046 - ML For Live Tempo Matching
GPT5 “Predicted” An Image For This Post
When Instinct Meets Inference:
DJs align tempos by ear and by feel. Machine learning aligns patterns by signal features and math. Predictive BPM sits in the middle. The goal is simple: estimate tempo quickly, update it smoothly, and respect the groove that humans hear. We will attempt to combine spectral flux for onsets, autocorrelation for periodicity, and a low-latency smoother to follow tempo during performance. Please try all the code shared at your own risk, as I do not know the Python setups or notebooks you may be running.
What “Predictive” Means In a Booth:
Fast estimation: deliver a first useful BPM within a second or two.
Stable tracking: avoid jitter from noisy transients.
Human alignment: snap near musically plausible values and avoid wild swings.
Key signals: onset strength (spectral flux), tempo candidates from autocorrelation, and a temporal filter that respects inertia.
Feature Design: Onset Strength From Spectral Flux:
Spectral flux estimates how much the spectrum changes frame to frame. Peaks often line up with drum hits. This gives a clean envelope for periodicity analysis.
Periodicity: Autocorrelation For Candidate Tempos:
Autocorrelation of the onset envelope exposes repeating intervals. Convert lag to BPM and constrain to DJ-friendly ranges, for example, 70–180 BPM. Split halves or doubles to the most plausible neighborhood of your target track or mix.
Real-Time Loop: Small Windows and Gentle Smoothing:
Use a sliding window of recent audio. Recompute onset strength, autocorrelate, pick the best BPM, then blend with the previous estimate using an exponential moving average or a Kalman filter. Small windows mean lower latency but noisier estimates, so smoothing is essential.
Minimal Python Demo (Librosa, Offline and Simulated Live):
Requirements: pip install librosa soundfile numpy
Optional audio I/O for your own streams can be added later. This demo processes a file, then simulates a “live” chunked flow to show how smoothing behaves. Below, I have shared some code to try, as this helps me learn the concepts I learned in Graduate school in a much more relatable way. The code below is Python.
import numpy as np
import librosa
import soundfile as sf
# ---- Config ----
AUDIO_PATH = "your_track.wav" # replace with a local file
SR = 22050 # sample rate
FRAME_LEN = 2048 # STFT frame size
HOP_LEN = 512 # hop length
BPM_MIN, BPM_MAX = 70, 180 # plausible DJ tempo band
EMA_ALPHA = 0.25 # smoothing factor for EMA (0..1)
# ---- Load ----
y, sr = librosa.load(AUDIO_PATH, sr=SR, mono=True)
# ---- Helper: onset envelope (spectral flux) ----
def onset_envelope(y, sr, frame_len=FRAME_LEN, hop_len=HOP_LEN):
S = np.abs(librosa.stft(y, n_fft=frame_len, hop_length=hop_len))
# Spectral flux across frames
flux = np.sqrt(np.sum(np.diff(S, axis=1, prepend=S[:, :1])**2, axis=0))
# Normalize
flux = (flux - flux.min()) / (flux.max() - flux.min() + 1e-9)
return flux
# ---- Helper: BPM from autocorrelation of onset envelope ----
def estimate_bpm_from_ac(on_env, sr, hop_len, bpm_min=BPM_MIN, bpm_max=BPM_MAX):
ac = librosa.autocorrelate(on_env)
ac[:2] = 0.0 # ignore lag 0 and near-zero
# Convert BPM band to lag indices
def bpm_to_lag(bpm):
period_sec = 60.0 / bpm
return int(round(period_sec * sr / hop_len))
lag_min = max(2, bpm_to_lag(bpm_max)) # higher BPM -> smaller lag
lag_max = min(len(ac)-1, bpm_to_lag(bpm_min))
if lag_min >= lag_max:
return None
# Pick peak lag in the plausible range
lag_idx = lag_min + np.argmax(ac[lag_min:lag_max])
# Convert lag back to BPM
bpm = 60.0 * sr / (lag_idx * hop_len)
# Snap to near-integer for stability
return float(np.round(bpm, 1))
# ---- Offline estimate (full track) ----
on_env_full = onset_envelope(y, sr)
bpm_offline = estimate_bpm_from_ac(on_env_full, sr, HOP_LEN)
print("Offline BPM estimate:", bpm_offline)
# ---- Simulated live estimation ----
# Process in ~1.5s chunks and update a smoothed BPM
chunk_sec = 1.5
chunk_samples = int(chunk_sec * sr)
ema_bpm = None
bpm_series = []
for start in range(0, len(y), chunk_samples):
end = min(len(y), start + chunk_samples)
y_chunk = y[max(0, start - 2*chunk_samples):end] # small context helps
on_env = onset_envelope(y_chunk, sr)
bpm_est = estimate_bpm_from_ac(on_env, sr, HOP_LEN)
if bpm_est is None:
continue
if ema_bpm is None:
ema_bpm = bpm_est
else:
# Exponential moving average for smooth following
ema_bpm = EMA_ALPHA * bpm_est + (1 - EMA_ALPHA) * ema_bpm
bpm_series.append((end / sr, float(np.round(ema_bpm, 2))))
print("Final smoothed BPM:", bpm_series[-1][1] if bpm_series else None)
# bpm_series now contains (time_sec, smoothed_bpm) pairs for plotting or UI
Notes On Performance and Feel:
Latency vs stability: shorter windows track faster but wobble more. Tune chunk_sec, EMA_ALPHA, and the BPM band.
Double time and half time: detect peaks near 2x or 0.5x and map to the band that matches your musical intent.
Initialization: seed with a library tag or user tap to reduce convergence time.
Advanced smoother: a 1D Kalman filter with process noise tied to the rate of spectral change will track drops and fills without jumping on syncopation.
Quick Pseudocode For a Booth-Ready Follower:
Below, I have pasted a little “pseudocode” block, which I find very helpful.
state: bpm_smoothed, confidence
loop each chunk:
on_env = spectral_flux(chunk)
bpm_raw, conf_raw = autocorr_to_bpm(on_env)
if conf_raw < threshold: keep previous bpm_smoothed
else:
bpm_raw = normalize_to_band(bpm_raw, min=70, max=180, prefer_near=bpm_smoothed)
bpm_smoothed = ema(bpm_smoothed, bpm_raw, alpha)
output bpm_smoothed each chunk
Where This Plugs Into Your Rig:
Preparation: precompute a first BPM from the intro.
Performance: track with short chunks and EMA during the mix.
UX: show a confidence meter and a small drift indicator in cents per beat.
Safety: allow manual override and a tap-tempo correction that re-centers the filter.
Some Useful Links:
Librosa: http://librosa.org/doc/latest/index.html (For Python)
Numpy: http://numpy.org (For N-Dimensional Arrays)
Python: http://www.python.org (Programming Language)
I will let you know how this all goes, probably in a future post, as I plan to dump a bunch of .WAVs in the same directory as my Jupyter notebook, and test all of this out. If I make any changes, I will share them in that subsequent post. As always, you may share this post on your selected social networks by using the buttons displayed in the code block below.
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Manish Miglani | Mani
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Techno Artist. AI Innovator. Building Sustainable Futures in Music, Space, Health, and Technology.
CEO & Co-Founder: MaNiverse Inc. & Nirmal Usha Foundation
Website: http://www.manimidi.com
My YouTube Channel: http://youtube.com/@djmanimidi
Book an Appointment: https://calendly.com/manish-miglani/30min
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