Speech Algorithms – Overview

What it is – A collection of reference implementations for classic and modern speech‑processing algorithms. The repo is organised into self‑contained folders, each holding a small demo or library that you can download (via DownGit) and run.

Why it matters – The code covers the whole pipeline that engineers encounter when building voice‑enabled products: raw‑signal handling, noise‑reduction, echo cancellation, voice‑activity detection, feature extraction, and even a few neural‑network‑based recognisers. Having a single place with ready‑to‑run examples speeds up learning and prototyping.

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Main functional areas

Area	Typical use‑case	Example implementation
Signal preprocessing	Resampling, alignment, pitch‑shift, watermarking	Resample, AudioAlignment, VoiceChange
Sound design	Generate synthetic ambience for games or VR	DesignSound/rain, DesignSound/wind
Acoustic echo cancellation (AEC)	Remove far‑end echo in teleconferencing	LMS‑based AEC, Kalman‑filter AEC, WebRTC AEC
Automatic gain control (AGC)	Keep microphone level stable	WebRTC AGC implementation
Noise reduction	Suppress background hiss or transient clicks	Spectral subtraction, transient‑interference suppression, WebRTC ANR
Speech enhancement	Create training data or improve noisy speech	Speech augmentation, DNN mask‑based enhancement, TF‑mask separation
Voice‑activity detection (VAD)	Detect when a speaker is talking	WebRTC VAD, LSTM‑based VAD, AMR‑coder label generator
Microphone‑array processing	Beamforming and source localisation for multi‑mic devices	CGMM‑MVDR beamformer, TDOA & SRP‑PHAT localisation
Pattern recognition	Keyword spotting, command recognition, speaker gender, environmental sounds	CNN command recogniser, XGBoost environmental classifier, deep‑learning keyword spotter
Speech codecs	Low‑bitrate transport of speech	Lyra (AI codec) and classic G.711 implementations
Quality metrics	Objective evaluation of processed speech	Speech‑quality and intelligibility measures

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How to use the repo

1. Clone the repository.
2. Navigate to the folder of interest (e.g., AudioProcess/Goertzel).
3. Follow any local README or script instructions; most folders contain a small Python/Matlab/C++ demo that can be run directly.
4. For large datasets (e.g., augmentation data) the README points to a separate repo (noise‑xorcist).

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Who will benefit?

• Students & hobbyists learning DSP concepts for speech.
• Engineers prototyping voice‑assistants, teleconferencing, or AR/VR audio pipelines who need quick reference implementations.
• Researchers looking for baseline code to compare against newer models.

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Limitations

• The implementations are primarily educational; they are not production‑hardened or heavily optimised.
• Some modules rely on external libraries (e.g., WebRTC, TensorFlow/PyTorch) that must be installed manually.
• No unified build system – each sub‑folder is independent.

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Bottom line – Speech Algorithms is a well‑structured toolbox of classic and modern speech‑processing code, useful for learning and rapid prototyping across the full speech‑signal chain.