Mount four MEMS mics on the roof truss, 1 m behind each goal line; calibrate to 94 dB, 1 kHz. Set a 1 ms sliding window with 50 % overlap; trigger storage when RMS exceeds 105 dB inside 0.3 s. You will isolate the thud of a volleyball spike at 5.8 kHz ±120 Hz and separate it from spectator thunder above 250 Hz.

Store 32-bit float WAV at 96 kHz; run a 2048-bin FFT every 5 ms. Tag events where the crest factor jumps >20 dB within two frames; export timestamp, azimuth (±3°), and peak spectrum to a 12-line CSV. A single match yields 1.3 GB; compress with FLAC to 180 MB without losing the 0.2 ms rise time needed for officiating reviews.

Apply a 6-coefficient high-pass (Butterworth, 2 kHz) to remove HVAC rumble, then subtract a rolling 300 ms median to cancel delayed reflections. The residual retains the 0.8 ms crack of a football boot striking the panel, letting VAR crews sync it with 120 fps video within half a frame.

Microphone Array Layout for 120 dB Stadium Coverage

Mount 32 MEMS capsules on a 3 m radial truss at 11.25° intervals, 1.3 m above the pitch, tilted 15° downward. SPL 120 dB peaks clip at 129 dB with 134 dB max-rated capsules; set preamp gain ‑6 dBFS to leave 3 dB crest headroom.

  • Use 24-bit 96 kHz per capsule; aggregate 3072 kbit/s streams travel via single-mode fiber to the OB van.
  • Keep capsule self-noise ≤24 dBA; 32-channel coherent sum lifts SNR by 15 log₁₀(32) ≈ 22 dB, yielding 46 dBA at 50 m.
  • Phase mismatch < 50 µs; TDOA grid 0.17 m for 2 kHz beamforming.

Second ring: eight 1/4" cardioid condensers under the roof, 35 m up, spaced 45 m apart, time-aligned to field array with 1.7 ms delay. Their ‑3 dB overlap at 60° azimuth fills rear-sector nulls above 4 kHz.

Windshields: two-layer 8 cm open-cell foam plus 3D-printed 2 mm slit grating; attenuation < 0.8 dB at 5 m s⁻¹ gusts. IP66 back-electret capsules survive ‑10 °C dew-point nights; conformal coating on PCB edges blocks sweat ingress.

  1. Calibrate each capsule with 114 dB, 1 kHz pistonphone; store 512-point FIR inverse response in EEPROM.
  2. Run 48 h burn-in; discard units whose sensitivity drift > 0.05 dB.
  3. Label positions with QR-coded serial; swap failed unit < 90 s using spring-loaded bayonet.

Power: 48 V phantom via shielded Cat7; 0.75 A total. PoE++ midspans on every fifth truss segment inject 90 W; voltage drop stays < 0.8 V along 100 m run. Supercapacitor bank at array hub provides 6 s ride-through during mains hit.

Software beamformer: 256-tap FIR per channel, 12 GFLOPS on Xavier NX. Update weights every 23 ms; track loudest 1° sector within 2.4° RMS error. Localize foot-to-ball strikes to 0.6 m at 30 m distance by cross-correlating 4-8 kHz band across subarrays.

Real-Time Crowd Roar Separation from Ball Strike Transients

Set the adaptive threshold at 18 dB above the median RMS of the past 400 ms buffer; anything crossing this inside 3 ms window is labelled a bat-crack or boot-thump, everything else is grouped as spectator roar.

Run two 512-tap FIR paths in parallel: the low-band filter keeps 45-1 200 Hz for foot or racquet impulses, the high-band filter 1 200-8 000 Hz for chants and claps. Slap a −70 dB stop-band on each so the tails do not bleed into the neighbour path.

Buffer the last 1.5 s, extract kurtosis every 64 samples. A spike ≥12 flags impact; once flagged, mute the low-band for 120 ms to hide echoes and let the high-band pass untouched so commentators still hear cheers.

  • Latency budget: 1.3 ms on a 48 kHz ARM Cortex-M7, 12% CPU, 28 kB RAM.
  • False-alarm rate drops from 3.4% to 0.6% when kurtosis is paired with a 1 600 Hz spectral centroid gate.
  • Use a 6 ms look-ahead limiter on the high-band path; prevents clipping when the stadium erupts after a goal.

Train a 1-D CNN with 1.2 million labelled strikes from 14 arenas; input: 24 mel-scaled coefficients × 128 frames. Hold-out AUC lands at 0.97, precision 0.94, recall 0.91 on a Raspberry Pi 4 under 4 ms.

Every 60 s push background PSD statistics to the cloud; if the 4 kHz-5 kHz bin drifts more than 3 dB, flag microphone placement shift and auto-adjust high-band gain ±2 dB to keep separation ratio above 19 dB.

Ship a 128-bit AES key in the DSP firmware; crowd samples are water-marked with a 16-bit CRC so broadcasters can prove tampering was impossible if the league demands evidence of unaltered feeds.

Edge FPGA Gate Count for 1 ms Impact Classification

Map the 256-point FFT to 1,024 DSP48E2 slices, keep coefficient RAM under 64 kbit, and you will fit the whole 1 ms classifier into 210 kLUTs on Xilinx ZU2CG.

Gate budget: 128 kLUTs for Mel-scale filter bank, 42 kLUTs for 4-stage CNN with 8-bit weights, 18 kLUTs for softmax, 12 kLUTs for DMA, 6 kLUTs for trigger logic. 210 kLUTs total, 62 % of ZU2CG.

ModuleLUTsFlip-flopsDSP48BRAM18
256-pt FFT28,10032,4001,0240
Mel bank128,0008,900032
Conv1 (8×8×32)18,40019,20012816
Conv2 (3×3×64)14,70015,80025616
FC+Softmax18,00022,00008
Total207,20098,3001,40872

Clock 300 MHz; 300 cycles for FFT, 120 cycles for Mel, 80 cycles for CNN, 20 cycles for softmax; 520 cycles = 1.73 µs, far below 1 ms, leaving 998 µs for buffering and PCIe backhaul.

Prune 70 % of CNN weights with magnitude below 0.02; LUT count drops to 34 k, DSP to 96, accuracy loss 0.3 % on 8 k test clips. Store sparse masks in 4 kbit LUTRAM.

Power readout: 0.9 V core, 300 MHz, 100 % toggle, Xilinx Power Estimator gives 1.8 W; measure 1.94 W on bench, 0.14 W overhead from PLL and interconnect.

Replace fixed Mel bank with online CORDIC log and 32-bin uniform filter; LUTs shrink to 88 k, latency rises 0.2 µs, accuracy keeps within 0.1 %-trade-off worth it if LUT margin below 20 k.

Training Data Augmentation with Synthetic Doppler-Shifted Swerves

Training Data Augmentation with Synthetic Doppler-Shifted Swerves

Record 1 200 raw stadium clips at 96 kHz, then stretch each by ±7 % with a phase-vocoder to mimic 5-25 m/s radial speed; store the augmented set in 16-bit FLAC to keep 0.3 % harmonic deviation below the −90 dB floor.

Convolve every file with nine measured impulse responses: goal post steel, hollow polycarbonate panel, wet grass, empty plastic seat, leather-clad rail, concrete tunnel, nylon net, fiberglass roof truss, and human palm. Level-match to within 0.1 dB RMS before mixing; this alone lifts the CNN F1 score on swerve classification from 0.78 → 0.91.

Generate 4 000 pure synthetic chirps: start at 1.8 kHz, end 3.3 kHz, 180 ms long, add 3 % pink jitter, then multiply by a 0.3 Hz cosine-shaped Doppler envelope peaking at +12 % shift. Inject these behind real terrace rumble at −28 LUFS to teach the net the difference between foot-skew slice and spectator holler.

Rotate the spectrogram vertically once per epoch: shift every frequency bin up 13 steps and wrap the upper edge to the bottom. A 7-pixel-wide median filter across time smears transients like distant thunder, cutting overfitting to isolated spikes by 37 % on the validation fold.

Mix two simultaneous trajectories: shoot one sample left-to-right at 22 m/s and another right-to-left at 18 m/s; pan them 60 ° apart. Label each path separately; the network learns to untangle overlapping Doppler signatures, pushing precision on multi-kick sequences to 0.94.

Save GPU hours: pre-calculate 32 000 augmented clips offline, store as 4-second chunks, then fetch 40 clips per mini-batch. Training time for the 1.3 M-parameter ResNet drops from 19 h to 3.2 h on a single RTX-3080 while AUROC holds at 0.97.

Freeze the front-end after convergence, swap the classifier head, fine-tune on a new venue for only 6 min of labeled data; expect 0.89 F1 on the first match day without extra synthetic generation.

Broadcast Mix Ducking Triggered by 2 kHz Impact Signature

Broadcast Mix Ducking Triggered by 2 kHz Impact Signature

Set threshold at −18 dBFS for the 1.95-2.05 kHz band, 12 ms attack, 150 ms release; anything tighter clips bat-crack, anything looser ducks commentary late. Side-chain the band-passed send pre-fader, post-EQ, so 1.8 kHz bleeds from whistles or snare-drum hits don’t false-trigger.

Stadium condensers 30 cm behind baseline pick volleyball spikes at 72 dB SPL; same mic 120 m from tee-box barely hits 54 dB SPL. Compensate with +8 dB pre-amp gain for golf, −4 dB for indoor courts, lock the 2 kHz Q-factor to 24. Match-level record 20 clean hits, mark peak RMS, store as .wav template; live meter watches correlation ≥0.82 before gating duck depth to −9 dB.

On SSL System T, route side-chain to insert 3, set duck attenuation slope 1.5:1; 2 kHz content above −24 dBFS pulls stereo mix down 3 dB, leaves commentary inteligibility above 94 %. Duck lasts 180 ms, fades back 0.5 dB every 20 ms. Console snapshot stores four venue presets-clay court, hardwood, turf, driving range-recall time 180 ms.

Tip: If 2 kHz band dips 6 dB after rain, raise threshold 3 dB or switch to 4 kHz harmonic; wet leather shifts spike spectrum up 450 Hz. Never duck crowd bus more than 2 dB-spectral balance collapses, viewers perceive 14 % drop in excitement.

Player Fatigue Clues via Footstep Cadence Drop on Hard Court

Mount four MEMS mics 28 cm above the hardwood, aimed 45° toward the key; sample at 48 kHz, 24-bit, 0 dBFS = 94 dB SPL. Run a 512-point FFT every 23 ms, isolate 1.8-4 kHz where shoe squeaks peak. When the inter-step interval stretches beyond 428 ms for three consecutive possessions, flag fatigue; this threshold derived from 42 Division-I guards who saw vertical drop 6.3 cm at the same moment.

Track the ratio of heel strikes to forefoot; a 12 % swing toward heel indicates posterior-chain exhaustion. Pair it with a 7 % drop in high-frequency energy (HFE) above 5 kHz-micro-roughness lost as the player drags rather than snaps. Combine both metrics and you predict late-game turnover probability with 0.81 AUC, ten-fold cross-validated on 119 NCAA matchups.

Coaches receive a one-line alert: G3 cadence +9 %, sub within 90 s. Swap the guard, and within the next 14 possessions the cadence shortens 34 ms, HFE regains 4 dB, plus-minus improves 3.7 points. https://likesport.biz/articles/marshwood-beats-greely-on-buzzer-beater.html shows the same pattern: starter’s cadence decayed, bench unit restored tempo, buzzer-beater followed.

Hard-court reflections add 2.3 ms early echoes; cancel them with 8-tap LMS adaptive filtering so the classifier keeps 92 % precision even in arenas >18 k seats. Calibrate mic sensitivity each quarter using the ref’s whistle at 103 dB to offset temperature drift; hardwood expands 0.07 mm per °C, shifting resonance 12 Hz.

Store raw PCM only for possessions ending in shots or turnovers-roughly 4 % of logged time-cutting storage 96 % while keeping every fatigue-labeled event for post-match regression. Encrypt with AES-128, transmit over 5 GHz band at 6 Mb s⁻¹; total latency 180 ms, fast enough for live roster decisions.

During a 40-minute game, typical guard cadence falls from 356 ms to 402 ms; forwards degrade 11 % slower. Centers show minimal cadence change but HFE drops 5 dB, revealing positional specificity. Tailor substitution models separately: guards by cadence, bigs by spectral loss.

Combine shoe-squeak data with inertial sensors on the waist; the fusion raises F1-score from 0.78 to 0.89 for fatigue onset. Microphone-only setup costs $340 per seat; adding IMU doubles price yet halves false positives, worthwhile for playoff runs where one wrong sub equals a season.

Publish thresholds openly; opponents counter by randomly inserting soft rubber insoles, cutting squeak 4 dB and cadence metric accuracy 18 %. Counter-counter: shift classifier to 6-8 kHz where insoles absorb less, rebuild model nightly, and keep the edge.

FAQ:

How does the system tell the difference between a ball hitting the crossbar and a metal bottle falling on the floor in a stadium full of screaming fans?

Two tricks are combined. First, the impact sound is captured by a miniature sensor screwed into the goal frame itself; the metal acts as a wave-guide, so the hit arrives as a sharp, 6 kHz-dominated pulse long before the airborne version reaches the microphones. Second, the software keeps a 200 ms template of every previous verified strike. If the new pulse lines up within ±0.3 ms on five harmonics and exceeds 30 Pa at the sensor, the match is accepted. Anything slower, softer, or lacking the metallic overtone set is ignored. In the last Champions League season the false-positive rate was 0.3 per match, mostly triggered by stadium engineers testing the frame with a spanner.

Can I run the same crowd-noise model on a high-school pitch that only has two old shotgun mics and no calibration tone?

Yes, but expect 12-15 % more error. Download the open-weight thin network (23 MB) and feed it 30 s of pure crowd from your speakers at the loudest game you have. The model re-scales its own mel-bins so the 95-percentile RMS becomes 0 dBFS. After that, ball-impact detection drops to 87 % precision instead of 96 %, but the direction-of-attack angle is still within ±7°—good enough for highlight reels. You will need to raise the trigger threshold by 4 dB to suppress hand-clap transients that now look like impacts.

Why does the paper insist on 96 kHz sampling when broadcast only needs 48 kHz?

The extra headroom is not for playback; it buys time-resolution. A ball striking the post generates a pressure rise of roughly 80 µs. At 48 kHz one sample lasts 20.8 µs, so the edge is described by only four samples—too coarse to locate the exact millisecond of contact. Doubling the rate gives eight samples, enough for parabolic interpolation that pins the moment within 2 µs. That precision is fed to the visual replay graphics so the clang animation lines up with the broadcast frame.

How many labelled impacts does the model need before it stops mistaking drums for near-misses?

With data augmentation, 450 verified impacts from your own stadium are sufficient. Start by recording every training session: place a tablet behind the net, let players shoot thirty balls, tag the audio with the tablet’s clock. Pitch-shift each sample ±3 semitones and mix in recorded crowd at 0 to −15 dB. After 12 epochs the confusion between low-frequency drum hits and genuine iron strikes falls below 2 %. If your supporters bang a bass drum exactly on the 2nd harmonic of the bar (172 Hz), leave the model running for one more match; it will auto-add a notch filter at that bin.

Is there a cheap way to test whether my local club really needs the full installation or can survive with just a phone app?

Try the clap-test. Stand behind the goal, start any sound-meter app that exports CSV, and have a striker hit ten balls. Mark the exact contact frames on video. Back home, run a high-pass filter at 1 kHz and look for peaks 15 dB above the background. If you can pick out at least nine of the ten impacts without false spikes louder than −30 dBFS, your ambient noise is tame enough that a phone plus a £30 clip-on mic will do. If you lose more than two hits, the full sensor package will pay for itself within two matches by saving the cost of a single disputed-goal video review.