Frame Level Audio Similarity - a Codebook Approach

Modeling audio signals by the long-term statistical distribution of their local spectral features often denoted as bag of frames approach (BOF) is a popular and powerful method to describe audio content. While modeling the distribution of local spectral features by semi-parametric distributions (e.g. Gaussian Mixture Models) has been studied intensively, we investigate a non-parametric variant based on vector quantization (VQ) in this paper. The essential advantage of the proposed VQ approach over stateof-the-art similarity measures is that the proposed audio similarity metric forms a normed vector space. This allows for more powerful search strategies, e.g. KD-Trees or Local Sensitive Hashing (LSH), making content-based audio similarity available for even larger music archives. Standard VQ approaches are known to be computationally very expensive; to counter this problem, we propose a multi-level clustering architecture. Additionally, we show that the multi-level vector quantization approach (ML-VQ), in contrast to standard VQ approaches, is comparable to state-ofthe-art frame-level similarity measures in terms of quality. Another important finding w.r.t. the ML-VQ approach is that, in contrast to GMM models of songs, our approach does not seem to suffer from the recently discovered hub problem.