What is Mel Spectrogram
Mel Spectrogram - Frequency-time representation aligned with human hearing
A visual representation of audio that maps frequency content over time using the mel scale, which approximates human auditory perception. Mel spectrograms are the standard input representation for most neural audio processing models in multimodal AI systems.
How It Works
A mel spectrogram is computed by first applying a Short-Time Fourier Transform (STFT) to convert audio from the time domain to the frequency domain, then mapping the linear frequency axis to the mel scale using triangular filter banks. The mel scale compresses higher frequencies logarithmically to match how humans perceive pitch. The result is a 2D image-like representation with time on one axis and mel-frequency bands on the other.
Technical Details
Standard parameters include 128 mel bands, a window size of 25ms, a hop length of 10ms, and sample rate of 16-22kHz. The output is often converted to log-mel scale (decibels) and normalized. MFCCs (Mel-Frequency Cepstral Coefficients) apply a DCT to the mel spectrogram for a more compact representation. Modern deep learning models prefer raw mel spectrograms over MFCCs as input, as the network learns optimal features from the richer representation.
Best Practices
- Use 128 mel bands as a reasonable default for most audio classification and embedding tasks
- Apply log scaling and normalization before feeding mel spectrograms to neural networks
- Choose hop length based on your temporal resolution requirements (10ms for detail, 20ms for efficiency)
- Use consistent preprocessing parameters between training and inference
Common Pitfalls
- Using mismatched spectrogram parameters between training and deployment models
- Not applying log scaling, which causes the network to focus only on high-energy components
- Setting too few mel bands for tasks requiring fine frequency discrimination
- Forgetting to handle the DC component and Nyquist frequency properly
Advanced Tips
- Apply SpecAugment (time and frequency masking) during training for robust audio models
- Use librosa or torchaudio for efficient, GPU-accelerated spectrogram computation
- Treat mel spectrograms as images and leverage pretrained vision models for audio tasks
- Compute mel spectrograms at multiple resolutions for multi-scale audio analysis