Streams as Seams: Carving trajectories out of the time-frequency matrix

A time-frequency representation of sound is commonly obtained through the Short-Time Fourier Transform. Identifying and extracting the prominent frequency components of the spectrogram is important for sinusoidal modeling and sound processing. Borrowing a known image processing technique, known as seam carving, we propose an algorithm to track and extract the sinusoidal components from the sound spectrogram. Experiments show how this technique is well suited for sound whose prominent frequency components vary both in amplitude and in frequency. Moreover, seam carving naturally produces some auditory continuity effects. We compare this algorithm with two other sine extraction techniques, based on peak detection on spectrogram frames. The seam carving skips this step and turns out to be applicable to a variety of sounds,although being more computationally expensive.