SpeechAlgo

A comprehensive Python library providing clean, educational implementations of fundamental speech processing algorithms.

Overview

SpeechAlgo provides reference implementations of 20 core speech processing algorithms, organized into five categories: preprocessing, voice activity detection (VAD), pitch detection, speech enhancement, and feature extraction. The library is designed for both educational purposes and production use, with clear code, comprehensive documentation, and mathematical foundations.

Features

• Clean, readable implementations suitable for learning and research
• Comprehensive documentation with mathematical foundations and references
• Type-annotated code with extensive docstrings
• NumPy-based implementations for efficiency
• Modular design with consistent APIs
• Well-tested algorithms with unit tests
• Real-time capable for many algorithms

Installation

From PyPI

PyPI: SpeechAlgo

pip install speechalgo

From source

git clone https://github.com/tarun7r/SpeechAlgo.git
cd SpeechAlgo
pip install -e .

Development installation

pip install -e ".[dev]"

Quick Start

Preprocessing

import numpy as np
from speechalgo.preprocessing import hamming_window, FrameExtractor, MFCC
from speechalgo.utils import load_audio

# Load audio
audio, sr = load_audio('speech.wav', sample_rate=16000)

# Apply window function
window = hamming_window(512)
windowed = audio[:512] * window

# Extract overlapping frames
frame_extractor = FrameExtractor(frame_length=512, hop_length=256)
frames = frame_extractor.extract_frames(audio)
print(f"Extracted {len(frames)} frames")

# Extract MFCC features
mfcc = MFCC(sample_rate=16000, n_mfcc=13)
mfcc_features = mfcc.process(audio)
print(f"MFCC shape: {mfcc_features.shape}")  # (13, n_frames)

Voice Activity Detection

from speechalgo.vad import EnergyBasedVAD, SpectralEntropyVAD, ZeroCrossingVAD

# Energy-based VAD
energy_vad = EnergyBasedVAD(sample_rate=16000)
is_speech = energy_vad.process(audio)
print(f"Speech detected in {is_speech.sum()} / {len(is_speech)} frames")

# Spectral entropy VAD (more robust in noise)
entropy_vad = SpectralEntropyVAD(sample_rate=16000)
is_speech = entropy_vad.process(audio)

# Zero-crossing rate VAD
zcr_vad = ZeroCrossingVAD(sample_rate=16000)
is_speech = zcr_vad.process(audio)

# Extract speech segments
speech_frames = np.where(is_speech)[0]
if len(speech_frames) > 0:
    start = speech_frames[0] * 256  # hop_length
    end = speech_frames[-1] * 256
    speech_segment = audio[start:end]

Pitch Detection

from speechalgo.pitch import YIN, Autocorrelation, HPS

# YIN algorithm (recommended for speech)
yin = YIN(sample_rate=16000)
pitch = yin.estimate(audio_frame)
print(f"Estimated pitch: {pitch:.1f} Hz")

# Autocorrelation method
autocorr = Autocorrelation(sample_rate=16000)
pitch = autocorr.estimate(audio_frame)

# Harmonic Product Spectrum
hps = HPS(sample_rate=16000)
pitch = hps.estimate(audio_frame)

Speech Enhancement

from speechalgo.enhancement import SpectralSubtraction, WienerFilter, NoiseGate

# Spectral subtraction
enhancer = SpectralSubtraction(sample_rate=16000)
clean_audio = enhancer.process(noisy_audio, noise_profile)

# Wiener filtering
wiener = WienerFilter(sample_rate=16000)
clean_audio = wiener.process(noisy_audio)

# Noise gate
gate = NoiseGate(threshold=-40.0, sample_rate=16000)
gated_audio = gate.process(audio)

Feature Extraction

from speechalgo.features import SpectralFeatures, TemporalFeatures, DeltaFeatures

# Spectral features
spectral = SpectralFeatures(sample_rate=16000)
centroid = spectral.spectral_centroid(audio)
rolloff = spectral.spectral_rolloff(audio)
flux = spectral.spectral_flux(audio)

# Temporal features
temporal = TemporalFeatures(sample_rate=16000)
zcr = temporal.zero_crossing_rate(audio)
energy = temporal.short_time_energy(audio)

# Delta features (velocity and acceleration)
delta = DeltaFeatures()
mfcc_delta = delta.compute_delta(mfcc_features)
mfcc_delta2 = delta.compute_delta(mfcc_delta)

Algorithm Categories

Preprocessing (5 algorithms)

1. Windowing - Hamming, Hanning, Blackman window functions for spectral analysis
2. Framing - Overlapping frame extraction with configurable hop length
3. Pre-emphasis - High-frequency boosting filter (α=0.97)
4. MFCC - Mel-Frequency Cepstral Coefficients extraction
5. Mel-Spectrogram - Mel-scale frequency representation

Voice Activity Detection (3 algorithms)

6. Energy-based VAD - Simple threshold-based detection using short-time energy
7. Spectral Entropy VAD - Entropy-based voice detection (robust in noise)
8. Zero-Crossing VAD - Combined energy and zero-crossing rate approach

Pitch Detection (4 algorithms)

10. Autocorrelation - Classic time-domain pitch estimation
11. YIN Algorithm - Improved autocorrelation with difference function
12. Cepstral Method - Pitch detection using cepstrum
13. Harmonic Product Spectrum (HPS) - Frequency-domain approach

Speech Enhancement (3 algorithms)

14. Spectral Subtraction - Classic noise reduction technique
15. Wiener Filtering - Statistical optimal filtering
16. Noise Gate - Threshold-based noise suppression

Feature Extraction (4 algorithms)

17. Spectral Features - Centroid, rolloff, flux, bandwidth
18. Zero Crossing Rate - Time-domain feature extraction
19. Short-Time Energy - Energy computation in frames
20. Delta Features - First and second-order temporal derivatives

Documentation

Comprehensive documentation with mathematical foundations and implementation notes:

• Getting Started Tutorial - Installation and first steps
• Preprocessing Theory - Windowing, framing, MFCC, mel-spectrogram
• VAD Theory - Voice activity detection methods and comparisons
• Pitch Detection Theory - Pitch estimation algorithms and best practices
• Speech Enhancement Theory - Noise reduction techniques

Examples

See Getting Started Tutorial for comprehensive examples including:

• Basic Examples: Window functions, framing, pre-emphasis, MFCC, VAD, pitch detection, enhancement
• Complete Workflows:
  • Feature extraction pipeline (MFCC + delta + delta-delta = 39 dimensions)
  • VAD + pitch tracking for voiced speech analysis
  • Multi-stage noise reduction (noise gate → Wiener filter)
  • Multi-algorithm comparison and benchmarking

Requirements

• Python 3.8+
• NumPy >= 1.20.0
• SciPy >= 1.7.0
• SoundFile >= 0.11.0

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please ensure:

• Code passes all tests
• New features include tests
• Documentation is updated
• Code follows PEP 8 style guide
• Type hints are included

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

This library implements algorithms from foundational research in speech processing:

• Davis & Mermelstein (1980) - MFCC
• de Cheveigné & Kawahara (2002) - YIN algorithm
• Rabiner & Schafer (1978) - Digital speech processing fundamentals
• Ephraim & Malah (1984) - Spectral subtraction
• And many others cited in individual algorithm documentation

Project Status

Current Version: 0.1.0 (MVP Complete)

✅ All 20 core algorithms implemented and tested!

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions