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The extraction of local tempo and beat information from audio recordings constitutes a challenging task, particularly for music that reveals significant tempo variations. Furthermore, the existence of various pulse levels such as measure, tactus, and tatum often makes the determination of absolute tempo problematic. In this paper, we present a robust mid-level representation that encodes local tempo information. Similar to the well-known concept of cyclic chroma features, where pitches differing by octaves are identified, we introduce the concept of cyclic tempograms, where tempi differing by a power of two are identified. Furthermore, we describe how to derive cyclic tempograms from music signals using two different methods for periodicity analysis and finally sketch some applications to tempo-based audio segmentation.

Given a large audio database of music recordings, the goal of classical audio identification is to identify a particular audio recording by means of a short audio fragment. Even though recent identification algorithms show a significant degree of robustness towards noise, MP3 compression artifacts, and uniform temporal distortions, the notion of similarity is rather close to the identity. In this paper, we address a higher level retrieval problem, which we refer to as audio matching: given a short query audio clip, the goal is to automatically retrieve all excerpts from all recordings within the database that musically correspond to the query. In our matching scenario, opposed to classical audio identification, we allow semantically motivated variations as they typically occur in different interpretations of a piece of music. To this end, this paper presents an efficient and robust audio matching procedure that works even in the presence of significant variations, such as nonlinear temporal, dynamical, and spectral deviations, where existing algorithms for audio identification would fail. Furthermore, the combination of various deformation- and fault-tolerance mechanisms allows us to employ standard indexing techniques to obtain an efficient, index-based matching procedure, thus providing an important step towards semantically searching large-scale real-world music collections

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