and automation tailored to specific business needs.

By leveraging these integration options, music publishers can optimize their workflows, generate data-driven insights, and respond swiftly to client demands, ultimately enhancing their overall operational efficiency.

We are committed to using AI technologies to optimize our revenues so we can speed the flow of royalties to artists and songwriters. We are delighted to be working with Cyanite to enhance our Synch services.

Gaurav Mittal

CTO, BMG

Conclusion

Cyanite’s AI search tool for music publishing & sync offers publishers powerful tools to optimize their workflows, enhance catalog discoverability, and improve sync licensing processes. By using Cyanite’s web app for internal searches, enriching DISCO and Source Audio libraries with AI-generated tags, and leveraging the CMS or API for seamless integration, publishers can stay ahead in a competitive industry.

Contact us today to learn more about our services and explore the opportunity to try Cyanite for free—no strings attached.

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AI Music Search Algorithms: Gender Bias or Balance?

AI Music Search Algorithms: Gender Bias or Balance?

This is part 1 of 2. To dive deeper into the data we analyzed, click here to check out part 2.

Gender Bias in AI Music: An Introduction

Gender Bias in AI Music Search is often overlooked. With the upcoming release of Cyanite 2.0, we aim to address this issue by evaluating gender representation in AI music algorithms, specifically comparing male and female vocal representation across both our current and updated models.

Finding music used to be straightforward: you’d search by artist name or song title. But as music catalogs have grown, professionals in the industry need smarter ways to navigate vast libraries. That’s where Cyanite’s Similarity Search comes in, offering an intuitive way to discover music using reference tracks. 

In our evaluation, we do not want to focus solely on perceived similarity but also on the potential gender bias of our algorithm. In other words, we want to ensure that our models not only meet qualitative standards but are also fair—especially when it comes to gender representation

In this article, we evaluate both our currently deployed algorithms Cyanite 1.0 and Cyanite 2.0 to see how they perform in representing artists of different genders, using a method called propensity score estimation.

Cyanite 2.0 – scheduled for Nov 1st, 2024, will cover an updated version of Cyanite’s Similarity and Free Text Search, scoring higher in blind tests measuring the similarity of recommended tracks to the reference track.

    Why Gender Bias and Representation Matters in Music AI

    In machine learning (ML), algorithmic fairness ensures automated systems aren’t biased against specific groups, such as by gender or race. For music, this means that AI music search should equally represent both male and female artists when suggesting similar tracks.

    An audio search algorithm can sometimes exhibit gender bias as an outcome of a Similarity Search. For instance, if an ML model is trained predominantly on audio tracks with male vocals, it may be more likely to suggest audio tracks that align with traditional male-dominated artistic styles and themes. This can result in the underrepresentation of female artists and their perspectives.

    The Social Context Behind Artist Representation

    Music doesn’t exist in a vacuum. Just as societal biases influence various industries, they also shape music genres and instrumentation. Certain instruments—like the flute, violin, and clarinet—are more often associated with female artists, while the guitar, drums, and trumpet tend to be dominated by male performers. These associations can extend to entire genres, like country music, where studies have shown a significant gender bias with a decline in female artist representation on radio stations over the past two decades. 

    What this means for AI Music Search models is that if they aren’t built to account for these gendered trends, they may reinforce existing gender- and other biases, skewing the representation of female artists.

    How We Measure Fairness in Similarity Search

    At Cyanite, we’ve worked to make sure our Similarity Search algorithms reflect the diversity of artists and their music. To do this, we regularly audit and update our models to ensure they represent a balanced range of artistic expressions, regardless of gender.

    But how do we measure whether our models are fair? That’s where propensity score estimation comes into play.

    What Are Propensity Scores?

    In simple terms, propensity scores measure the likelihood of a track having certain features—like specific genres or instruments—that could influence whether male or female artists are suggested by the AI. These scores help us analyze whether our models are skewed toward one gender when recommending music.

    By applying propensity scores, we can see how well Cyanite’s algorithms handle gender bias. For example, if rock music and guitar instrumentation are more likely to be associated with male artists, we want to ensure that our AI still fairly recommends tracks with female vocals in those cases.

    Bar chart comparing the average female vocal presence across two Cyanite AI models. The blue bars represent the old model (Cyanite 1.0), and the green bars represent the improved model (Cyanite 2.0). A horizontal dashed purple line at 50% indicates the target for gender parity. The x-axis displays the likelihood of female vocals in different ranges, while the y-axis shows the percentage of female presence.

    Picture 1: We aim for gender parity in each bin, meaning the percentage of tracks with female vocals should be approximately 50%. The closer we are to that horizontal purple dashed line, the better our algorithm performs in terms of gender fairness.

    Comparing Cyanite 1.0 and Cyanite 2.0

    To evaluate our algorithms, we created a baseline model that predicts the likelihood of a track featuring female vocals, relying solely on genre and instrumentation data. This gave us a reference point to compare with Cyanite 1.0 and Cyanite 2.0.

    Take a blues track featuring a piano. Our baseline model would calculate the probability of female vocals based only on these two features. However, this model struggled with fair gender representation, particularly for female artists in genres and instruments dominated by male performers. The lack of diverse gender representation in our test dataset for certain genres and instruments made it difficult for the baseline model to account for societal biases that correlate with these features.

    The Results

    The baseline model significantly underestimated the likelihood of female vocals in tracks with traditionally male-associated characteristics, like rock music or guitar instrumentation. This shows the limitations of a model that only considers genre and instrumentation, as it lacks the capacity to handle high-dimensional data, where multiple layers of musical features influence the outcome.

    In contrast, Cyanite’s algorithms utilize rich, multidimensional embeddings to make more meaningful connections between tracks, going beyond simple genre and instrumentation pairings. This allows our models to provide more nuanced and accurate predictions.

    Despite its limitations, the baseline model was useful for generating a balanced test dataset. By calculating likelihood scores, we paired male vocal tracks with female vocal tracks that had similar characteristics using a nearest-neighbour approach. This helped eliminate outliers, such as male vocal tracks without clear female counterparts and resulted in a balanced dataset of 2,503 tracks, each with both male and female vocal representations.

    When we grouped tracks into bins based on the likelihood of female vocals, our goal was a near-equal presence of female vocals across all bins, with 50% representing the ideal gender balance. We conducted this analysis for both Cyanite 1.0 and Cyanite 2.0.

    The results were clear: Cyanite 2.0 produced the fairest and most accurate representation of both male and female artists. Unlike the baseline model and Cyanite 1.0, which showed fluctuations and sharp declines in female vocal predictions, Cyanite 2.0 consistently maintained balanced gender representation across all probability ranges.

    To see more explanation on how propensity scores can help aid gender bias in AI music and balance the gender gap, check out part 2 of this article.

    Conclusion: A Step Towards Fairer Music Discovery

    Cyanite’s Similarity Search has applications beyond ensuring gender fairness. It helps professionals to:

     

    • Use reference tracks to find similar tracks in their catalogs.
    • Curate and optimize playlists based on similarity results.
    • Increase the overall discoverability of a catalog.

    Our comparative evaluation of artist gender representation highlights the importance of algorithmic fairness in music AI. With Cyanite 2.0, we’ve made significant strides in delivering a balanced representation of male and female vocals, making it a powerful tool for fair music discovery.

    However, it’s crucial to remember that societal biases—like those seen in genres and instrumentation—don’t disappear overnight. These trends influence the data that AI music search models and genAI models are trained on, and we must remain vigilant to prevent them from reinforcing existing inequalities.

    Ultimately, providing fair and unbiased recommendations isn’t just about gender—it’s about ensuring that all artists are represented equally, allowing catalog owners and music professionals to explore the full spectrum of musical talent. At Cyanite, we’re committed to refining our models to promote diversity and inclusion in music discovery. By continuously improving our algorithms and understanding the societal factors at play, we aim to create a more inclusive music industry—one that celebrates all artists equally.

    If you’re interested in using Cyanite’s AI to find similar songs or learn more about our technology, feel free to reach out via mail@cyanite.ai.

    You can also try our free web app to analyze music and experiment with similarity searches without needing any coding skills.

    AI Music Recommendation Fairness: Gender Balance

    AI Music Recommendation Fairness: Gender Balance

    Eylül

    Eylül

    Data Scientist at Cyanite

    Part 2 of 2. To get a more general overview of AI Music recommendation fairness – more specifically the topic of gender bias, click here to check out part 1.

    Diving Deeper: The Statistics of Fair Music Discovery

    While the first part of this article introduced the concept of gender fairness in music recommendation systems in an overview, this section delves into the statistical methods and models that we employ at Cyanite to evaluate and ensure AI music recommendation fairness, particularly in gender representation. This section assumes familiarity with concepts like logistic regression, propensity scores, and algorithmic bias, so let’s dive right into the technical details.

    Evaluating Fairness Using Propensity Score Estimation

    To ensure our music discovery algorithms offer fair representation across different genders, we employ propensity score estimation. This technique allows us to estimate the likelihood (or propensity) that a given track will have certain attributes, such as the genre, instrumentation, or presence of male or female vocals. Essentially, we want to understand how different features of a song may bias the recommendation system and adjust for that bias accordingly to enhance AI music recommendation fairness.

    Baseline Model Performance

    Before diving into our improved music discovery algorithms, it’s essential to establish a baseline for comparison. We created a basic logistic regression model that utilizes only genre and instrumentation to predict the probability of a track featuring female vocals. 

    A model is considered well-calibrated when its predicted probabilities (represented by the blue line) closely align with the actual outcomes (depicted by the purple dashed line in the graph below). 

    Calibration plot comparing the predicted probability to the true probability in a logistic regression model. The solid blue line represents the logistic regression performance, while the dashed purple line represents a perfectly calibrated model. The x-axis shows the predicted probability, and the y-axis shows the true probability in each bin

    Picture 1: Our analysis shows that the logistic regression model used for baseline analysis tends to underestimate the likelihood of female vocal presence within a track at higher probability values. This is evident from the model’s performance, which falls below the diagonal line in reliability diagrams. The fluctuations and non-linearity observed suggest the limitations of relying solely on genres and instrumentation to predict artist representation accurately.

    Propensity Score Calculation

    In Cyanite’s Similarity Search – one of our music discovery algorithms – we model the likelihood of female vocals in a track as a function of genre and instrumentation using logistic regression. This gives us a probability score for each track, which we refer to as the propensity score. Here’s a basic formula we use for the logistic regression model:

    Logistic regression formula used to calculate the probability that a track contains female vocals based on input features like genre and instrumentation. The equation shows the probability of the binary outcome Y being 1 (presence of female vocals) given input features X. The formula includes the intercept (β0) and coefficients (β1, β2, ..., βn) for each input feature.

    Picture 2: The output is a probability (between 0 and 1) representing the likelihood that a track will feature female vocals based on its attributes. 

    Binning Propensity Scores for Fairness Evaluation

    To assess the AI music recommendation fairness of our models by observing the correlations between the input features such as genre and instrumentation with the gender of the vocals, we analyze for each propensity the model outcome of the female artist ratio. To see the trend of continuous propensity scores into discrete variables and the average of female vocal presentation for that range, binning has been applied. 

    We then calculate the percentage of tracks within each bin that have female vocals as the outcome of our models. This allows us to visualize the actual gender representation across different probability levels and helps us evaluate how well our music discovery algorithms promote gender balance.

     

    A bar chart comparing the average female vocal presence in Cyanite's Similarity Search results across different metadata groups.

    Picture 3: We aim for gender parity in each bin, meaning the percentage of tracks with female vocals should be approximately 50%. The closer we are to that horizontal purple dashed line, the better our algorithm performs in terms of gender fairness.

    Comparative Analysis: Cyanite 1.0 vs Cyanite 2.0

    By comparing the results of Cyanite 1.0 and Cyanite 2.0 against our baseline logistic regression model, we can quantify how much fairer our updated algorithm is.

    • Cyanite 1.0 showed an average female presence of 54%, indicating a slight bias towards female vocals.

    • Cyanite 2.0, however, achieved 51% female presence across all bins, signaling a more balanced and fair representation of male and female artists.

    This difference is crucial in ensuring that no gender is disproportionately represented, especially in genres or with instruments traditionally associated with one gender over the other (e.g., guitar for males, flute for females). Our results underscore the improvements in AI music recommendation fairness.

    How Propensity Scores Help Balance the Gender Gap

    Propensity score estimation is a powerful tool that allows us to address biases in the data samples used to train our music discovery algorithms. Specifically, propensity scores help ensure that features like genre and instrumentation do not disproportionately affect the representation of male or female artists in music recommendations.

    The method works by estimating the likelihood of a track having certain features (such as instrumentation, genre, or other covariates) using and checking if those features directly influence our Similarity Search by putting our algorithms to the test. Therefore, we investigate the spurious correlation which is directly related to gender bias in our dataset, partly from the societal biases. 

    We would like to achieve a scenario where we could represent genders equally in all kinds of music. This understanding allows us to fine-tune the model’s behavior to ensure more equitable outcomes and further improve our algorithms.

    Conclusion: Gender Balance 

    In conclusion, our comparative analysis of artist gender representation in music discovery algorithms highlights the importance of music recommendation fairness in machine learning models.

    Cyanite 2.0 demonstrates a more balanced representation, as evidenced by a near-equal presence of female and male vocals across various propensity score ranges.

    If you’re interested in using Cyanite’s AI to find similar songs or learn more about our technology, feel free to reach out via mail@cyanite.ai.

    You can also try our free web app to analyze music and experiment with similarity searches without needing any coding skills.

    Music CMS Solutions Compatible with Cyanite: A Case Study

    Music CMS Solutions Compatible with Cyanite: A Case Study

    In today’s digital age, efficiently managing vast amounts of content is crucial for businesses, especially in the music industry. For those who decide not to build their own library environment, music Content Management Systems (CMS) have become indispensable tools. At Cyanite, we integrate our AI-powered analysis and search algorithms with these systems – helping you create music moments.

    In this blog post, we’ll delve into Cyanite’s compatibility with various CMS. We’ll provide an overview of the features Cyanite offers for each platform, recommend the ideal user types for each CMS, and include relevant examples

    Additionally, you’ll find information on how to use Cyanite via each of these providers.

    A Spreadsheet giving an overview of what Cyanite features are implemented into which content management system.

      Synchtank

      Synchtank provides cutting-edge SaaS solutions specifically designed to simplify and streamline asset and rights management, content monetization, and revenue processing. 

      It is trusted by some of the world’s leading music and media companies, including NFL, Peermusic, Warner Music, and Warner Bros. Discovery, to drive efficiency and boost revenue.

      Cyanite Features Available

      • Auto-Tagging
      • Auto-Descriptions
      • Similarity Search

      Recommended for

      • Music Publishers
      • Record Labels
      • Production Music Libraries
      • Broadcast Media/Entertainment Companies
      A Screenshot showing United Masters Sync's website using the CMS Synchtank

      Synchtank in United Masters Sync

      How to use Cyanite via Synchtank

      Cyanite is directly integrated into Synchtank.

      If you want to use Cyanite with Synchtank, please get in touch with a member of the Synchtank team or schedule a call with us to learn more via the button below.

      Reprtoir

      Reprtoir is a France-based CMS offering solutions for asset management, playlists, contacts, contracts, accounting, and analytics – providing supported data formats for various music platforms, distributors, music techs, and collective management organizations.

      Cyanite Features Available

      • Auto-Tagging
      • Auto-Descriptions
      • Similarity Search
      • Free Text Search
      • Visualizations

      Recommended for

      • Record Labels
      • Music Publishers
      • Production Music Libraries
      • Sync Teams
      A screen recording of Reprtoir, a music content management system. It provides a brief overview of Cyanite's integration into the platform.
      Screen Recording of Reprtoir with Cyanite

      How to use Cyanite via Reprtoir

      Cyanite is directly integrated into Reprtoir.

      If you want to use Cyanite with Reprtoir, please get in touch with a member of the Reprtoir team or schedule a call with us to learn more via the button below.

      Source Audio

      US-based Source Audio is a CMS that features built-in music distribution and offers access to broadcasters and streaming networks. Whilst offering its own AI tagging and search functions, again, specifically larger catalogs will find deeper, more accurate tagging necessary to effectively navigate their repertoire.

      Cyanite Features Available

      • Auto-Tagging
      • Auto-Descriptions

      Recommended for

      • Production Music Libraries
      • TV-Networks and Streaming Services
      A Screenshot showing the Interface of the Music CMS Source Audio

      How to use Cyanite via Sourceaudio

      Cyanite is directly integrated into Sourceaudio.

      If you want to use Cyanite inside Sourceaudio, send us an email or schedule a call below.

      Harvest Media

      Harvest Media is an Australian cloud-based music business service. They were founded in 2008 and offer catalog managing, licensing, and distribution tools based on standardized metadata and music search engines.

      Cyanite Features Available

      • Auto-Tagging
      • Auto-Descriptions
      • Similarity Search
      • Free Text Search

      Recommended for

      • Production Music Libraries
      • Music Publishers
      • Music Licensing & Subscription Services
      • Record Labels
      • TV Production, Broadcast and Entertainment Companies
      A screen recording of Human Librarian's interface, based on the CMS Harvest Media. It provides a brief overview of Cyanite's integration into the platform.

      Screen Recording of Harvest Media in Human Librarian

      How to use Cyanite via Harvest Media

      Cyanite is directly integrated into Harvest Media.

      If you want to use Cyanite inside Harvest Media, send us an email or schedule a call below.

      MusicMaster

      MusicMaster is the industry-standard software for professional music scheduling. It offers flexible rule-based planning, seamless integration with automation systems, and scalable tools for managing music programming across single stations or complex broadcast networks.

      Cyanite Features Available

      • Auto-Tagging
      • Visualizations

      Recommended for

      • Broadcast radio groups
      • FM/AM radio stations
      • Satellite radio networks
      A screen recording of Human Librarian's interface, based on the CMS Harvest Media. It provides a brief overview of Cyanite's integration into the platform.

      Screenshot of MusicMaster Scheduling Software

      How to use Cyanite via MusicMaster

      Cyanite is directly integrated into MusicMaster.

      If you want to use Cyanite inside MusicMaster, send us an email or schedule a call below.

      Cadenzabox

      Cadenzabox is one of the UK-based music Content Management Systems offering tagging, search, and licensing tools as a white-label service, enabling brand-specific designs and a deep level of customization built by Idea Junction – a full-service digital creative studio. 

      Cyanite Features Available

      • Auto-Tagging
      • Auto-Descriptions
      • Similarity Search
      • Free Text Search

      Recommended for

      • Production Music Libraries
      • Music Publishers
      A screen recording of Music Mind Co., a music library using the content management system Cadenzabox. It provides a brief overview of Cyanite's integration into the platform.

      Screen Recording of Cadenzabox in MusicMind Co.

      How to use Cyanite via Cadenza Box

      Cyanite is directly integrated into Cadenzabox.

      If you want to use Cyanite inside Cadenzabox, send us an email or schedule a call below.

      Tunebud

      UK-based Tunebud offers an easy, no-code music library website-building solution complete with extensive file delivery features, music search, playlist creation, e-commerce solutions, watermarking, and bulk downloads. It’s an all-in-one music library website solution suitable for individual composers wanting to showcase their works to music publishers and labels looking for a music sync solution for catalogs of up to 500k tracks.  

      Cyanite Features Available

      • Auto-Tagging
      • Auto-Descriptions
      • Similarity Search
      • Free Text Search

      Recommended for

      • Musicians
      • Composers
      • Music Publishers
      • Record Labels
      • Music Library and SFX Library Operators
      A Screenshot showing an example website using the CMS Tunebud
      Tunebud with Cyanite’s similarity search

      How to use Cyanite via Tunebud

      Cyanite is directly integrated into Tunebud.

      If you want to use Cyanite with Tunebud, please get in touch with a member of the TuneBud team or schedule a call with us to learn more via the button below.

      Supported CMS

      DISCO

      DISCO is an Australia-based sync pitching tool to manage, share, and receive audio files. While DISCO offers its own audio tagging version, particularly catalogs north of 10,000 songs may prefer using Cyanite’s deeper, more accurate tagging to organize and browse its catalog. 

      Cyanite Features Available

      • Auto-Tagging
      • Auto-Descriptions

      Recommended for

      • Music Publishers
      • Record Labels
      • Sync Teams
      A Screenshot of the Music CMS DISCO

      DISCO

      How to use Cyanite via DISCO

      All you need to do is reach out to your DISCO customer success manager and ask for a CSV spreadsheet of your catalog including mp3 download links. We’ll download, analyze, and tag your music, according to your requirements, and you can effortlessly upload the updated spreadsheet back to DISCO.

      You decide which tags to use, which to keep, and which to replace.

      Are you missing any music Content Management Systems? Feel free to chat with us and share your thoughts!

      Haven’t decided on a CMS yet? Contact us for free testing periods.

      Your Cyanite Team.

      The Importance of Music Auto-Tagging for Content Strategies

      The Importance of Music Auto-Tagging for Content Strategies

      An Introduction

      By Jakob Höflich, Co-Founder and CMO of Cyanite

      When I was 19, I worked at community radio 4ZZZ in Brisbane, tasked with digitizing daily CD deliveries, tagging their genre, and sorting them in the library. It was a tedious and challenging task – every mistake could persist in the library until corrected. And let’s face it, this rarely was the case. This was one of the experiences that motivated me to found Cyanite many years later, also to help catalog owners tag their catalogs with AI and to eradicate the legacy of tagging mistakes made in the past 25 years of digitization.

      While Auto-Tagging to create a clean and better searchable library has become a commodity, with various music companies worldwide leveraging this to alleviate the burden on their tagging teams and create more space for creative work, there is one underappreciated use case that has recently grown in significance: using Auto-Tagging data on a global catalog basis to derive actionable insights for your content strategy.

      From Hunches to Data-Driven Insights

      If you own or work with a music catalog, you likely have a solid understanding of its character. But what if the number of songs goes in the tens of thousands or even beyond? How confident can you be to know the profile of the catalog and what it stands for? When making important decisions about the creative direction of your catalog, especially with multiple stakeholders involved, this ‘feeling’ can tend to be subjective and leave room for guesswork. It’s the music’s subjective and “magical” nature that makes it hard to quantify and discuss.

      That’s why keywords remain crucial when managing and developing a catalog where AI can be so helpful to your work. By providing data insights, AI can turn these hunches into consistent and concrete knowledge.

      Here are three benefits of leveraging music Auto-Tagging for your content strategy.

      1. Deep Understanding of a Catalog’s Character

      AI music Auto-Tagging dives deep into the sound character of a catalog. By translating the complexity of music into concrete datapoints such as genres and moods, it allows for a shared, objective and consistent understanding across your team or company. Imagine having a precise breakdown of your catalog’s characteristics at your fingertips. Like, which percentage of my repertoire is Rock, Funk, Disco? Does it stand for upbeat or more melancholic tones? Do I maybe have a gender problem by favoring one more over the other? This not only enhances internal cohesion but also aligns strategies and decisions.

      Pro Tip: Our Auto-Tagging focuses on creative metadata, extracting information such as genres, BPM, key, and energy. Curious? Check out our full taxonomy. It does not extract copyright or performance metadata. A smart move here is to pair the AI-driven insights with other data pools. For example, pairing AI-insights with performance and sales data can reveal things like: Only 2% of my catalog is Hip Hop, yet this content has a 200% higher performance rate.

      2. Uncovering Blind Spots and Highlighting Trends

      AI’s ability to uncover blind spots and highlight trends within a catalog is another significant benefit. This data-driven approach can reveal underutilized niches or trends when data is placed on a timeline. Whether it’s identifying a resurgence in a particular genre or pinpointing areas with high sync opportunities, AI insights shed light on the hidden corners of a catalog. Particularly for sync teams of companies that do not have a distinct genre profile it is beneficial to have a balanced catalog to answer to the upmost possible amounts of briefs with adequate content.

      3. Informed Decisions for Catalog Acquisition

      Lastly, AI-driven insights are not limited to managing your existing catalog. They are invaluable when evaluating to-be-acquired catalogs. While frontline repertoire might be familiar, B-sides and deep cuts often remain mysterious territories. By thoroughly analyzing these lesser-known tracks, AI can contribute a creative due diligence aspect by providing a comprehensive understanding, which in turn informs better acquisition decisions. This ensures you’re investing in a catalog that has the ability to complement your existing one.

      Contrarily, if you want to sell a catalog, comprehensive tagging data on your repertoire can help you identify the perfect acquirer or prove the future longevity of your catalog to drive up the multiple.

      A Real-World Example

      A German publisher utilizing Cyanite’s AI insights discovered previously underappreciated genres, allowing them to optimize their catalog strategy effectively. The analysis showed that the genres Hip Hop, Funk, and RnB and the mood Epic were underrepresented even though both have been extremely valuable qualities for successful sync placements in the last years.

      Visual representations, such as pie charts and graphs, can further show how AI can dissect and categorize catalog elements, providing clear, actionable insights.

      All data above can be retrieved via our API.

      Conclusion: Embracing the Future with AI

      AI music Auto-Tagging can be a great help for developing content strategies in the music industry. These actionable insights provide a deep, data-driven understanding of catalogs, uncover blind spots, highlight trends, and inform strategic decisions for catalog acquisitions.

      Undoubtedly, AI can’t and shouldn’t replace the final decision-making process as it can’t anticipate the future as us humans do. But it can be used as a great tool to navigate this process with data that make it easier – and often more convincingly – to talk about the magic of music.

      As we live in a time where content production is at an all-time peak providing the sync market with opportunities as never before, every song in the catalog should have the same chance of being discovered. Having a well-organized and indexed catalog is key to that.

       

      How to Use AI Music Search for Your Music Catalog

      How to Use AI Music Search for Your Music Catalog

      The burgeoning field of artificial intelligence has brought forth more tools than we can count, aiming to revolutionize the music industry. Amidst this landscape, AI-based music tagging and search stand out as proven technologies with quantifiable benefits for music companies worldwide.

      This article aims to demystify the potential of AI music search and recommendation systems, offering insights to help music catalog owners and distributors determine the relevance of this technology for their businesses. Whether navigating a vast catalog or seeking innovative solutions for content discovery, this guide is tailored to your needs.

      If you are also interested in Auto-Tagging music, please check out our article on The Power of Automatic Music Tagging with AI.

      Assessing Your Needs

      Understanding the suitability of AI music search begins with assessing your specific requirements. Consider the following prompts:

       

        • Has your catalog experienced significant growth from various sources?
        • Do a variety of different users access and search through your catalog?
        • Is sync a key aspect of your company?
        • Do you find yourself repeatedly using the same tracks while underutilizing others?

      If you answer affirmatively to at least two of these questions, further exploration of AI music search is advisable.

      Exploring Options

      Two primary AI-driven search options dominate this landscape:

      Similarity Search: Ideal for discovering tracks similar to a reference song, this feature aids sync and licensing teams worldwide. Whether finding similar songs in your catalog to worldwide hits or augmenting your song collection for your sync pitch, this tool enhances any music search.

      Free Text Search (Searching by natural language): Utilizing text prompts, this search method facilitates music discovery for visual projects and instances where translating thoughts into keywords proves challenging. Its versatility extends from B2B applications, common in music licensing, to B2C scenarios, as demonstrated by Cyanite’s collaboration with streaming platforms like Sonu.

      Lyrics Search: Enhance your search experience by delving into the lyrical content of songs. This feature adds a contextual dimension to sound-based searches, allowing users to find songs based on specific words or themes within their lyrics. Whether you’re searching for songs containing a particular word like “love” or exploring themes such as “falling in love,” this functionality offers a nuanced approach to music discovery.

      If you want to test those AI music search options, you can easily create a free account for Cyanite’s web app and try it out.

      Building vs. Buying

      Choosing between building an in-house AI solution or licensing from an external provider hinges on several factors:

       

        • Do you possess an internal data science and development team?
        • Do you have a meticulously tagged library of at least 500,000 audios?
        • Can you allocate a significant budget for AI development (mid-six-figure sum) and ongoing updates?
        • Are you prepared to invest in monthly maintenance costs for a proprietary AI system (monthly five-figure sum)?

      Building your AI may be viable if you answer at least two of these questions positively. Alternatively, partnering with an AI provider like Cyanite presents a compelling option for those lacking the resources or expertise for independent development.

      To book an individual and free consultation with our experts, please follow the button below.

      Long Story Short

      AI music search offers a transformative solution for navigating the complexities of music catalogs, empowering businesses with enhanced efficiency and discovery capabilities. Whether leveraging similarity search or text-based prompts, the adoption of AI-driven technologies promises to redefine content discovery in the music industry.

      Explore the possibilities of AI music search with Cyanite, and unlock the full potential of your music catalog today.

       

      Your Cyanite Team.