Module 24: Audio and Speech Processing

Master audio and speech processing with deep learning for real-world applications.

What You'll Learn

• Audio Signal Fundamentals
• Speech Recognition (ASR)
• Text-to-Speech (TTS)
• Audio Classification
• Music Generation
• Voice Cloning and Voice Conversion

Topics Covered

1. Audio Fundamentals

• Digital audio representation (waveforms, spectrograms)
• Sampling rate, bit depth, audio formats
• Time-domain vs Frequency-domain analysis
• Mel Spectrograms and MFCCs

2. Speech Recognition (ASR)

• Automatic Speech Recognition basics
• Connectionist Temporal Classification (CTC)
• Attention-based ASR
• End-to-end ASR models (Wav2Vec, Whisper)
• Real-time speech recognition

3. Text-to-Speech (TTS)

• Traditional TTS methods
• Neural TTS (Tacotron, WaveNet)
• Modern TTS (Tacotron 2, FastSpeech)
• Voice cloning and synthesis
• Multi-speaker TTS

4. Audio Classification

• Audio tagging and event detection
• Music genre classification
• Environmental sound classification
• Audio-based emotion recognition

5. Music Generation

• Music generation with RNNs/LSTMs
• Music generation with Transformers
• Music generation with GANs
• MIDI generation and audio synthesis

6. Voice Processing

• Voice Activity Detection (VAD)
• Speaker identification and verification
• Voice conversion
• Speech enhancement and denoising

Learning Objectives

By the end of this module, you should be able to:

• Understand audio signal processing fundamentals
• Build speech recognition systems
• Create text-to-speech models
• Classify audio events and music
• Generate music and audio
• Use audio ML libraries (librosa, torchaudio, transformers)

Prerequisites

Before starting this module, you should have completed:

• Module 09: Neural Networks Basics
• Module 10: Deep Learning Frameworks (PyTorch recommended)
• Module 12: Natural Language Processing (helpful for ASR/TTS)

Projects

1. Speech Recognition: Build ASR system with Whisper
2. Text-to-Speech: Generate speech from text
3. Music Genre Classification: Classify music by genre
4. Voice Cloning: Clone a voice for TTS
5. Audio Event Detection: Detect events in audio recordings

Key Concepts

• Spectrogram: Time-frequency representation of audio
• MFCC: Mel-frequency cepstral coefficients for speech
• CTC: Connectionist Temporal Classification for sequence alignment
• Attention: For sequence-to-sequence ASR/TTS
• Mel Spectrogram: Perceptually-relevant frequency representation

Documentation & Learning Resources

Official Documentation:

• librosa: Audio analysis
• torchaudio: PyTorch audio processing
• Hugging Face Transformers: Pre-trained ASR/TTS models

Free Courses:

• Speech and Language Processing: Jurafsky & Martin

Complete Detailed Guide →

Additional Resources:

• Advanced Topics →: Voice cloning, music generation, real-time processing
• Project Tutorial →: Step-by-step ASR implementation
• Quick Reference →: Audio processing, code snippets