Audio Processing та обробка аудіо

🎓 Feature Extraction

MFCC

Concept: Mel-Frequency Cepstral Coefficients. Mimics human perception.

Advantages: Effective for speech, compact representation.

Applications: Speech recognition, speaker identification.

Spectrogram

Concept: Time-frequency representation. STFT (Short-Time Fourier Transform).

Advantages: Visual, well-suited for CNNs.

Applications: Audio classification, music analysis.

Chroma Features

Concept: Musical notes (12 semitones).

Applications: Music analysis, chord detection.

🔧 Audio Processing Tasks

Speech Recognition

Task: Converting speech to text (ASR).

Models: Wav2Vec, Whisper, DeepSpeech, Transformer-based.

Applications: Voice assistants, transcription.

Audio Classification

Task: Classifying audio (genres, sounds, languages).

Models: CNNs on spectrograms, Transformer-based.

Applications: Music genre, sound event detection.

Speaker Recognition

Task: Recognizing the speaker (identification, verification).

Models: Deep learning for embeddings.

Applications: Security, authentication.

📚 Practical Examples

Example 1: Speech Recognition with Whisper

Preparation: Prepare audio files.

Whisper: Utilize the pre-trained Whisper model.

Transcription: Obtain text from speech.

Evaluation: Verify WER on a test set.

Example 2: Audio Classification with CNN

Features: Create spectrograms from audio.

CNN: Train a CNN on spectrograms.

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Audio Processing: processing of audio signals

Try it live

Everything above runs in your browser — open Audio Spectrogram Processing Grid and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.