torsh-signal

Signal processing operations for the ToRSh deep learning framework.

Overview

This crate provides PyTorch-compatible signal processing functionality built on top of the SciRS2 ecosystem for high-performance scientific computing. It leverages scirs2-signal and scirs2-fft to deliver state-of-the-art signal processing operations with excellent performance and PyTorch compatibility.

Features

Core Signal Processing (Powered by SciRS2)

• Advanced Spectral Operations: STFT, ISTFT, spectrograms with scirs2-fft acceleration
• Professional Window Functions: Hamming, Hann, Blackman, Kaiser, Gaussian powered by scirs2-signal
• High-Performance Filtering: Convolution, correlation with scirs2-signal optimization
• SciRS2 Integration: Full access to scirs2-signal's advanced signal processing algorithms

PyTorch Compatibility

• Drop-in Replacement: Compatible API for torch.signal operations
• Seamless Migration: Easy transition from PyTorch signal processing
• Performance Benefits: Rust-native performance with Python-like API

Modules

ToRSh Signal Processing Modules

• spectral: STFT, ISTFT, spectrograms, mel-scale filtering (scirs2-fft powered)
• windows: Various window functions for signal processing (scirs2-signal powered)
• filters: Convolution and correlation operations (scirs2-signal powered)

SciRS2 Integration

• scirs2-signal: Advanced signal processing algorithms and optimizations
• scirs2-fft: High-performance FFT operations with SIMD acceleration
• scirs2-core: Scientific computing primitives and memory management

Usage

Basic Signal Processing with SciRS2 Integration

use torsh_signal::prelude::*;
use torsh_tensor::Tensor;

// Create a signal
let signal: Tensor<f32> = Tensor::randn(&[1000])?;

// Apply Hann window
let window = hann_window(256, false)?;
let windowed = signal.slice(0, 0, 256)? * &window;

// Compute STFT
let stft_result = stft(
    &signal,
    256,        // n_fft
    Some(64),   // hop_length
    Some(256),  // win_length
    Some(Window::Hann),
    true,       // center
    false,      // normalized
    true,       // onesided
    false,      // return_complex
)?;

// Compute spectrogram
let spec = spectrogram(
    &signal,
    512,        // n_fft
    Some(128),  // hop_length
    Some(512),  // win_length
    Some(Window::Hann),
    0.0,        // power (0.0 for magnitude)
    false,      // normalized
)?;

Dependencies

Core ToRSh Dependencies

• torsh-core: Core types and device abstraction
• torsh-tensor: Tensor operations and storage
• torsh-functional: Functional API components
• torsh-linalg: Linear algebra operations

SciRS2 Ecosystem Dependencies

• scirs2-signal: Advanced signal processing algorithms and optimizations
• scirs2-fft: High-performance FFT operations with SIMD acceleration
• scirs2-core: Scientific computing primitives and memory management

Supporting Libraries

• num-complex: Complex number support
• num-traits: Numeric trait abstractions
• ndarray: Multi-dimensional arrays (via scirs2-core)
• thiserror: Error handling

Performance

torsh-signal delivers exceptional performance through the SciRS2 ecosystem:

SciRS2-Powered Optimizations

• SIMD-accelerated operations through scirs2-signal and scirs2-core
• High-performance FFT via scirs2-fft with CPU-specific optimizations
• Memory-efficient processing with scirs2-core's advanced memory management
• Vectorized operations for window functions and filtering

Advanced Features

• Automatic optimization selection based on input size and hardware
• Parallel processing capabilities for large-scale signal processing
• Zero-copy operations where possible to minimize memory overhead
• Complex number optimizations with proper magnitude calculations

Compatibility

Designed to be a drop-in replacement for PyTorch's signal processing operations:

• torch.stft → torsh_signal::stft
• torch.istft → torsh_signal::istft
• torch.spectrogram → torsh_signal::spectrogram
• Window functions match PyTorch's implementation

Examples

See the examples/ directory for comprehensive usage examples and PyTorch compatibility demonstrations.