Computer Vision for Music Identification: Video Demonstration

Yan Ke, Derek Hoiem, Rahul Sukthankar

Abstract: This paper describes a demonstration video for our music identification system. The goal of music identification is to reliably recognize a song from a small sample of noisy audio. This problem is challenging because the recording is often corrupted by noise and because the audio sample will only match a small portion of the target song. Additionally, a practical music identification system should scale (in both accuracy and speed) to databases containing hundreds of thousands of songs. Recently, the music identification problem has attracted considerable attention. However, the task remains unsolved, particularly for noisy real-world queries. We cast music identification into an equivalent sub-image retrieval framework: identify the portion of a spectrogram image from the database that best matches a given query snippet. Our approach treats the spectrogram of each music clip as a 2D image and transforms music identification into a corrupted sub-image retrieval problem.