Audio Processing Series with Python | How to Compare MFCC features from Two Audio Samples | Part 7

Audio Processing Series with Python: How to Compare MFCC Features from Two Audio Samples | Part 7
In this seventh installment of our Audio Processing Series, we'll build upon our understanding of MFCCs and explore how to compare them between two audio samples. This technique is fundamental for tasks like audio similarity detection, plagiarism detection, and speaker identification.
Key topics covered in this video:
MFCC distance metrics: Learn about various distance metrics (e.g., Euclidean distance, cosine similarity) that can be used to quantify the difference between MFCC feature vectors.
Implementation using Python libraries: Discover how to use Python libraries like Librosa and scikit-learn to calculate distance metrics between MFCCs.
Real-world applications: Explore practical use cases of MFCC comparison, including audio fingerprinting, music recommendation systems, and forensic analysis.
By the end of this video, you'll be able to:
Calculate distance metrics between MFCC feature vectors.
Compare the similarity or dissimilarity between two audio samples.
Apply MFCC comparison techniques to various audio processing tasks.
Join us in this exciting journey of audio processing and discover the power of MFCC comparison!
#AudioProcessing #Python #MFCC #FeatureComparison #DataScience