Audio Classification

Audio classification involves the categorization of audio signals into predefined classes or labels based on their acoustic features. This field is crucial for applications such as speech recognition, music genre classification, environmental sound analysis, and audio event detection.

Methods and Techniques

• Mel-Frequency Cepstral Coefficients (MFCCs) - Extracts coefficients representing the spectral characteristics of audio signals, commonly used as features for speech and general audio classification.

• Short-Time Fourier Transform (STFT) - Decomposes audio signals into their frequency components over short time intervals, providing time-frequency representations for classification.

• Audio as Image Data - Applies deep learning architectures (e.g.: CNNs) designed for processing grid-structured data, such as spectrograms, as images to automatically learn hierarchical features for audio classification.

• Sequential Models (RNN, LSTM, GRU, Transformer, etc.) - Processes sequential information in audio signals, capturing temporal dependencies and context for improved classification accuracy.

• Hidden Markov Models (HMMs) - Models the underlying sequence of audio features probabilistically, particularly useful for tasks involving sequential patterns like speech recognition.

• Data Augmentation Techniques - Generates variations of audio data, such as pitch shifting or time stretching, to augment the training dataset and enhance model generalization.

• Energy-Based Models - Models classify audio based on the energy distribution or patterns within the signal, suitable for tasks like acoustic event detection.