MFCC
Mel-Frequency Cepstral Coefficients, a feature representation commonly used in speech and audio processing. It extracts spectral envelope features through a logarithmic Mel filter bank and a discrete cosine transform, and is widely used in speech recognition and speaker recognition.
Timeline (last 90 days)
In audio regression testing, because TTS models are non-deterministic, the same text synthesized twice produces different byte streams but sounds consistent, so byte-level comparison cannot be used; instead, acoustic fingerprints (such as MFCC, F0 trajectory, duration) should be extracted and compared with golden audio using cosine similarity or DTW distance.
DC offset refers to the centerline of an audio waveform deviating from zero level, which makes the waveform asymmetric, affects the accuracy of feature extraction such as MFCC, and can even introduce low-frequency hum.
All Facts (2)
In audio regression testing, because TTS models are non-deterministic, the same text synthesized twice produces different byte streams but sounds consistent, so byte-level comparison cannot be used; instead, acoustic fingerprints (such as MFCC, F0 trajectory, duration) should be extracted and compared with golden audio using cosine similarity or DTW distance.
50%UnverifiedDC offset refers to the centerline of an audio waveform deviating from zero level, which makes the waveform asymmetric, affects the accuracy of feature extraction such as MFCC, and can even introduce low-frequency hum.
50%