https://github.com/saadsalmanakram/essence-of-sound
Understanding sound with respect to AI and Data
https://github.com/saadsalmanakram/essence-of-sound
signal-processing sound-engineering sound-processing
Last synced: 6 months ago
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Understanding sound with respect to AI and Data
- Host: GitHub
- URL: https://github.com/saadsalmanakram/essence-of-sound
- Owner: saadsalmanakram
- Created: 2024-08-20T17:38:07.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-05T14:33:03.000Z (6 months ago)
- Last Synced: 2025-04-05T15:28:35.192Z (6 months ago)
- Topics: signal-processing, sound-engineering, sound-processing
- Homepage:
- Size: 60.5 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
---
# 🎵 Essence of Sound – Exploring Sound through Programming, AI, and Data
## 📌 Introduction
Sound is at the core of communication, music, speech, and AI-driven applications. This repository serves as a **comprehensive guide** to working with sound in **programming, artificial intelligence, and data science**.
📌 **Learn the fundamentals of sound waves, audio processing, and signal analysis**
📌 **Explore AI-driven applications like speech recognition and music generation**
📌 **Analyze and visualize audio data using Python, NumPy, and Matplotlib**
📌 **Implement real-world applications, including voice assistants and audio classification**
---
## 🚀 Features
- 🎼 **Audio Processing: Fourier Transforms, Spectrograms, and Filters**
- 🔊 **Speech Recognition & Voice Processing with AI**
- 🎶 **Music Analysis, Generation, and Synthesis**
- 🧠 **Deep Learning for Sound: Audio Classification & Sound Event Detection**
- 📊 **Data Science Applications in Sound Processing**
---
## 🏁 Getting Started
### 1️⃣ Clone the Repository
```bash
git clone https://github.com/saadsalmanakram/Essence-of-Sound.git
cd Essence-of-Sound
```
### 2️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```
### 3️⃣ Run Example Scripts
```bash
python audio_basics/fourier_transform.py
python speech_processing/speech_to_text.py
python deep_learning/audio_classification.py
```
---
## 🔍 Topics Covered
### 🎧 **Sound & Signal Processing**
- **Understanding Sound Waves, Amplitude, and Frequency**
- **Fourier Transforms for Spectral Analysis**
- **Spectrograms and Wavelet Transforms**
- **Audio Filtering and Noise Reduction**
### 🗣️ **Speech Processing**
- **Speech-to-Text (STT) using OpenAI Whisper, DeepSpeech, and Wav2Vec**
- **Text-to-Speech (TTS) with Tacotron, Coqui TTS, and VITS**
- **Voice Activity Detection (VAD) and Speaker Recognition**
### 🎵 **Music AI & Sound Synthesis**
- **AI-Generated Music using Recurrent Neural Networks (RNNs) and Transformers**
- **MIDI Processing and Music Theory for Algorithmic Composition**
- **Synthesizing Sounds using Waveforms and Neural Networks**
### 🤖 **Deep Learning for Audio**
- **Audio Classification with Convolutional Neural Networks (CNNs)**
- **Sound Event Detection (SED) with Transformer-based Models**
- **Generating Sound using GANs and Diffusion Models**
### 📊 **Audio Data Science & Visualization**
- **Feature Extraction: MFCCs, Spectral Centroid, and Chroma Features**
- **Audio Data Augmentation for Training Robust Models**
- **Sound Source Separation using Deep Learning**
---
## 🔥 Example Code
### 🔨 **Generating a Sine Wave using NumPy**
```python
import numpy as np
import matplotlib.pyplot as plt
# Generate a sine wave
fs = 44100 # Sampling rate
t = np.linspace(0, 1, fs)
freq = 440 # Frequency in Hz (A4 note)
wave = np.sin(2 * np.pi * freq * t)
# Plot the wave
plt.plot(t[:1000], wave[:1000]) # Plot first 1000 samples
plt.title("Sine Wave (440 Hz)")
plt.xlabel("Time [s]")
plt.ylabel("Amplitude")
plt.show()
```
### 🔥 **Speech-to-Text with OpenAI Whisper**
```python
import whisper
model = whisper.load_model("base")
result = model.transcribe("audio_file.wav")
print(result["text"])
```
### 🔑 **Audio Classification with PyTorch**
```python
import torchaudio
import torch.nn as nn
# Load audio file
waveform, sample_rate = torchaudio.load("audio_sample.wav")
# Define a simple CNN for audio classification
class AudioClassifier(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv1d(1, 16, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(16, 10)
def forward(self, x):
x = self.conv1(x)
x = x.mean(dim=-1)
x = self.fc1(x)
return x
model = AudioClassifier()
output = model(waveform.unsqueeze(0))
print("Predicted Class:", torch.argmax(output))
```
---
## 🏆 Real-World Applications
✅ **Voice Assistants (Alexa, Siri, Google Assistant)**
✅ **Music Generation & AI Composers**
✅ **Speech Recognition & Transcription Services**
✅ **Noise Reduction & Sound Enhancement**
✅ **Emotion Recognition from Audio**
---
## 🤝 Contributing
Contributions are welcome! 🚀
🔹 **Fork** the repository
🔹 Create a new branch (`git checkout -b feature-name`)
🔹 Commit changes (`git commit -m "Added spectrogram visualization"`)
🔹 Push to your branch (`git push origin feature-name`)
🔹 Open a pull request
---
## 📜 License
This project is licensed under the **MIT License** – feel free to use, modify, and share the code.
---
## 📬 Contact
📧 **Email:** saadsalmanakram1@gmail.com
🌐 **GitHub:** [SaadSalmanAkram](https://github.com/saadsalmanakram)
💼 **LinkedIn:** [Saad Salman Akram](https://www.linkedin.com/in/saadsalmanakram/)
---
🎧 **Unlock the Power of Sound with AI and Programming!** 🎧
---