A COSINE DISTANCE BASED NEURAL NETWORK FOR MUSIC ARTIST RECOGNITION USING RAW I VECTOR FEATURES
Recently, i vector features have entered the field of Music Information Retrieval (MIR) exhibiting highly promising performance in important tasks such as music artist recognition or music similarity estimation. The i vector modelling approach relies on a complex processing chain that limits by the use of engineered features such as MFCCs. The goal of the present paper is to make an important step towards a trulyend to endmodelling system inspired by the i vector pipeline, to exploit the power of Deep Neural Networks 1 (DNNs) to learn optimized feature spaces and transformations. Several authors have already tried to combine the power of DNNs with i vector features, where DNNs were used for feature extraction, scoring or classification. In this paper, we try to use neural networks for the important step of i vector post processing and classification for the task of music artist recognition. Specifically, we propose a novel neural network for i vector features with a cosine distance loss function, optimized with stochastic gradient decent (SGD) We first show that current networks do not perform well with unprocessed i vector features, and that post processing methods such as Within Class Covariance Normalization (WCCN) and Linear Discriminant Analysis (LDA) are crucially important to improve the i vector representation. We further demonstrate that these linear projections (WCCN and LDA) can not be learned using general objective functions usually used in neural networks. We examine our network on a 50 class music artist recognition dataset using i vectors extracted from frame level timbre features. Our experiments suggest that using our network with fully unprocessed i vectors, we can achieve the performance of the i vector pipeline which uses i vector post processing methods such as LDA and WCCN.