Audio Signal Processing for Machine Learning Series Overview

Introduction

• New video series focused on audio digital signal processing for machine learning and deep learning.
• Addressing the gap in resources for audio data compared to image data.
• Aim to clarify how to process audio data for deep learning applications.

The Problem

• Many resources available for image processing in deep learning.
• Audio data processing lacks clarity and resources.
• Need for a comprehensive series on audio digital signal processing.

Applications of Audio Signal Processing

• Key areas of application:
  • Audio classification problems.
  • Speech recognition.
  • Speaker verification and diarization.
  • Audio denoising.
  • Music Information Retrieval (MIR):
    • Instrument identification.
    • Music mood and genre classification.

Series Content Overview

• Topics to be covered include:
  • Waves: Digital to analog converters and analog to digital converters.
  • Audio features in time and frequency domains:
    • Spectral centroid.
    • Mel-frequency cepstral coefficients (MFCCs).
  • Important audio transformations:
    • Fourier Transform, Short-Time Fourier Transform (STFT), spectrograms.
    • Comparison with Constant Q Transform, Mel spectrograms, and chromagrams.
  • Audio and music perception for data pre-processing.

Structure of the Series

• Combination of theoretical and practical coding sessions:
  • Theoretical sessions to delve into concepts.
  • Coding sessions to implement theories discussed.
• Materials will be available on GitHub:
  • Code samples, slides, and other resources.

Learning Outcomes

• Understand audio data and its manipulation.
• Familiarity with frequency and time domain audio features.
• Ability to extract features relevant to different audio ML applications.
• Knowledge of the math behind audio transformations.
• Efficient use of the Librosa library for audio feature extraction.
• Ability to interpret spectrograms and what they represent.

Target Audience

• Ideal for:
  • Machine learning and deep learning engineers.
  • Computer science students.
  • Software engineers interested in audio and music.
  • Music technologists or tech-oriented musicians.
• Not suitable for absolute beginners; intermediate skills recommended.

Community Engagement

• Encouragement to join the Sound of Slack community.
• Networking opportunities with like-minded individuals interested in audio processing.

Conclusion

• Anticipation for the journey ahead in the series.
• Invitation to join and participate.