Modeling audio signals via the long-term statistical distribution of their local spectral features – often denoted as bag of frames (BOF) approach – 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 audio similarity measures is that the similarity metric proposed here 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.

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We describe an audio analysis method to create a high-level audio annotation, expressed as a single scalar. Typically, low values of this feature indicate songs with dominant harmonic elements while high values indicate the dominance of mainly percussive or drum-like sounds. The proposed feature is based on a simple idea: Filters known from image processing are used to extract attack and harmonic parts of the spectrum, and the ratio of their overall strengths is used as the final feature. The feature takes values in the unit range, and is highly independent of the overall loudness. We present a number of experiments that indicate the potential of the proposed feature. A suggested application scenario is to write the feature value into the comments field of an audio file, so that it can be used by a number of existing audio players in conjunction with metadata-based search mechanisms, most notably genre.

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