# Spotify Audio Analysis: CNNs and the 13 Audio Features |…

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Description: Spotify CNNs extract 13 audio features from raw waveforms to power Radio, Autoplay, and cold-start recommendations. How each feature influences placements.

Trigger the Spotify Algorithm with Dynamoi Start Now Dynamoi Learn Spotify Audio Analysis: CNNs and the 13 Audio Features Spotify uses convolutional neural networks to extract 13 audio features from raw waveforms. These power Radio, Autoplay, and cold-start recommendations for new releases. How-to Guide Jun 3, 2026 Reading time 6 min read Spotify uses convolutional neural networks (CNNs) to analyze spectrograms of raw audio waveforms and extract 13 measurable features per track, including tempo, energy, valence, danceability, and key. These features power Radio and Autoplay by finding sonically compatible neighbors, and they solve the cold start problem for new artists with no listening history. For Discover Weekly, audio analysis acts as a tiebreaker when candidate tracks share similar collaborative filtering scores. How audio analysis works at Spotify When a track is uploaded to Spotify through a distributor, it goes through an automated audio analysis pipeline. The system processes the raw waveform and extracts dozens of measurable characteristics. The core technology is convolutional neural networks (CNNs) , the same type of machine learning models used for image recognition. Instead of analyzing pixels, Spotify's CNNs analyze spectrograms , which are visual representations of sound frequencies over time. The CNN learns to detect patterns in these spectrograms: strong drum beats and synthesizers suggest electronic or dance music; mellow acoustic guitar patterns indicate folk or singer-songwriter genres; complex harmonic structures might signal jazz or classical. The audio features Spotify extracts Spotify's API exposes 13 audio features for every track. These are the building blocks the algorithm uses to measure sonic similarity. Rhythm and tempo features Feature Definition Range tempo Estimated beats per minute (BPM) 0-250 time_signature Beats per measure (3/4, 4/4, etc.) 1-7 danceability How suitable for dancing based on tempo, rhythm stability, beat strength 0.0-1.0 Danceability is not just tempo. A 120 BPM track with irregular rhythms scores lower than a 100 BPM track with a steady groove. Energy and intensity features Feature Definition Range energy Perceptual measure of intensity and activity 0.0-1.0 loudness Overall loudness in decibels (dB) -60 to 0 dB Energy combines multiple signals: dynamic range, perceived loudness, timbre, onset rate (how often new sounds start), and overall entropy. Death metal scores high; a Bach prelude scores low. Tonal features Feature Definition Range key The tonal center of the track 0-11 (C=0, C#=1, etc.) mode Major (1) or minor (0) 0 or 1 These features help the algorithm group tracks with compatible harmonic structures, so Radio and Autoplay can move between them without a jarring key change. Mood and character features Feature Definition Range valence Musical positiveness (happy vs sad) 0.0-1.0 acousticness Confidence that the track is acoustic 0.0-1.0 instrumentalness Predicts if the track has no vocals 0.0-1.0 speechiness Presence of spoken words 0.0-1.0 liveness Probability the track was performed live 0.0-1.0 Valence is particularly important for mood-based recommendations. A high-valence track (0.8+) sounds cheerful or euphoric. A low-valence track (0.2 or below) sounds sad, melancholic, or angry. How audio features influence recommendations Audio analysis solves the cold start problem . When a new artist uploads their first track, they have no listening history or collaborative filtering data. But the audio features are available immediately. Here is how each algorithmic surface uses audio analysis: Radio and Autoplay When Radio generates a queue based on a seed track, audio similarity is the primary signal. The algorithm finds tracks with similar: Tempo (within a reasonable range for smooth transitions) Energy level (to maintain the session's intensity) Key and mode (for harmonic compatibility) Valence (to preserve the emotional tone) This is why a Radio station seeded from a high-energy electronic track will not suddenly insert a slow acoustic ballad, even if both songs share genre tags. Discover Weekly Discover Weekly primarily uses collaborative filtering , but audio analysis acts as a tiebreaker. When multiple candidate tracks have similar listening overlap scores, the algorithm favors those with audio features closest to your existing taste profile. What artists can learn from audio features You cannot control how Spotify analyzes your audio. But knowing these features tells you how the algorithm hears your music, and which tracks it will group yours with. Checking your track's audio features Tip Third-party tools can pull your track's audio features from Spotify's API. Look for services that let you enter a Spotify track URL and return the feature values. What to look for: Consistent features across your catalog help the algorithm cluster your music. If your tracks vary wildly in energy, tempo, and valence, the algorithm has a harder time predicting who will enjoy them. Features that match your target audience improve Radio placement. If your sound is high-energy and danceable, your tracks are more likely to appear in workout and party-oriented Radio sessions. The intro problem Audio analysis examines the full track, but listener behavior is heavily influenced by the first 30 seconds. If your intro has different characteristics than the rest of the song (a quiet ambient intro before a loud drop), the audio features may not reflect what listeners experience first. This can create a mismatch: the algorithm recommends your track based on overall energy, but listeners skip because the intro does not match their expectations. Optimizing your intro is a separate skill from optimizing your overall audio profile. Limitations of audio analysis Audio analysis is powerful, but it has blind spots: Cultural context is missing. The algorithm knows your track has high energy and a 128 BPM tempo, but it does not know that the lyrics reference a specific cultural moment or that the production style evokes a particular era. Similar sounds are not the same as similar audiences. Two tracks can have nearly identical audio features but appeal to completely different listeners. Audio analysis finds sonic neighbors, not audience neighbors. Genre is inferred, not declared. Spotify uses your distributor-provided genre tags, but audio analysis can override them if the sonic characteristics do not match. A track tagged as "hip-hop" that sounds like acoustic folk may get recommended to folk listeners instead. The role of audio in the broader algorithm Audio analysis is one of three main data sources the Spotify algorithm uses: Data source What it captures Best for Collaborative filtering Listening patterns across users Finding audience overlap Natural language processing Lyrics, playlist titles, web mentions Understanding cultural context Audio analysis Sonic characteristics of the waveform Finding sonically similar tracks For established artists, collaborative filtering dominates. For new artists, audio analysis carries more weight because there is no listening history to analyze. The goal is to release music with clear, consistent audio characteristics while building an engaged listener base. Audio analysis gets you discovered; engagement signals determine whether you keep getting recommended. Part of How the Spotify Algorithm Works [2026] → Related learning FAQ Spotify BaRT Algorithm: How It Powers Your Home Feed FAQ Spotify Collaborative Filtering: How It Works [2026] Complete Guide How the Spotify Algorithm Works [2026] How-to Guide Spotify Algorithm Optimization Guide [Step-by-Step] See pricing →