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Communication and media
realtime-audio-dsp
real-time audio processing
RNNoise, Krisp
System
Techniques · 3
Primitives · 18
realtime-audio-dsp
Acoustic echo cancellation
media · medium: Adaptive filter (NLMS/frequency-domain) removing far-end echo from the microphone signal
Noise suppression
media · medium: Spectral gain estimation (Wiener/RNN) to remove background noise from speech
Sample rate conversion
signal-processing · medium: Polyphase and windowed-sinc resampling; asynchronous rate conversion for clock drift
Realtime audio callback
media · low: Allocation- and lock-free audio thread design with bounded work per buffer
Fast Fourier transform
numerics · low: Computes the DFT in O(n log n) by recursively splitting into even and odd halves; the basis of fast convolution and spectral methods.
›
IIR and FIR filters
signal-processing · low: Two ways to shape a sampled signal's spectrum: FIR (finite taps, always stable, exact linear phase) or IIR (feedback, a few coefficients, cheap but phase-distorting).
›
Recurrent networks
machine-learning · low: GRU and LSTM cells for streaming sequence models
Voice activity detection
machine-learning · low: Per-frame speech/non-speech decision with smoothing, from an energy threshold over a tracked noise floor up to small neural nets.
›
Short-time Fourier transform and sp…
signal-processing · low: Slide a window over a signal and FFT each frame to get a time-frequency picture; the front end of audio ML, vibration and radar analysis.
›
SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
›
SPSC ring buffer
data-structures · low: Bounded wait-free queue for exactly one producer and one consumer thread, built from an array and two indices with acquire/release ordering.
›
Automatic gain control
media · low: Level estimation and smoothed gain to keep speech at a target loudness
Weight quantization
ml-training · low: Store weights as 8/4-bit integers with per-group scales; decode is memory-bound, so 4-bit weights give about 3-4x tokens/s and fit 4x larger models.
›
Floating-point error
numerics · low: How IEEE 754 rounding error accumulates and how to contain it: compensated (Kahan/Neumaier) and pairwise summation, error-free transformations with FMA, ULP comparisons.
›
Parameter smoothing
media · low: Per-sample ramps of control parameters to avoid zipper noise
False sharing
concurrency · low: Independent variables written by different cores on one cache line force the line to bounce between cores; pad or regroup hot data to fix it.
›
Memory ordering
concurrency · low: What Relaxed, Acquire, Release and SeqCst atomics actually guarantee, what they cost on x86 and ARM, and which one each pattern needs.
›
CPU caches and the memory hierarchy
hardware-os · low: Sizes, line widths and latencies of L1/L2/L3/DRAM on modern x86 and Apple M-series, and the data-layout rules that follow from them.
›
Low precision number formats
numerics · low: Bf16, fp16, fp8 (E4M3, E5M2), MX block formats and int4: ranges, rounding, scaling
+ 2 more primitives
System
Techniques · 3
Primitives · 18
realtime-audio-dsp
Acoustic echo cancellation
media · medium: Adaptive filter (NLMS/frequency-domain) removing far-end echo from the microphone signal
Noise suppression
media · medium: Spectral gain estimation (Wiener/RNN) to remove background noise from speech
Sample rate conversion
signal-processing · medium: Polyphase and windowed-sinc resampling; asynchronous rate conversion for clock drift
Realtime audio callback
media · low: Allocation- and lock-free audio thread design with bounded work per buffer
Fast Fourier transform
numerics · low: Computes the DFT in O(n log n) by recursively splitting into even and odd halves; the basis of fast convolution and spectral methods.
›
IIR and FIR filters
signal-processing · low: Two ways to shape a sampled signal's spectrum: FIR (finite taps, always stable, exact linear phase) or IIR (feedback, a few coefficients, cheap but phase-distorting).
›
Recurrent networks
machine-learning · low: GRU and LSTM cells for streaming sequence models
Voice activity detection
machine-learning · low: Per-frame speech/non-speech decision with smoothing, from an energy threshold over a tracked noise floor up to small neural nets.
›
Short-time Fourier transform and sp…
signal-processing · low: Slide a window over a signal and FFT each frame to get a time-frequency picture; the front end of audio ML, vibration and radar analysis.
›
SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
›
SPSC ring buffer
data-structures · low: Bounded wait-free queue for exactly one producer and one consumer thread, built from an array and two indices with acquire/release ordering.
›
Automatic gain control
media · low: Level estimation and smoothed gain to keep speech at a target loudness
Weight quantization
ml-training · low: Store weights as 8/4-bit integers with per-group scales; decode is memory-bound, so 4-bit weights give about 3-4x tokens/s and fit 4x larger models.
›
Floating-point error
numerics · low: How IEEE 754 rounding error accumulates and how to contain it: compensated (Kahan/Neumaier) and pairwise summation, error-free transformations with FMA, ULP comparisons.
›
Parameter smoothing
media · low: Per-sample ramps of control parameters to avoid zipper noise
False sharing
concurrency · low: Independent variables written by different cores on one cache line force the line to bounce between cores; pad or regroup hot data to fix it.
›
Memory ordering
concurrency · low: What Relaxed, Acquire, Release and SeqCst atomics actually guarantee, what they cost on x86 and ARM, and which one each pattern needs.
›
CPU caches and the memory hierarchy
hardware-os · low: Sizes, line widths and latencies of L1/L2/L3/DRAM on modern x86 and Apple M-series, and the data-layout rules that follow from them.
›
Low precision number formats
numerics · low: Bf16, fp16, fp8 (E4M3, E5M2), MX block formats and int4: ranges, rounding, scaling
LU and Cholesky factorization
numerics · low: Factor A = PLU (any square matrix, partial pivoting) or A = LLᵀ (SPD) once in O(n³), then solve each right-hand side in O(n²).
›
Biquad filters
signal-processing · low: Second-order IIR sections (RBJ cookbook): low/high pass, peaking EQ; stable cascades
Copy for AI
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