Benchmark of the best AI for music analysis in 2025

Best AI for music analysis: Bridge and other solutions in 2024

In an era where technology and music intertwine more intricately than ever, AI solutions for music analysis are revolutionizing the way we understand, create, and interact with music. Today, we take an objective dive into the latest advancements in this field, comparing some of the leading music analysis AIs: Bridge.audio, Musiio, Cyanite and Aims. Through a detailed benchmark spanning various genres and songs, we aim to uncover which AI stands out in this dynamic domain.

Understanding AI Music Analysis

What is AI Music Analysis?

Counter-intuitively, AI (Artificial Intelligence) for music analysis relies on images (spectrograms) to understand, categorize, and predict a song’s musical characteristics. From genre, mood, to tempo, or key, these AI tools provide valuable insights that previously depended on human expertise.

The importance of AI Music Analysis

For creators, platforms, and listeners, AI Music Analysis offers unparalleled efficiency and depth in music categorization, recommendation, and creation, opening new pathways for exploration and innovation in the music industry.

2025 Benchmark Overview

Almost a year after the initial publication of this comparative analysis, we’ve decided to test how each of these descriptive AIs have evolved by analyzing four new standout tracks from this past year:

1. “Not Like Us” by Kendrick Lamar
2. “Birds of a Feather” by Billie Eilish
3. “Good Luck, Babe!” by Chappell Roan
4. “Die With a Smile” by Bruno Mars & Lady Gaga

The AIs were still assessed based on their ability to analyze genre, mood, key, BPM, vocals and more, but this time, our analysis also compared these AI analyzers’ ability to detect the song theme and language, and their capacity to generate descriptive prompts (or pitches) for each track.

See how this new analysis compares to our 2024 benchmark detailed at the end of the article.

“Not Like Us” by Kendrick Lamar

*AI-generated pitch by Bridge.audio: “Not Like Us” delves into the gritty realities of violence and societal issues, wrapped in a predominantly Urban/Hip-Hop soundscape. The track’s electronic elements are strikingly prominent, creating an immersive atmosphere that underscores the weight of its themes. The artist’s delivery is intensely rapped, channeling a raw energy that complements the heavy subject matter. The subtle use of electronic effects enhances the song’s introspective quality, painting vivid imagery of a world fraught with tension and unrest. This sonic blend crafts a compelling narrative that resonates deeply with the listener.

**AI-generated pitch by Cyanite: Bouncy and hip hop featuring male rap vocals

Results analysis
The analyzers agreed on the overarching hip-hop genre, but their interpretations of subgenres varied, with Bridge highlighting “Urban/Hip-Hop” and Cyanite identifying “Gangsta,” which underscores differences in how tools approach cultural and stylistic nuances. Instrumentation tags showed general alignment, consistently referencing bass, drums, and synths, though Bridge and Cyanite provided more extensive detail. Mood tagging demonstrated notable divergence; while terms like “confident” and “energetic” were common, each analyzer captured distinct facets of the track’s tone. Key, BPM, and male vocals were consistently identified, but thematic and contextual depth was only explicitly noted by Bridge.

“Birds of a Feather” by Billie Eilish

*AI-generated pitch by Bridge.audio: “Birds of a Feather” by Billie Eilish gracefully intertwines alternative pop and electro-pop, creating a dreamy soundscape that feels both nostalgic and ethereal. The track’s electronic elements, notably the synth, are complemented by the subtle presence of an electric guitar, enhancing the romantic and hopeful themes of love and togetherness. Eilish’s voice, with its reverberated quality, adds a sweet and gracious layer to the song, evoking imagery of slow-motion moments shared between kindred spirits. The lyrics explore the bonds of romance and unity, painting a picture of two souls intertwined in harmony.

**AI-generated pitch by Cyanite: Electro pop song, punchy and catchy, synths layers, female voice and rhythm.

Results analysis
This track’s dreamy, seductive qualities were universally recognized, though genre classifications varied from R&B and electronic dance (Cyanite) to indie pop (Aims). Subgenre tagging was sparse across most analyzers, with only Bridge emphasizing the track’s alternative and electro-pop layers. Instrumentation tags showed significant overlap, especially in highlighting synths and percussion, though Bridge offered additional detail by mentioning reverberated and acoustic textures. Mood descriptors like “dreamy” and “seductive” were echoed across platforms, but Bridge’s inclusion of nostalgic and feminine tones provided a unique layer of interpretation. Discrepancies in the key between Bridge and others highlighted potential differences in precision or detection methodologies.

“Good Luck, Babe!” by Chappell Roan

*AI-generated pitch by Bridge.audio: Chappell Roan’s “Good Luck, Babe!” is a compelling blend of alternative pop and pop, with a notable electronic influence that adds a modern sheen to the track. The electric guitar and synths create a vibrant soundscape, enhancing the song’s exploration of love, romance, and the pangs of loneliness. The artist’s voice navigates these themes with a hopeful yet slightly melancholic tone, capturing the duality of longing and optimism. The reverberated effects amplify the emotional depth, evoking a sense of introspection and solitude, while the layered build-up provides a dynamic progression that keeps the listener engaged.

**AI-generated pitch by Cyanite: Uplifting pop rock with bright piano and female vocals, panoramic electric guitar and synths waves, positive and motivational feel

*Results analysis
All analyzers classified this track under pop, but the specificity of subgenre tagging varied, with Bridge standing out for identifying “synthpop,” while others provided less granular insights. Instrumentation descriptions were consistent in referencing drums, bass, and synths, though Cyanite and Aims lagged slightly in descriptive richness compared to Bridge. Mood tagging highlighted universal themes of positivity and energy, though Musiio leaned toward a more subdued tone with “calm” and “carefree.” BPM were in alignment for all AI analyzers, but once again, Bridge differed in its key evaluation compared to the others.

“Die With a Smile” by Bruno Mars & Lady Gaga

*AI-generated pitch by Bridge.audio: “Die With A Smile” by Lady Gaga and Bruno Mars is a captivating blend of alternative pop and pop, with a significant emphasis on electric sounds. The electric guitar and drums create a nostalgic yet romantic atmosphere, perfectly complementing the song’s themes of love, romance, dreams, and aspirations. The reverberated effects add a dreamy quality, enhancing the hopeful and aspirational emotions conveyed. The vocals are delivered with a dynamic intensity, capturing the essence of longing and desire. The track’s gradual build-up evokes a sense of slow-motion imagery, inviting listeners to immerse themselves in its emotive narrative.

**AI-generated pitch by Cyanite: Emotive, heartfelt modern pop ballad with soulful male vocals.

Results analysis
The analyzers unanimously classified this as pop but diverged slightly in mood and subgenre tagging, with Cyanite noting an R&B influence and Bridge emphasizing indie rock undertones. Instrumentation tags were broadly consistent, though Bridge’s additional mention of acoustic and reverberated textures gave a slightly more nuanced view of the track’s production. Mood descriptors like “uplifting” and “romantic” were common across all tools, reflecting a shared understanding of the track’s emotive focus. However, significant discrepancies in BPM and key suggest variability in how analyzers process tempo and harmonic information. Notably, Bridge’s inclusion of movement descriptors was a unique addition, though other tools demonstrated a balanced focus in other areas.

2024 Benchmark overview: methodology and songs

This benchmark evaluates four leading AI solutions for music analysis across five diverse songs, chosen to represent a wide range of genres and emotions:

1. “Day is Done” by Nick Drake
2. “Ne Me Quitte Pas” by Yuri Buenaventura
3. “Fado Português” by Amália Rodrigues
4. “You Broke My Heart” by Drake
5. “Dracula Theme” by Wojciech Kilar

AI tools were assessed based on their ability to accurately analyze genre, mood, key, BPM (beats per minute), tempo, and vocals, among other parameters.

Deep dive into benchmark results

Folk and emotional depth: “Day is Done” by Nick Drake

The song “Day is Done” by Nick Drake allowed these differents AIs to demonstrate their capabilities in analyzing folk music. Bridge offered a nuanced understanding of the song’s acoustic richness and emotional depth, highlighting its calm and nostalgic mood. Musiio and Cyanite identified the primary genre correctly but differed in mood and instrument detection, with Musiio leaning towards a more generic acoustic profile and Cyanite emphasizing the song’s romantic and dreamy qualities. Not only did Cyanite correctly identify the key and BPM of the song, but it also provided a broader interpretation of the genre, suggesting a mix of folk and indie influences, which indicates versatility in genre identification. Aims demonstrated a more generalized approach to mood analysis.

Latin rhythms and energy: “Ne Me Quitte Pas” by Yuri Buenaventura

In “Ne Me Quitte Pas,” Bridge and Aims accurately captured the song’s Latin essence and its energetic, romantic mood. Bridge stood out for its detailed analysis of instrumental composition, identifying a broad range of textures. Musiio and Cyanite, while recognizing the Latin genre, diverged in their mood assessments and instrument recognition, with Musiio focusing on the song’s happiness and relaxed nature, and Cyanite showcasing an ability to detect a wider array of instruments but with a more segmented view of the song’s emotional palette. This highlighted the differences in how each AI interprets the complexity of musical emotions.

The haunting melodies of “Dracula Theme” by Wojciech Kilar

For the famous “Dracula Theme,” all AI tools were challenged to analyze a piece with no vocals and a strong classical influence. Bridge accurately captured the mood of suspense and heroism, along with a precise identification of instruments typical of a movie score. Musiio and Cyanite both identified the classical genre, but with varying focuses; Musiio highlighted the dramatic tension, while Cyanite recognized the energy and seriousness of the piece. Aims, detecting the correct genre and mood, illustrated the piece’s dark and epic qualities, demonstrating its strength in analyzing film scores. It’s also interesting to look at how differently the BPM was analyzed and counted by the different AIs.

Urban beats and heartbreak: “You Broke My Heart” by Drake

In analyzing “You Broke My Heart,” Bridge provided a comprehensive overview, identifying the urban/hip-hop genre and capturing the song’s emotional depth. Moreover, Bridge was able to detect that the vocals present in the song were rapped and autotuned! Musiio focused on the power and romantic aspects, slightly narrowing its interpretation of the song’s emotional range. Cyanite and Aims both recognized the genre and BPM accurately, with Cyanite offering a detailed mood analysis that included passion and emotional depth, and Aims highlighting the song’s energetic and uplifting nature. This showed varying degrees of sensitivity to the song’s thematic content and mood among the AIs.

Traditional sounds: “Fado Português” by Amália Rodrigues

The traditional “Fado Português” presented an opportunity for the AI solutions to explore their capabilities in non-western music. Bridge analysis was remarkable for the precision with which it captured the song’s mood, genre and instrumentation, highlighting its romantic and nostalgic essence. Musiio and Cyanite offered contrasting mood interpretations; Musiio leaned towards a more romantic and sad mood, whereas Cyanite captured a broader emotional range, including sentimentality and warmth. Aims accurately identified the song’s key and BPM, recognizing the traditional and melancholic nature of Fado, which illustrates how adaptable it is to different musical traditions without exactly citing them.

The future of Music Analysis: insights and innovations

The evolution of Music Analysis AI

This benchmark reveals significant advancements in music analysis AI, with Bridge.audio showcasing a deep understanding across a diverse range of music. This evolution points towards more nuanced and accurate music discovery, creation, and recommendation. A lot of AI tools are being used for music, feel free to check them out!

Bridging the gap: the role of Bridge.audio

As demonstrated in the benchmark, Bridge.audio AI offers a comprehensive analysis that rivals human expertise. Its ability to discern intricate musical elements across genres and songs underscores its potential to revolutionize the music industry. See our music-tagging best practices here!

Bridge.audio auto-tagging API

Want to integrate Bridge AI auto-tagging technology into your own platform? Check out Bridge API, enabling you to auto-tag and enrich your catalog with precise tags for better searchability and analysis. Schedule a call with our team to find out how our API can meet your specific needs.

Conclusion: the symphony of AI and music

The benchmark of AIs for music analysis offers a glimpse into a future where technology enriches our musical experiences. With AI solutions such as Bridge.audio leading the charge, we stand on the brink of a new era in music, where discovery, creation, and appreciation are boundless.

As we continue to explore the capabilities of AI in music analysis, it’s clear that the harmony between AI and music is just beginning. This comparative study highlights the spirit of innovation prevalent in the industry, paving the way for a future where the convergence of music and technology enriches the mapping of extensive repertoires with increased accuracy. This development promises to bring significant added value to the analysis and classification of music, marking a new era where relevance and technicality come together to redefine our approach to vast musical libraries.

At Bridge.audio, we pride ourselves on the efficiency of our artificial intelligence (AI) in music analysis, achieving a remarkable accuracy rate of 80% in providing relevant suggestions. However, we recognize that the goal of absolute accuracy of 100% remains out of reach, illustrated by cases where specific nuances, like the unique voice of Nina Simone, can be misinterpreted. This observation underlines the crucial importance of human intervention in our process, allowing adjustments and corrections to refine and personalize the analysis.

We are also aware of the opportunity to improve and enrich our own AI technology for music analysis. This benchmark has allowed us to identify some discrepancies, particularly in terms of key and tempo, which we will continue to refine over time. By integrating new dimensions of analysis, such as assessing the notoriety of tracks and detecting the era of production, we aim to increase the accuracy and subtlety of our system. Bridge is dedicated to continuous innovation in the field of music analysis, seeking to provide ever more refined and tailored solutions to our users.

Interested in discovering analysis comparisons with other tracks? Send your request at hi@bridge.audio!