Real-time beats per minute analysis using multiple algorithms
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+ Live Waveform
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+ Click "Start Listening" to begin analyzing audio from your microphone
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Combined Estimate
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BPM
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+ Confidence
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Peak Detection
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BPM
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+ Confidence
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Autocorrelation
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BPM
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+ Confidence
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Spectral Flux
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BPM
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+ Confidence
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+ For best results, play music with a clear, steady beat. Detection accuracy improves after 5-10 seconds of listening.
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How It Works
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Combined Estimate (Primary)
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+ The combined estimate uses a weighted average of all three detection methods, with weights
+ dynamically adjusted based on each algorithm's confidence level. This ensemble approach
+ provides more robust results than any single method alone.
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+ When algorithms agree closely (within 5 BPM), confidence is high. When they disagree
+ significantly, the system weights more reliable methods higher and reduces overall confidence.
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+ This method analyzes the audio signal's energy envelope to find "peaks" — moments where
+ the sound intensity suddenly increases, typically corresponding to drum hits or strong
+ musical accents.
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Calculates the RMS (Root Mean Square) energy of sliding windows
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Applies a low-pass filter to create a smooth energy envelope
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Detects peaks that exceed a dynamic threshold based on recent audio levels
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Measures time intervals between consecutive peaks
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Converts the median interval to BPM: BPM = 60 / interval
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+ Best for: Music with prominent drum patterns and clear transients.
+ Works well with rock, pop, electronic, and hip-hop genres.
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Autocorrelation
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+ Autocorrelation finds repeating patterns in the audio by comparing the signal with
+ time-shifted versions of itself. When the signal correlates strongly with a delayed
+ copy, that delay corresponds to the beat period.
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Computes the audio's energy envelope to reduce noise
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Calculates R(τ) = Σ x(t) × x(t + τ) for various time lags τ
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Searches for peaks in the correlation function within the 40-200 BPM range
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The lag with the highest correlation indicates the beat period
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Converts lag to BPM: BPM = 60 × sampleRate / lagSamples
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+ Best for: Music with consistent, repetitive rhythmic patterns.
+ Particularly effective for electronic music, dance tracks, and songs with steady tempos.
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Spectral Flux
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+ Spectral flux measures how the frequency content of the audio changes over time.
+ Musical beats typically cause sudden changes in the spectrum, especially in the
+ low-frequency (bass) range.
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Performs FFT (Fast Fourier Transform) to get frequency spectrum
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Calculates the difference between consecutive spectral frames
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Uses half-wave rectification to emphasize increases in energy
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Applies onset detection to find beat positions
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Analyzes inter-onset intervals to determine tempo
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+ Best for: Complex music with layered instrumentation.
+ Handles tempo variations better than peak detection and works well with
+ jazz, classical, and progressive genres.
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Technical Considerations
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+ BPM detection from live audio is challenging due to several factors:
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Octave ambiguity: Algorithms may detect half or double the actual tempo (55 vs 110 vs 220 BPM)
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Background noise: Environmental sounds can interfere with beat detection
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Complex rhythms: Syncopation and off-beat patterns can confuse peak detection
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Tempo changes: Songs that speed up or slow down require adaptive algorithms
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Audio quality: Microphone frequency response and room acoustics affect accuracy
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+ This tool attempts to mitigate these issues by using multiple algorithms and
+ continuously refining estimates as more audio is analyzed. The confidence indicators
+ help you understand how reliable each measurement is.
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