This paper presents methods for the automatic detection of music within audio streams, in the fore- or background. The problem occurs in the context of a real-world application, namely, the analysis of TV productions w.r.t. the use of music. In contrast to plain speech/music discrimination, the problem of detecting music in TV productions is extremely difficult, since music is often used to accentuate scenes while concurrently speech and any kind of noise signals might be present. We present results of extensive experiments with a set of standard machine learning algorithms and standard features, investigate the difference between frame-level and clip-level features, and demonstrate the importance of the application of smoothing functions as a post-processing step. Finally, we propose a new feature, called Continuous Frequency Activation (CFA), especially designed for music detection, and show experimentally that this feature is more precise than the other approaches in identifying segments with music in audio streams.

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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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In this paper we present a new method to compute frame based audio similarities, based on nearest neighbour density estimation. We do not recommend it is as a practical method for large collections because of the high runtime. Rather, we use this new method for a detailed analysis to get a deeper insight on how a bag of frames approach (BOF) determines similarities among songs, and in particular, to identify those audio frames that make two songs similar from a machine’s point of view. Our analysis reveals that audio frames of very low energy, which are of course not the most salient with respect to human perception, have a surprisingly big influence on current similarity measures. Based on this observation we propose to remove these low-energy frames before computing song models and show, via classification experiments, that the proposed frame selection strategy improves the audio similarity measure.

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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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In this paper we present a novel approach to computing music similarity based on block-level features. We first introduce three novel block-level features — the Variance Delta Spectral Pattern (VDSP), the Correlation Pattern (CP) and the Spectral Contrast Pattern (SCP). Then we describe how to combine the extracted features into a single similarity function. A comprehensive evaluation based on genre classification experiments shows that the combined block-level similarity measure (BLS) is comparable, in terms of quality, to the best current method from the literature. But BLS has the important advantage of being based on a vector space representation, which directly facilitates a number of useful operations, such as PCA analysis, k-means clustering, visualization etc. We also show that there is still potential for further improve of music similarity measures by combining BLS with another stateof-the-art algorithm; the combined algorithm then outperforms all other algorithms in our evaluation. Additionally, we discuss the problem of album and artist effects in the context of similaritybased recommendation and show that one can detect the presence of such effects in a given dataset by analyzing the nearest neighbor classification results.

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