Features for melody spotting using hidden Markov models

The amount of digitized music stored on personal computers and available on the Internet is growing at a rapid rate. To address the access problem that this creates, we explore adapting HMM-based wordspotting techniques from speech recognition to create a system for melody-based retrieval of songs from a database of digitized music stored in a musically-unstructured format. In this paper, we present the construction of this melody spotter and evaluate its performance when trained under different feature vectors including a musical scale-based subset of the FFT and two Mel-scale based features. The results show the success of this system under the scale-based features when presented with both perfect melody queries and queries perturbed by minor errors.