Fast Cover Song Retrieval in Advanced Audio Coding Domain Based on Deep Learning Technique

Abstract

We proposed a novel deep neural network based cover song retrieval method in AAC domain to reduce the computation complexity. The modified discrete cosine transform coefficients from the AAC were extracted and mapped into the 12-dimensional chroma features. Chroma features were further segmented to preserve the melody of music. Each segment of chroma features was trained and learned to reduce its dimension by using an autoencoder, used for deep learning of artificial neural networks.Experiments were conducted on cover80 database, which was provided by Ellis [3]. The results showed that the mean reciprocal rank increased to 0.46. The performance of the proposed method was compared with other systems, including LabROSA’06 (Tempos 120 and 60), and the method of principal component analysis (PCA) from the cover80. Fig. 1 plots the performances of MRR and similarity matching time. The proposed method improved the MRR performance and reduced approximately 80% of the matching time compared with LabROSA’06 (Tempo 120) [1].