Raw Features: Waveform amplitudes, signal energy.

Engineered Features: Mel-frequency cepstral coefficients (MFCCs), spectrogram data, pitch.

Context: In speech recognition, MFCCs are features extracted to characterize the audio signal.

Raw Features: Pixel intensity values, RGB color values.

Engineered Features: Haar features, Gabor wavelets, Histogram of gradients (HOG), edge counts, convolutional feature maps.

Context: In object detection, pixel patterns or edge-based features help detect objects in the image.

Raw Features: occurence of specific character sequences, word or token counts, sequence length

Engineered Features: Word "embeddings" (e.g., Word2Vec, BERT embeddings),

Context: In sentiment analysis, embeddings provide dense, meaningful representations of text features.