stft-rs

High-quality, streaming-friendly STFT/iSTFT implementation in Rust working with raw slices (&[f32]).

[!CAUTION] This crate is a WIP, expect API changes and breakage until first stable version

Features

• Batch Processing: Process entire audio buffers at once
• Streaming Support: Incremental processing for real-time applications
• High Quality: >138 dB SNR reconstruction
• Dual Reconstruction Modes:
  • OLA (Overlap-Add): Optimal for spectral processing
  • WOLA (Weighted Overlap-Add): Standard implementation
• Multiple Window Functions: Hann, Hamming, Blackman
• NOLA/COLA Validation: Ensures reconstruction quality
• Flexible Buffer Management: Three allocation strategies from simple to zero-allocation
• Multi-Channel Audio: Process stereo, 5.1, 7.1+ with planar or interleaved formats (parallelized with rayon)
• Generic Float Support: Works with f32, f64, and other float types
• Type Aliases: Convenient aliases like StftConfigF32, BatchStftF32 for cleaner code
• Spectral Operations: Built-in helpers for magnitude/phase manipulation, filtering, and custom processing
• No External Tensor Libraries: Works directly with slices

Quick Start

use stft_rs::prelude::*;

let config = StftConfig::<f32>::default_4096();

let stft = BatchStft::new(config.clone());
let istft = BatchIstft::new(config);

let signal: Vec<f32> = vec![0.0; 44100];
let spectrum = stft.process(&signal);

// Manipulate spectrum here...

let reconstructed = istft.process(&spectrum);

Type Aliases for Convenience

For cleaner code, use type aliases instead of specifying generic types:

use stft_rs::prelude::*;

// Instead of StftConfig::<f32>, use:
let config = StftConfigF32::default_4096();
let stft = BatchStftF32::new(config.clone());
let istft = BatchIstftF32::new(config);

// Available aliases:
// - StftConfigF32, StftConfigF64
// - BatchStftF32, BatchIstftF32, BatchStftF64, BatchIstftF64
// - StreamingStftF32, StreamingIstftF32, StreamingStftF64, StreamingIstftF64
// - SpectrumF32, SpectrumF64
// - SpectrumFrameF32, SpectrumFrameF64

Prelude

For convenience, import commonly used types with:

use stft_rs::prelude::*;

This exports:

• Core types: BatchStft, BatchIstft, StreamingStft, StreamingIstft, StftConfig, Spectrum, SpectrumFrame
• Type aliases: StftConfigF32/F64, BatchStftF32/F64, BatchIstftF32/F64, StreamingStftF32/F64, StreamingIstftF32/F64, SpectrumF32/F64, SpectrumFrameF32/F64
• Enums: ReconstructionMode, WindowType, PadMode
• Utilities: apply_padding, interleave, deinterleave, interleave_into, deinterleave_into

Batch vs Streaming

Batch API (Stateless)

Best for: Processing entire files, offline processing, ML training

use stft_rs::prelude::*;

let config = StftConfig::default_4096();
let stft = BatchStft::new(config.clone());
let istft = BatchIstft::new(config);

let spectrum = stft.process(&signal);
let reconstructed = istft.process(&spectrum);

Streaming API (Stateful)

Best for: Real-time audio, low-latency processing, incremental processing

use stft_rs::prelude::*;

let config = StftConfig::default_4096();
let mut stft = StreamingStft::new(config.clone());
let mut istft = StreamingIstft::new(config.clone());

let pad_amount = config.fft_size / 2;
let padded = apply_padding(&signal, pad_amount, PadMode::Reflect);

let mut output = Vec::new();
for chunk in padded.chunks(512) {
    let frames = stft.push_samples(chunk);
    for frame in frames {
        let samples = istft.push_frame(&frame);
        output.extend(samples);
    }
}

for frame in stft.flush() {
    output.extend(istft.push_frame(&frame));
}
output.extend(istft.flush());

// Remove padding: output[pad_amount..pad_amount + signal.len()]

Note on Padding in Streaming Mode:

• Batch mode automatically applies reflection padding internally for optimal quality
• Streaming mode requires manual padding for best results (>130 dB SNR)
• Without padding, edge effects reduce quality to ~40-60 dB SNR
• Use apply_padding() helper function or implement custom padding
• For truly real-time applications without pre-roll, accept the edge artifacts or use fade-in/fade-out

Buffer Management

The library provides three allocation strategies for different performance requirements:

Level 1: Simple API (Allocates on each call)

Best for: Prototyping, one-off processing, simplicity

// Each call allocates new Vec for frames/samples
let frames = stft.push_samples(chunk);
let samples = istft.push_frame(&frame);

Level 2: Reusable Containers (_into methods)

Best for: Repeated processing, reduced allocator pressure

// Reuse outer Vec, but still allocates frame data
let mut frames = Vec::new();
let mut output = Vec::new();

loop {
    frames.clear();  // Keeps capacity
    stft.push_samples_into(chunk, &mut frames);

    for frame in &frames {
        istft.push_frame_into(frame, &mut output);
    }
}

Batch mode:

let mut spectrum = Spectrum::new(num_frames, freq_bins);
let mut output = Vec::new();

stft.process_into(&signal, &mut spectrum);
istft.process_into(&spectrum, &mut output);

Level 3: Zero-Allocation Frame Pool (_write methods)

Best for: Real-time audio, hard real-time constraints, minimum latency variance

// Pre-allocate frame pool once
let max_frames = (chunk_size + config.hop_size - 1) / config.hop_size + 1;
let mut frame_pool = vec![SpectrumFrame::new(config.freq_bins()); max_frames];
let mut output = Vec::new();

loop {
    let mut pool_idx = 0;
    stft.push_samples_write(chunk, &mut frame_pool, &mut pool_idx);

    for i in 0..pool_idx {
        istft.push_frame_into(&frame_pool[i], &mut output);
    }
}

Configuration

Creating Custom Configurations

use stft_rs::prelude::*;

// OLA mode
let config = StftConfig::new(
    4096,
    1024,
    WindowType::Hann,
    ReconstructionMode::Ola
).expect("Valid configuration");

// WOLA mode
let config = StftConfig::new(
    2048,
    512,
    WindowType::Hamming,
    ReconstructionMode::Wola
).expect("Valid configuration");

Window Functions

• Hann: Smooth frequency response, good general purpose
• Hamming: Slightly better frequency resolution
• Blackman: Lower side lobes, better for spectral analysis

Reconstruction Modes

OLA (Overlap-Add)

• Window applied on forward transform only
• No window on inverse transform
• Normalizes by accumulated window energy: sum(w)
• Use for: Spectral processing, modification, filtering
• Requires: COLA (Constant Overlap-Add) condition

WOLA (Weighted Overlap-Add)

• Window applied on both forward and inverse transforms
• Normalizes by accumulated window squared: sum(w²)
• Use for: Standard analysis/resynthesis
• Requires: NOLA (Nonzero Overlap-Add) condition

Spectral Processing

The library provides powerful helpers for frequency domain manipulation:

let mut spectrum = stft.process(&signal);

// Get magnitude and phase
let mag = spectrum.magnitude(frame, bin);
let phase = spectrum.phase(frame, bin);

// Set from magnitude and phase
spectrum.set_magnitude_phase(frame, bin, new_mag, new_phase);

// Get all magnitudes/phases for a frame
let magnitudes = spectrum.frame_magnitudes(frame);
let phases = spectrum.frame_phases(frame);

// Apply gain to frequency range
spectrum.apply_gain(100..200, 0.5); // Attenuate bins 100-200

// Zero out frequency range
spectrum.zero_bins(0..50); // Remove DC and low frequencies

// Custom processing with closure
spectrum.apply(|frame, bin, complex| {
    // Return modified complex value
    complex * gain_factor
});

Examples

High-Pass Filter

let mut spectrum = stft.process(&signal);

// Zero out low frequencies (simple and clean!)
spectrum.zero_bins(0..100);

let filtered = istft.process(&spectrum);

Volume Control (Spectral Domain)

let mut spectrum = stft.process(&signal);

// Apply gain in magnitude/phase domain
for frame in 0..spectrum.num_frames {
    for bin in 0..spectrum.freq_bins {
        let mag = spectrum.magnitude(frame, bin);
        let phase = spectrum.phase(frame, bin);
        spectrum.set_magnitude_phase(frame, bin, mag * 0.5, phase);
    }
}

let quieter = istft.process(&spectrum);

Band-Pass Filter

let mut spectrum = stft.process(&signal);

// Keep only frequencies between 300 Hz and 3000 Hz
let sample_rate = 44100.0;
let freq_resolution = sample_rate / config.fft_size as f32;
let low_bin = (300.0 / freq_resolution) as usize;
let high_bin = (3000.0 / freq_resolution) as usize;

spectrum.zero_bins(0..low_bin);
spectrum.zero_bins(high_bin..spectrum.freq_bins);

let filtered = istft.process(&spectrum);

Multi-Channel Audio

Process stereo, 5.1, or any channel count. Channels are processed in parallel with rayon (enabled by default):

// Planar: separate Vec per channel
let left = vec![0.0; 44100];
let right = vec![0.0; 44100];
let spectra = stft.process_multichannel(&[left, right]); // Parallel with rayon

// Interleaved: L,R,L,R...
let interleaved = vec![0.0; 88200];
let spectra = stft.process_interleaved(&interleaved, 2);

// Convert between formats
let channels = deinterleave(&interleaved, 2);
let interleaved = interleave(&channels);

Disable parallel processing: cargo build --no-default-features

See examples/multichannel_stereo.rs and examples/multichannel_midside.rs for more.

Performance Characteristics

• Batch Mode: Optimized for throughput, minimal allocations
• Streaming Mode: Optimized for latency, incremental output
• Memory: Batch allocates once, streaming uses growing buffers
• Latency: Streaming introduces fft_size - hop_size samples of latency

Typical Performance (4096 FFT, 1024 hop)

• Reconstruction Quality: >138 dB SNR
• Algorithmic Latency: 3072 samples (69.7 ms @ 44.1kHz)
• Throughput: Depends on FFT implementation (rustfft)

Examples

Run the included examples:

# Basic batch processing
cargo run --example basic_usage

# Streaming processing with chunks
cargo run --example streaming_usage

# Spectral manipulation (filtering, time-varying processing)
cargo run --example spectral_processing

# Multi-channel stereo processing
cargo run --example multichannel_stereo

# Mid/side stereo width manipulation
cargo run --example multichannel_midside

# Advanced streaming with buffer reuse patterns
cargo run --example advanced_streaming

# Performance comparison of allocation strategies
cargo run --release --example buffer_reuse

# Type aliases usage demonstration
cargo run --example type_aliases

# Spectral operations (magnitude/phase, filtering)
cargo run --example spectral_operations

Implementation Details

Critical Design Decisions

1. Flat Data Layout: Spectrum stores data as [real_all, imag_all] for cache efficiency
2. Padding: Batch mode uses reflection padding (fft_size/2 on each side)
3. Normalization: Per-sample normalization by accumulated window energy
4. Conjugate Symmetry: Automatically handled in iSTFT for real signals
5. Streaming Latency: Samples released only when fully reconstructed (all overlaps complete)

STFT Formula

X[k,n] = Σ x[n + m] * w[m] * e^(-j2πkm/N)

Where:

• x[n]: Input signal
• w[m]: Window function
• N: FFT size
• k: Frequency bin
• n: Frame index (hop positions)

iSTFT Reconstruction

OLA Mode:

x[n] = Σ IFFT(X[k,m]) / Σ w[n - m*hop]

WOLA Mode:

x[n] = Σ IFFT(X[k,m]) * w[n - m*hop] / Σ w²[n - m*hop]

Testing

Run the comprehensive test suite:

cargo test --lib

# With output
cargo test --lib -- --nocapture

Tests verify:

• NOLA/COLA condition validation
• Batch OLA roundtrip (>138 dB SNR)
• Batch WOLA roundtrip (>138 dB SNR)
• Streaming OLA roundtrip (>138 dB SNR)
• Streaming WOLA roundtrip (>138 dB SNR)
• Batch vs streaming consistency
• All window functions (Hann, Hamming, Blackman)
• Constant signal reconstruction
• Padding modes (reflect, zero, edge)

Dependencies

• rustfft: High-performance FFT implementation
• ndarray: Only for internal padding operations (minimal usage)

License

[MIT]

Contributing

Contributions welcome! Areas for improvement:

• Additional window functions (Kaiser, Gaussian)
• SIMD optimizations
• GPU acceleration support
• More padding modes
• Overlap-save mode